We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, (...) allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions. (shrink)

The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based (...) on field work, which should go beyond the in-vitro analysis of UX to examine in-vivo problems emerging in the field. Our methodology is also comparative, as it chooses to steer away from the almost exclusive focus on ML to compare its challenges with those faced by more vintage algorithms. Finally, it is also philosophical, as we defend the relevance of the philosophical literature to define the epistemic desiderata of a good explanation. This study was conducted in collaboration with Etalab, a Task Force of the French Prime Minister in charge of Open Data & Open Government Policies, dealing in particular with the enforcement of the right to an explanation. In order to illustrate and refine our methodology before going up to scale, we conduct a preliminary work of case studies on the main different types of algorithms used by the French administration: computation, matching algorithms and ML. We study the merits and drawbacks of a recent approach to explanation, which we baptize input-output black box reasoning or BBR for short. We begin by presenting a conceptual framework including the distinctions necessary to a study of pedagogical explainability. We proceed to algorithmic case studies, and draw model-specific and model-agnostic lessons and conjectures. (shrink)

Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus (...) on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory (...) strategies will lack a well-defined goal. Aside from providing a clearer objective for XAI, focusing on understanding also allows us to relax the factivity condition on explanation, which is impossible to fulfill in many machine learning models, and to focus instead on the pragmatic conditions that determine the best fit between a model and the methods and devices deployed to understand it. After an examination of the different types of understanding discussed in the philosophical and psychological literature, I conclude that interpretative or approximation models not only provide the best way to achieve the objectual understanding of a machine learning model, but are also a necessary condition to achieve post hoc interpretability. This conclusion is partly based on the shortcomings of the purely functionalist approach to post hoc interpretability that seems to be predominant in most recent literature. (shrink)

The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and (...) social explainability can require an incoherent theory of what social categories are. Our findings suggest that most often the social categories may not admit counterfactual manipulation, and hence may not appropriately satisfy the demands for evaluating the truth or falsity of counterfactuals. This is important because the wide- spread use of counterfactuals in machine learning can lead to misleading results when applied in high-stakes domains. Accordingly, we argue that even though counterfactuals play an essential part in some causal inferences, their use for questions of algorithmic fairness and social explanations can create more problems than they resolve. Our positive result is a set of tenets about using counterfactuals for fairness and explanations in machine learning. (shrink)

In a recent article, Erasmus, Brunet, and Fisher (2021) argue that Artificial Neural Networks (ANNs) are explainable. They survey four influential accounts of explanation: the Deductive-Nomological model, the Inductive-Statistical model, the Causal-Mechanical model, and the New-Mechanist model. They argue that, on each of these accounts, the features that make something an explanation is invariant with regard to the complexity of the explanans and the explanandum. Therefore, they conclude, the complexity of ANNs (and other Machine Learning models) does (...) not make them less explainable. In this reply, it is argued that Erasmus et al. left out one influential account of explanation from their discussion: the Unificationist model. It is argued that, on the Unificationist model, the features that makes something an explanation is sensitive to complexity. Therefore, on the Unificationist model, ANNs (and other Machine Learning models) are not explainable. It is emphasized that Erasmus et al.’s general strategy is correct. The literature on explainable Artificial Intelligence can benefit by drawing from philosophical accounts of explanation. However, philosophical accounts of explanation do not settle the problem of whether ANNs are explainable because they do not unanimously declare that explanation is invariant with regard to complexity. (shrink)

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers (...) to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software. (shrink)

Since its emergence in the 1960s, Artifcial Intelligence (AI) has grown to conquer many technology products and their felds of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from diferent domains, together with numerous (...) tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models. This variety of tasks has led to inconsistencies in the terminology with, for instance, terms such as interpretable, explainable and transparent being often used interchangeably in methodology papers. These words, however, convey diferent meanings and are “weighted" diferently across domains, for example in the technical and social sciences. In this paper, we propose an overarching terminology of interpretability of AI systems that can be referred to by the technical developers as much as by the social sciences community to pursue clarity and efciency in the defnition of regulations for ethical and reliable AI development. We show how our taxonomy and defnition of interpretable AI difer from the ones in previous research and how they apply with high versatility to several domains and use cases, proposing a—highly needed—standard for the communication among interdisciplinary areas of AI. (shrink)

The use of machine learning to medical image classification has seen significant development and implementation in the last several years. Computers can learn to identify patterns, make predictions, and use data to inform their judgements; this capability is known as machine learning, a branch of Artificial intelligence (AI). Classifying images according to their contents allows us to do things like identify the type of sickness, organ, or tissue depicted. Medical picture classification and interpretation using machine learning algorithms (...) has greatly improved the speed and accuracy of diagnosis and treatment. Machine learning algorithms can aid radiologists and doctors in diagnosing patients using medical pictures from X-rays, positron emission tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), and other scans. (shrink)

