The main objective o f this descriptive paper is to present the general notion of translation between logical systems as studied by the GTAL research group, as well as its main results, questions, problems and indagations. Logical systems here are defined in the most general sense, as sets endowed with consequence relations; translations between logical systems are characterized as maps which preserve consequence relations (that is, as continuous functions between those sets). In this sense, logics together with translations form a (...) bicomplete category of which topological spaces with topological continuous functions constitute a full subcategory. We also describe other uses of translations in providing new semantics for non-classical logics and in investigating duality between them. An important subclass of translations, the conservative translations, which strongly preserve consequence relations, is introduced and studied. Some specific new examples of translations involving modal logics, many-valued logics, para- consistent logics, intuitionistic and classical logics are also described. (shrink)

I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.

This chapter responds to Michael Tamir and Elay Shech’s chapter “Understanding from Deep Learning Models in Context.”.

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us (...) to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review. (shrink)

Should objects count as necessarily having certain properties, despite their not having those properties when they do not exist? For example, should a cat that passes out of existence, and so no longer is a cat, nonetheless count as necessarily being a cat? In this essay I examine different ways of adapting Aldo Bressan’s MLν so that it can accommodate an affirmative answer to these questions. Anil Gupta, in The Logic of Common Nouns, creates a number of languages that have (...) a kinship with Bressan’s MLν , three of which are also tailored to affirmatively answering these questions. After comparing their languages, I argue that metaphysicians and philosophers of language should prefer MLν to Gupta’s