Background: Microbiome biomarker discovery for patient diagnosis, prognosis, and risk evaluation is attracting broad interest. Selected groups of microbial features provide signatures that characterize host disease states such as cancer or cardio-metabolic diseases. Yet, the current predictive models stemming from machine learning still behave as black boxes and seldom generalize well. Their interpretation is challenging for physicians and biologists, which makes them difficult to trust and use routinely in the physician-patient decision-making process. Novel methods that provide interpretability and biological (...) insight are needed. Here, we introduce "predomics", an original machine learning approach inspired by microbial ecosystem interactions that is tailored for metagenomics data. It discovers accurate predictive signatures and provides unprecedented interpretability. The decision provided by the predictive model is based on a simple, yet powerful score computed by adding, subtracting, or dividing cumulative abundance of microbiome measurements. -/- Results: Tested on >100 datasets, we demonstrate that predomics models are simple and highly interpretable. Even with such simplicity, they are at least as accurate as state-of-the-art methods. The family of best models, discovered during the learning process, offers the ability to distil biological information and to decipher the predictability signatures of the studied condition. In a proof-of-concept experiment, we successfully predicted body corpulence and metabolic improvement after bariatric surgery using pre-surgery microbiome data. -/- Conclusions: Predomics is a new algorithm that helps in providing reliable and trustworthy diagnostic decisions in the microbiome field. Predomics is in accord with societal and legal requirements that plead for an explainable artificial intelligence approach in the medical field. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence (AI) technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle (...) the possibility to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability (originally developed in the philosophy of science) to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (shrink)

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors (...) with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks. (shrink)

Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to (...) receive a systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...) desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three diferent aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. (shrink)

Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathématique et causale d’un phénomène (...) physique. La différence majeure est qu’une méthode d’AM ne prétend pas représenter une causalité entre les paramètres d’entrées, qui peuvent être de plus de haute dimensionnalité, et ceux de sortie. Nous montrons dans cet article l’intérêt de mettre en œuvre les distinctions épistémologiques entre les différentes fonctions épistémiques d’un modèle, d’une part, et entre la fonction épistémique et l’usage d’un modèle, d’autre part. Enfin, la dernière partie de cet article présente nos travaux en cours sur l’évaluation d’une explication, qui peut être plus persuasive qu’informative, ce qui peut ainsi causer des problèmes d’ordre éthique. (shrink)

This paper provides an analysis of the way in which two foundational principles of medical ethics–the trusted doctor and patient autonomy–can be undermined by the use of machine learning (ML) algorithms and addresses its legal significance. This paper can be a guide to both health care providers and other stakeholders about how to anticipate and in some cases mitigate ethical conflicts caused by the use of ML in healthcare. It can also be read as a road map as to (...) what needs to be done to achieve an acceptable level of explainability in an ML algorithm when it is used in a healthcare context. (shrink)

The success of deep learning in natural language processing raises intriguing questions about the nature of linguistic meaning and ways in which it can be processed by natural and artificial systems. One such question has to do with subword segmentation algorithms widely employed in language modeling, machine translation, and other tasks since 2016. These algorithms often cut words into semantically opaque pieces, such as ‘period’, ‘on’, ‘t’, and ‘ist’ in ‘period|on|t|ist’. The system then represents the resulting segments in a (...) dense vector space, which is expected to model grammatical relations among them. This representation may in turn be used to map ‘period|on|t|ist’ (English) to ‘par|od|ont|iste’ (French). Thus, instead of being modeled at the lexical level, translation is reformulated more generally as the task of learning the best bilingual mapping between the sequences of subword segments of two languages; and sometimes even between pure character sequences: ‘p|e|r|i|o|d|o|n|t|i|s|t’ → ‘p|a|r|o|d|o|n|t|i|s|t|e’. Such subword segmentations and alignments are at work in highly efficient end-to-end machine translation systems, despite their allegedly opaque nature. The computational value of such processes is unquestionable. But do they have any linguistic or philosophical plausibility? I attempt to cast light on this question by reviewing the relevant details of the subword segmentation algorithms and by relating them to important philosophical and linguistic debates, in the spirit of making artificial intelligence more transparent and explainable. (shrink)

Some physicians, in their care of patients at risk of misusing opioids, use machine learning (ML)-based prediction drug monitoring programmes (PDMPs) to guide their decision making in the prescription of opioids. This can cause a conflict: a PDMP Score can indicate a patient is at a high risk of opioid abuse while a patient expressly reports oppositely. The prescriber is then left to balance the credibility and trust of the patient with the PDMP Score. Pozzi1 argues that a prescriber (...) who downgrades the credibility of a patient’s testimony based on a low PDMP Score is epistemically and morally unjustified and contributes to a form of testimonial injustice. This results in patients being silenced, excluded from decision-making processes and subjected to structural injustices. Additionally, the use of ML systems in medical practices raises concerns about perpetuating existing inequalities, overestimating their capabilities and displacing human authority. However, almost the very same critiques apply to human-based systems. Formalisation, ML systems included, should instead be viewed positively,2 and precisely as a powerful means to begin eroding these and other problems in ethically sensitive domains. In this case, the epistemic virtues of formalisation include promoting transparency, consistency and replicability in decision making. Rigorous ML systems can also help ensure that models …. (shrink)

In contemporary healthcare, medical image categorization is essential for illness prediction, diagnosis, and therapy planning. The emergence of digital imaging technology has led to a significant increase in research into the use of machine learning (ML) techniques for the categorization of images in medical data. We provide a thorough summary of recent developments in this area in this review, using knowledge from the most recent research and cutting-edge methods.We begin by discussing the unique challenges and opportunities associated with medical (...) image classification, including the complexity of anatomical structures, variability in imaging modalities, and the need for interpretability and reliability in clinical settings. Subsequently, we survey a wide range of ML algorithms and techniques employed for medical image classification, including traditional methods such as support vector machines and k-nearest neighbors, as well as deep learning approaches like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Furthermore, we examine key considerations in dataset preparation, feature extraction, and model evaluation specific to medical image classification tasks. We highlight the importance of large, annotated datasets, transfer learning, and data augmentation techniques in enhancing model performance and generalization. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report (...) on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about (...) justice and fairness in healthcare. In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Why would we want to develop artificial human-like arithmetical intelligence, when computers already outperform humans in arithmetical calculations? Aside from arithmetic consisting of much more than mere calculations, one suggested reason is that AI research can help us explain the development of human arithmetical cognition. Here I argue that this question needs to be studied already in the context of basic, non-symbolic, numerical cognition. Analyzing recent machine learning research on artificial neural networks, I show how AI studies could potentially (...) shed light on the development of human numerical abilities, from the proto-arithmetical abilities of subitizing and estimating to counting procedures. Although the current results are far from conclusive and much more work is needed, I argue that AI research should be included in the interdisciplinary toolbox when we try to explain the development and character of numerical cognition and arithmetical intelligence. This makes it relevant also for the epistemology of mathematics. (shrink)

This study aimed to identify real-life experts working for a port authority and lay people (students) who played The Sustainable Port, a serious game aiming to simulate the dynamics occurring in a port area. To achieve this goal, we analyzed eye gaze data collected noninvasively using low-grade webcams from 28 participants working for the port authority of the Port of Rotterdam and 66 students. Such data were used for a classification task implemented using a MiniRocket classifier, an algorithm used for (...) time-series classification. The classifier reached an F1 score of 0.75 (SD = 0.07), a PR AUC of 0.73 (SD = 0.14), and an ROC AUC of 0.75 (SD = 0.15) providing evidence that it is possible to identify real-life experts about maritime port management using data that can be obtained from a webcam. We speculate that the gaze direction used to train the MiniRocket may contain relevant information about the cognitive processes and decisions occurring throughout the gameplay. We suggest that the methods here presented not only can be used to detect experts playing simulations, such as serious games, but also to identify experts tackling screen-presented tasks. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can (...) be handled more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

This paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this prevalent interpretation by (...) drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values. I show how neglecting these considerations can distort our normative deliberations, and result in costly and misaligned interventions and justifications. Taken together, these considerations form a sociotechnical framework that could guide those involved in AI governance to assess how, in many cases, we can and should have higher aspirations than the prevalent interpretation of the trade-offs would suggest. I end by drawing out the normative opportunities and challenges that emerge out of these considerations, and highlighting the imperative of interdisciplinary collaboration in fostering responsible AI. (shrink)

There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits (...) that are difficult to integrate into an algorithm’s broader ethical assessment. In this paper, we derive lessons from ethical philosophy and welfare economics as they relate to the contextual factors relevant for fairness. In particular we highlight the debate around acceptability of particular inequalities and the inextricable links between fairness, welfare and autonomy. We propose Key Ethics Indicators (KEIs) as a way towards providing a more holistic understanding of whether or not an algorithm is aligned to the decision-maker’s ethical values. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the (...) experts who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

We argue that the trend toward providing users with feasible and actionable explanations of AI decisions—known as recourse explanations—comes with ethical downsides. Specifically, we argue that recourse explanations face several conceptual pitfalls and can lead to problematic explanation hacking, which undermines their ethical status. As an alternative, we advocate that explanations of AI decisions should aim at understanding.

We argue that, to be trustworthy, Computa- tional Intelligence (CI) has to do what it is entrusted to do for permissible reasons and to be able to give rationalizing explanations of its behavior which are accurate and gras- pable. We support this claim by drawing par- allels with trustworthy human persons, and we show what difference this makes in a hypo- thetical CI hiring system. Finally, we point out two challenges for trustworthy CI and sketch a mechanism which could be (...) used to gener- ate sufficiently accurate as well as graspable rationalizing explanations for CI behavior. (shrink)

When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them (...) from such activity? A close look at the methodology of interpretive social science reveals several abilities necessary to make a social scientific discovery, and one capacity necessary to possess any of them is imagination. For machines to make discoveries in social science, therefore, they must possess imagination algorithms. (shrink)

Holm (2022) argues that a class of algorithmic fairness measures, that he refers to as the ‘performance parity criteria’, can be understood as applications of John Broome’s Fairness Principle. We argue that the performance parity criteria cannot be read this way. This is because in the relevant context, the Fairness Principle requires the equalization of actual individuals’ individual-level chances of obtaining some good (such as an accurate prediction from a predictive system), but the performance parity criteria do not guarantee (...) any such thing: the measures merely ensure that certain population-level ratios hold. (shrink)

After providing a brief overview of the economic theory of judicial decisions this paper presents an argument for why not only the economic theory of judicial decisions, but also the rational approach in general, most often fails in explaining decision-making. Work done within the research program of New Institutionalism is presented as a possible alternative. Within this research program judicial activity is conceptualized as the activity of "interpreting the rules of the game", i.e. the institutions that frame the economic and (...) political interaction in a society. Such a conceptualization of judicial interpretation and judicial decision-making would have to depart from rational choice modes of reasoning, and should instead focus on how human beings actually reason, learn and choose drawing from research in cognitive science. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...) explain why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

Kate Crawford presents AI as “both reflecting and producing social relations and understandings of the world”; or again, as “a form of exercising power, and a way of seeing… as a manifestation of highly organized capital backed by vast systems of extraction and logistics, with supply chains that wrap around the entire planet”. I interpret these material insights through a Marxist understanding of ideology, with reference to Marx/Engels, Guy Debord and Louis Althusser. In the German Ideology, Marx and Engels present (...) ideology as the alienated and alienating discourse of exchange, dissimulating the material forces of production and its science. Guy Debord presents the spectacle as the further representation of capital. The spectacle is a total Weltanschauung, pure appearance, where “whatever appears is good and where what is good, appears.” Above all, the spectacle is ideology, representing “the impoverishment, enslavement and negation of real life”. Louis Althusser presents ideology as essential for the reproduction of labor-power and capital. His notion of “interpellation” explains the subjective uptake of ideology, how we respond, “yes!” to its call. The algorithms of AI are enactments of surplus value generating capital as data. The AI data-world is the spectacle, “the autonomous movement of non-life” (Debord) where the proletariat/consumer is the producer of their own ideological subjection. Is resistance possible? What form might it take? And if AI is understood as ideology, i.e. as a language that obscures the true conditions of real life, then what are those true conditions? What is “real life”? (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...) consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

The traditional approach to explanation in cognitive neuroscience is realist about psychological constructs, and treats them as explanatory. On the “standard framework,” cognitive neuroscientists explain behavior as the result of the instantiation of psychological functions in brain activity. This strategy is questioned by results suggesting the distribution of function in the brain, the multifunctionality of individual parts of the brain, and the overlap in neural realization of purportedly distinct psychological constructs. One response to this in the field has been (...) to employ the tools of databasing and machine learning to attempt to find and quantify specific correlations between psychological kinds such as ‘memory’ or ‘attention’ (or sub-kinds thereof) and patterns of activity in the brain. I assess the status and prospects of these projects. I argue that current proponents of the project are vague about their aims, vis-à-vis the standard framework, sometimes suggesting substantiation of the framework, sometimes suggesting retaining the framework but revising the ontology of mental constructs, and sometimes suggesting abandonment of the framework. I argue that extant results from within the projects fail to substantiate the standard framework, and propose an alternative. On my view, psychological constructs should not be viewed as explanantia, but instead as heuristic concepts that help us uncover ways that behaviors can vary and the ways that the brain implements those distinctions. I then discuss the normative upshot of these views for databasing and brain mapping projects. (shrink)

Artificial intelligence (AI) systems have demonstrated impressive performance across a variety of clinical tasks. However, notoriously, sometimes these systems are 'black boxes'. The initial response in the literature was a demand for 'explainable AI'. However, recently, several authors have suggested that making AI more explainable or 'interpretable' is likely to be at the cost of the accuracy of these systems and that prioritising interpretability in medical AI may constitute a 'lethal prejudice'. In this paper, we defend the value of (...) interpretability in the context of the use of AI in medicine. Clinicians may prefer interpretable systems over more accurate black boxes, which in turn is sufficient to give designers of AI reason to prefer more interpretable systems in order to ensure that AI is adopted and its benefits realised. Moreover, clinicians may be justified in this preference. Achieving the downstream benefits from AI is critically dependent on how the outputs of these systems are interpreted by physicians and patients. A preference for the use of highly accurate black box AI systems, over less accurate but more interpretable systems , may itself constitute a form of lethal prejudice that may diminish the benefits of AI to - and perhaps even harm - patients. (shrink)

As the demand for explainable deep learning grows in the evaluation of language technologies, the value of a principled grounding for those explanations grows as well. Here we study the state-of-the-art in explanation for neural models for natural-language processing (NLP) tasks from the viewpoint of philosophy of science. We focus on recent evaluation work that finds brittleness in explanations obtained through attention mechanisms.We harness philosophical accounts of explanation to suggest broader conclusions from these studies. From this analysis, we (...) assert the impossibility of causal explanations from attention layers over text data. We then introduce NLP researchers to contemporary philosophy of science theories that allow robust yet non-causal reasoning in explanation, giving computer scientists a vocabulary for future research. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...) with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only (...) demonstrate how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

In this chapter we will deal with “mechanizing” induction, i.e. with ways in which theoretical computer science approaches inductive generalization. In the field of Machine Learning, algorithms for induction are developed. Depending on the form of the available data, the nature of these algorithms may be very different. Some of them combine geometric and statistical ideas, while others use classical reasoning based on logical formalism. However, we are not so much interested in the algorithms themselves, but more on the (...) philosophical and theoretical foundations they share. Thus in the first of two parts, we will examine different approaches and inductive assumptions in two particular learning settings. While many machine learning algorithms work well on a lot of tasks, the interpretation of the learned hypothesis is often difficult. Thus, while e.g. an algorithm surprisingly is able to determine the gender of the author of a given text with about 80 percent accuracy [Argamon and Shimoni, 2003], for a human it takes some extra effort to understand on the basis of which criteria the algorithm is able to do so. With that respect the advantage of approaches using logic are obvious: If the output hypothesis is a formula of predicate logic, it is easy to interpret. However, if decision trees or algorithms from the area of inductive logic programming are based purely on classical logic, they suffer from the fact that most universal statements do not hold for exceptional cases, and classical logic does not offer any convenient way of representing statements which are meant to hold in the “normal case”. Thus, in the second part we will focus on approaches for Nonmonotonic Reasoning that try to handle this problem. Both Machine Learning and Nonmonotonic Reasoning have been anticipated partially by work in philosophy of science and philosophical logic. At the same time, recent developments in theoretical computer science are expected to trig- ger further progress in philosophical theories of inference, confirmation, theory revision, learning, and the semantic and pragmatics of conditionals. We hope this survey will contribute to this kind of progress by building bridges between computational, logical, and philosophical accounts of induction. (shrink)

The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it (...) helps to maximize patients’ benefits and optimizes limited resources. However, we claim that the opaqueness of the algorithmic black box and its absence of explainability threatens core commitments of procedural fairness such as accountability, avoidance of bias, and transparency. To illustrate this, we discuss liver transplantation as a case of critical medical resources in which the lack of explainability in AI-based allocation algorithms is procedurally unfair. Finally, we provide a number of ethical recommendations for when considering the use of unexplainable algorithms in the distribution of health-related resources. (shrink)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that (...) conceived the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathéma- tique et causale d’un (...) phénomène physique. La différence majeure est qu’une méthode d’AM ne prétend pas représenter une causalité entre les paramètres d’entrées et ceux de sortie. Après avoir proposé une clarification et une adaptation des notions d’inter- prétabilité et d’explicabilité telles qu’on les rencontre dans la littérature déjà abondante sur le sujet, nous montrons dans cet article l’intérêt de mettre en œuvre les distinctions épistémologiques entre les différentes fonctions épistémiques d’un modèle, d’une part, et entre la fonction épistémique et l’usage d’un modèle, d’autre part. Enfin, la dernière partie de cet article présente nos travaux en cours sur l’évaluation d’une explication, qui peut être plus persuasive qu’informative, ce qui peut ainsi causer des problèmes d’ordre éthique. (shrink)

June 2022 A Revolutionary New Metaphysics, Based on Consciousness, and a Call to All Philosophers We are in a unique moment of our history unlike any previous moment ever. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. As I write, the planet is in deep trouble, heat, fires, great storms, and record flooding, (...) weather patterns drastic everywhere. Capitalism has been so successful in supplying us with an endless array of consumer items, that we have never noticed there is a cost. But there is a cost, the cost is the very life of the Earth herself, and now the day of reckoning is here. Days of reckoning are most often days of rath. But I am hoping that this is a day of major Awakening over the entire planet. We have caused a crisis so great for the Earth we can hardly get our minds around it. But I am proposing that deeply embedded in the crisis are ideas, about who and what the Earth is, Ourselves, and what we are here for. I am proposing that this is a call to all philosophy and philosophers to rethink ourselves from the ground up. The human race has never gotten the Earth right and now we must, for the Earth is the true foundation for civilization. We are in deep trouble with the Earth, and it owes to the fact that the great male thinkers never got the Earth right, they could never make the connection between the life of mind and the life of the Earth. I am proposing that deeply embedded in the crisis we are in are ideas. And that in fact the planet is in the grip of a false metaphysics, and scientific image of nature that really comes most recently from Descartes. His conviction that matter, and with it the Earth, are simply the weighable, the measurable the extended in space and time, utterly devoid of any inner life of mind thoughts or Consciousness. This view of matter and the Earth goes back to the very dawn of thought, Descartes is just its most recent and comprehensive version of things. This view of matter is really a false metaphysics that dominates most of the modern world and underpins the whole scientific image of nature itself. I am proposing this is a false metaphysics and offering a new version of things based on Consciousness. If there is a single idea fueling and driving the destruction of the planet, it is the conviction that the Earth is something without Consciousness. When you intend to ravage and savage the planet, turn it into consumer items to be converted into money in the bank, first pretend that all of the bright beings in the natural world are devoid of Consciousness, just “things” you do not have to have any conscious about destroying them. I am proposing that this term Consciousness, that has appeared as term and a problem for modern science, is the key to getting ourselves right with the Earth. I propose this new theory for science will ramify into every other disciplines as well, and the ways we think just about everything. This old paradigm based on a false theory of matter, has run its course, it has brought us to the brink of our own extinction. We need new ideas, and we need them now. You will find my recent work in the Scientific God Journal under the title: Consciousness as the Organizing Principle, and a very complete version of my arguments for this term Consciousness, along with radical reinterpretation of modern science itself, on the Galileo Commission site. It is a radical moment for all of the thinkers among us, take this as my small contribution to rethinking ourselves in new terms, based on my understanding that the true basis for the whole of Being, the Universe, the Cosmos, and with them Ourselves, is this term Consciousness. And unpack it. And I predict that with this term Consciousness every intractable human problem in both science and the modern world will go down like a line of dominos before it. And so, here are a few new ideas I would offer you. These are the times of the Reappearance of the Feminine, a great Awakening as of Springtime after a long dark and lonely Winter. We are leaving behind us an old civilization based on male dominance, fear lies and control, now in decay, disease, and death disintegrating all around us. We are moving into a whole new civilization based on Love and Truth. A new civilization requires new basic terms values and first principles. And so, I return to Descartes. 1. Just over 300 years ago a young man travelling with the army found himself stranded in a foreign town in a bare room lit by a candle with nothing to do but think. And in a couple of nights Descartes laid the entire thought foundations for modern science and most of the modern world. It isn’t clear that modern philosophers with their funding sources of grants, their graduate students, their reputation and all the perks and privileges of academia can do any better. Descartes claimed there are just two principles in the Universe, Matter and Mind. God was pure mind; human beings were mind and matter. And we are the only ones on the planet with mind. Everything else all of the bright array of beings who share the planet with us are simply matter. And matter? The weighable the measurable the extended in space and time utterly devoid of any inner life of thoughts, feelings, or Consciousness This classification was a boon for developing science. Scientists did not have to worry about any inner life in nature, ideas or Consciousness, they could just set about weighing and measuring, as they have until the present day. But it was a disaster for the Earth. Earth was once worshiped as a Goddess. With Descartes she became almost overnight “things.” Simply commodities and resources, an endless of supply of consumer items, to be turned into money in the bank. And now Descartes concept of matter is up for us. If there is a single idea that is enabling and driving the destruction of the Earth, it is the conviction that Earth is something without Consciousness. This whole scientific image of nature as something devoid of Consciousness is driving the destruction of the Earth, and driving us all insane, and it needs to change. Ever since Descartes formulated Dualism, scientists and philosophers have been trying to get rid of it. They have done this by attempting to make matter the fundamental term of reality, and to explain mind or Consciousness in terms of matter. And now, Consciousness has emerged as term and a problem for modern science. 2. The prevailing Universe picture has been mainly based on the belief that Consciousness appears only at the end of evolution in ourselves and some of the higher animals when matter attains “a certain state of complexity” and it is produced by neurons in the brain. But Consciousness is nothing real in itself, the neurons are doing the work, Consciousness is simply a byproduct of neural activity. As William James summed up this position, not his: Consciousness is to the brain as a shadow to the runner, it runs beside him but never influences his stride. And so, the great problem of neuroscience: How do neurons produce Consciousness? So far, no one has been able to say how. All we have is parallelism. Two processes are running in parallel a train of neural events and a train of Consciousness events. And so, which one is primary? The face of neuroscience and much of the modern world is that the neural events are what is really real, Consciousness is secondary. The conviction that Consciousness is something produced by neurons is so deeply held and has been so for so long, that it seems like an obvious fact. It is in fact an assumption and a theory, and it could all well be the other way around. The scientific image of nature as something devoid of Consciousness is now up for us. It has enabled the destruction of the Earth, and we are now at a unique place in our own evolution where we can foresee if we continue on as we are, our own extinction. And so, there are the best reasons for rethinking Descartes concepts of matter. Scientists and philosophers have been trying to escape dualism by making matter the fundamental term. And now, this has not worked. In a nutshell: The mind body problem, the “hard problem of Consciousness” ---how do neurons in the brain, atoms and molecules give rise to ideas thoughts and feelings and who what and where is the “I” who sees them? And then, the near-death experience. I remind you of the truth of the near-death experience. The body in the bed is brain dead eeg flat, but the patient, pure Consciousness is hovering at the ceiling watching the doctors work over the body below, able to read dials he could not have seen from the bed, sometimes able to events in the corridor beyond the room, all subsequently confirmed by the doctors. Evidence as good as it gets that the patient really was out of his body. And so, the patient is able to see without eyes, hear without ears and to think and remember without a brain. There is not a single scientific principle that can account for this. And I am proposing that the near-death experience is a threshold and a key for a much deeper understanding of the Universe. These three problems are so hard they cannot be solved by present scientific principles and therefore call all present principles and ways of thought into question. When your axioms cannot account for the data, it is time to drop them and find better fundamental terms. We have not been able to solve the problems of Consciousness with the assumption that matter is the most basic and fundamental term. And so, if Cartesian dualism is to be overcome at all, we need to try it the other way around. 3. We are in the position of Copernicus. Remember Copernicus? He was making his calculations on the assumption that the Earth was the center of the Universe until they became so unwieldy that he decided to try it all the other way around. To make the sun the center, the Earth revolving around it. And lo, all of his data fell neatly into place. I am proposing a whole new Universe picture. Evolution makes us continuous with every other being, Consciousness would not be in us if it were not in them also, and then, in the atoms and molecules and cells that make up living beings. I propose to make Consciousness the most basic term and see what follows. I am in fact, proposing a new Copernican Revolution, that Consciousness is the true basis for the Universe and the right fundamental term, for science itself all other disciplines as well, and the whole of the modern world. And I predict that every intractable problem in science or the modern world, will go down like a line of dominos before it. In a nutshell: Consciousness and not matter is “first and fundamental” in the Universe, it is there from the very “Beginning” everything has it and all of the true causalities, the explanatory principles belong to it. Not to matter. What then is matter? All matter is an Appearance of something much more real, Consciousness. All matter is an expression of Consciousness, even the least little bit of matter contains Consciousness, and is an expression of Consciousness. And so, we revise the scientific image of nature. Science at present is two termed, matter/energy. It needs to become three termed: Matter/energy/Consciousness, with the recognition that all of the basic principles, the true causalities of events, and of the Universe belong to Consciousness. And now physics. To accommodate this new Universe picture physics needs to alter just one term, energy. All energy contains Consciousness, that idea alone will change our world forever. The animals the trees the plants are just as conscious as we are, as is the Earth herself, a continuum of many forms of Consciousness of which our own is one. The Earth consists of interacting and intersecting forms of Consciousness, Consciousness within Consciousness within Consciousness within Consciousness. A remarkable work has just appeared entitled “Beyond Words What Animals Feel and Think.” Describing in detail how like ourselves all of the animals are. And the more we can see how like us everything else really is, the harder it will be for us to do them all in, in the interest of turning them into consumer items. And so, here is a new principle of understanding, that entails completely new forms of explanation, and a whole new Universe picture to be spelled out in terms of Consciousness and its various properties. This is a Universe that is aware awake and Enspirited throughout. And so, there is only one principle, not two. And so, how does Consciousness work? There is “no mind/body problem.” My Consciousness is aligning the Consciousness of neurons in the brain, that aligns the Consciousness of all other cells in the body, that aligns the Consciousness of molecules and atoms themselves. Mainly from the top down, but also interactive, and not reduction to the purely physical forces. 4. And so, here is a radical new Universe picture, and with it a new way to think about the Universe itself, the Earth, Ourselves, and Spirit. It requires a complete revision of both of the terms of modern science itself. The problem of the modern world owes in deep measure to Descartes concept of matter as something devoid of interior life. But he is only the most powerful and simple expression of a whole tradition of thought that predates him, that goes back to the very dawn of thought itself. It owes to the conviction of both Plato and Aristotle that the Earth was something devoid of reason, or mind, and therefore something to be held in complete contempt. We are emerging from a civilization based on contempt for the Earth, and down the road, it will become contempt for the body, sex, women, the Native people, and people of color everywhere. And so, this entire tradition is now up for us, it has brought us to the brink of our own extinction, and we need to get beyond it. I am proposing that this term Consciousness is the way out. The missing piece of the puzzle and the idea whose time has come. And it will enable us to move into a completely new civilization based on respect for the Earth, women, sex, people of color, that honors everyone and has a place for all. Revolutions in science occur when new data show up that cannot be accounted for by existing paradigms (Thomas Kuhns: The Structure of Scientific Revolutions). Consciousness, and all the properties and problems of Consciousness are just such data, and they compel a new scientific revolution. But we are a scientific age. A new Copernican revolution in science is such as revolution everywhere. And Consciousness is the key to getting every other reality at all levels and depths right as well. It will enable us finally to get the Earth right, get ourselves right with the Earth, and with one another. Archimedes: Give me a place to stand, and I will move the world. I am proposing a new synthesis among our fundamental terms in which this term Consciousness is the new lynchpin. And so, I return once more to Descartes. Descartes in the Mediations was a dualist, they were two realities interacting in the pineal gland mind and body. But Descartes age was high on “Cartesianisms,” on Descartes concept of matter. Animals were simply cleverly wired machines (Hobbes called them automata). Rumor had it that Descartes followers went about kicking dogs reasoning that their howls were not due to any feelings of pain, but simply the wiring of the machine. We have been doing this to the Earth ever since, and now the Earth is not going to stand for it any longer. It is time we all woke up. There is no matter in Descartes sense of it, or in Plato and Aristotle’s sense of it. This old concept of matter is embedded in all of the crisis and catastrophes---fires, drought, pandemics, over population, the disintegrating economies, our ways of life, capitalism and consumerism themselves. All matter is Enspirited, contains Consciousness, and is an expression of Consciousness. Now I tell you something about Descartes you may not know. In the Mediations was a dualist, there are minds and bodies. But by the end of his life, he knew the truth. Descartes guided his whole life by these vivid dreams, and toward the end of his life he had this dream: He saw the Universe as a giant machine and in the heart of the machine propelling the whole, was Consciousness. And so, out of the shades and shadows and into the light of day. Here is a renewed and a new science, a new cosmology. And with it, a revolutionary new metaphysics. The whole of the modern world is in the grip of a false metaphysics, based on Descartes theory of matter. Here is a new, and finally true metaphysics based on the single term, Consciousness. 5. And in all my works, I spell out this new metaphysics based on Consciousness, and what it means for how we think about virtually everything: The Universe, the Earth, Ourselves, and Spirit. Of who, what and where Spirit is, that we may connect with Spirit everywhere. There is only one term for the Universe, and not two, this single term Consciousness, that manifests itself in two modes: Consciousness in Form, Consciousness Formless. And so, here is a sketch of what this new term Consciousness really means. For four great aspects of the real, four new cornerstones for a whole new civilization on Earth, based on Love and Truth. A. For the Universe: New causalities, all matter is an expression of Consciousness, awake and aware, Enspirited throughout, which has a place for the causalities of love ideas intent plan and purpose, what Aristotle called final causality, that for the sake of which. And it has a place for subjectivity, the greatest mystery in the whole of the Universe, knowable only in ourselves, but there in everything else as well, bedrock in the chain of causalities. The buck stops here. Here are two very different Universe pictures. The Cartesian Universe consists of things, pushed, and pulled around by the physical forces that function blind. The true Universe consists of selves and not things. Selves at least in ourselves, are organized and focused about an I, an I am, or a sense of self in everything, ever other being dear to itself. Just one aspect of subjectivity. And so, it is Consciousness all the way down, and subjectivity all the way down. Science proceeds on the basis of the negligible of what it can leave out as unimportant. Subjectivity is the ultimate animating principle of the Universe. What Descartes really did was to strip from nature what is most essential about it, its animating principle Consciousness. And so, we come out of a long history of “clunk.” The great male thinkers have killed creation. First in concepts and now in the lived life. And so, after centuries of fruitless search, not all of it in vain, finally, and just in time the true basic principle for the Universe has shown up in virtually all disciplines, this term Consciousness. B. And so, the Earth: What does this term Consciousness really mean for the Earth? It means that all of the bright array of beings around us have Consciousness. And the more we can realize how like us they are, the harder it will be to do them all in. Is this what it really means to be “made in the image of Spirit?” To destroy ever other living being? The god of Genesis is a creator god who has created billions and billions of living forms. Man, taking himself to be made in the image of Spirit has destroyed virtually all of it. The Earth is not things, not commodities and resources, not an endless array of consumer goods. The Earth consists of conscious living spiritual beings like ourselves, who need to be honored and respected, nurtured and cared for, to be Loved. And if the human race can finally pull it off and bring Love here and start helping the Earth rather than destroying her, even our vast numbers now at 8 billion, may not matter. Deeply embedded in the modern world is a false cosmology based on the hitherto useful but ultimately false metaphysics of Descartes. In particular it owes to Descartes concepts of matter, and of the Earth, as something devoid of Consciousness. And now this concept is up for us. If there is a single idea enabling and driving the destruction of the planet, it is the conviction that the Earth is something devoid of Consciousness. And we all have a vested interest in keeping this false concept in place. When you intend to ravage and savage the Earth, to turn it into consumer items, and money in the bank, first pretend she has no Consciousness that way we need not have any conscious about it. We are in deep trouble with the planet. Virtually all human economies are based on the destruction of the Earth, and we are now at a place in our history where we can foresee if we continue on as we are, our own extinction. And so, here are a few new guiding principles for the planet, based on Consciousness. Consciousness has field properties. The Earth is Consciousness throughout, connected and interconnected throughout, intersecting and interacting fields of Consciousness of which our own is one. To harm one is to harm all. Only this understanding of the Earth can set our destructive technologies, and capitalism, their goals, and their limits. The real problem with capitalism, and with civilization itself is it has no true understanding of the Earth, all the exquisite networks and webworks that connect all beings with one another. Nor it is about to admit that the Earth is our “life support system,” reason gone insane in the modern world. We are destroying our life support system, capitalism is the full expression of the human death wish in the modern world, and it needs to change. And now, let the barriers between ourselves and all other beings go down. C. And so, Ourselves. This new Universe picture based on Consciousness opens into and has a place for new concepts of human identity. In particular, it has a place for reincarnation, a process whereby one Consciousness dons’ bodies again and again and lays them down. This is a process wholly impossible on materialistic theories. This new view of things puts a higher perspective on Freudian determinism, which is a partial knowing in a greater account. We are something more than the victims of genes drives and childhood. We are immortally eternal evolving spiritual beings who take on a cycle of earthly lives in order to achieve certain goals and abilities, and then, our earthly cycle complete, we depart the Earth plane for other areas of the Universe physical or purely spiritual, to continue our evolution elsewhere. The scientific evidence for reincarnation is increasing on all sides, see for example the work of Michael Newton, a mainstream scientist forced to confront the fact of reincarnation. A completely new reincarnational picture is now emerging everywhere. We choose our parents for abilities we could acquire through them. We all come here with reasons for being here, and we all have free will, an original property of Consciousness, and we work out our issues accordingly. We all come here with the same reason for being here. It is to experience the Earth plane and human society at all levels and depths, in every social role, every climate, both sexes male and female and in all races. We are all here to acquire a deep understanding of the Earth plane with all its challenges and all of its present problems. We are not here to devour and consume the Earth, but to honor her for the learning experience she makes available for us. And so, reincarnation is the great equalizer, the fundamental basis for any bill of rights. Many of the problems of the modern world owe to the fact that reincarnation has not been taught in the West. Reincarnation was the center piece of Jesus teachings. It was banned by a fourth century council that decided the populous could best be controlled through fear. They edited all mention of reincarnation from the scriptures, and inserted fear terms instead, a judgmental god, and so on. If reincarnation had been taught, there would have been no suppression of women, but the gift of both sexes would have been honored and developed. And there would have been no reason for contempt for people of color because who they are, we have been or will be. There is no reason to look down on anyone when we know that our own identity includes membership in theirs. The truth of reincarnation needs to be the foundation for getting ourselves right with the Earth, and with one another. D. And so, Spirit. This new Universe picture, based on Consciousness, has a place for Spirit as simply Infinite Consciousness. All universes all worlds are formed out of this Consciousness, divine and sacred throughout. And Spirit is everywhere in the Earth and can be connected everywhere. And the Native North American people had things right. Everything has its own Spirit, and without contradiction is also an expression of Spirit. And what I am really doing in terms of concepts of modern science is presenting the Native North American version of the Real. And this new Universe picture has a place for the essential insight of all religions almost smothered by church tradition: Love. Not an emotion but a great Oneness, that can be found everywhere, even in a busy city street. But especially in the Earth, that is levels and depths of Oneness everywhere. And so, for the first time in their long and bitter antagonistic history, science and religion now share a common base with this term Spirit, the lynchpin in a new conceptual framework. And I offer an interpretation of where we are in our history, this present moment of historical grace, and these tumultuous times in which we live, the return of the Goddess and the Divine Feminine, the Reappearance of the Feminine in history, the Advent of Women, this great Awakening, as of Springtime after a long dark Winter, the Awakening, the Second Coming. And so, stay tuned! (shrink)

Consumer behavior analytics has become a pivotal aspect for businesses to understand and predict customer preferences and actions. The advent of machine learning (ML) algorithms has revolutionized this field by providing sophisticated tools for data analysis, enabling businesses to make data-driven decisions. However, the effectiveness of these ML algorithms significantly hinges on the optimization techniques employed, which can enhance model accuracy and efficiency. This paper explores the application of various optimization techniques in consumer behaviour analytics using machine learning (...) algorithms. By focusing on the optimization of key parameters, the study aims to improve the predictive power of models and reduce computational costs. (shrink)

Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black-box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general (...) ethical frameworks to clinical decision-making entails conceptual questions: Is explicability a free-standing principle? Is it already covered by the well-established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual-ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to? (shrink)

Constitutive mechanistic explanations are said to refer to mechanisms that constitute the phenomenon-to-be-explained. The most prominent approach of how to understand this constitution relation is Carl Craver’s mutual manipulability approach to constitutive relevance. Recently, the mutual manipulability approach has come under attack (Leuridan 2012; Baumgartner and Gebharter 2015; Romero 2015; Harinen 2014; Casini and Baumgartner 2016). Roughly, it is argued that this approach is inconsistent because it is spelled out in terms of interventionism (which is an approach to causation), (...) whereas constitutive relevance is said to be a non-causal relation. In this paper, I will discuss a strategy of how to resolve this inconsistency, so-called fat-handedness approaches (Baumgartner and Gebharter 2015; Casini and Baumgartner 2016; Romero 2015). I will argue that these approaches are problematic. I will present a novel suggestion of how to consistently define constitutive relevance in terms of interventionism. My approach is based on a causal interpretation of mutual manipulability, where manipulability is interpreted as a causal relation between the mechanism’s components and temporal parts of the phenomenon. (shrink)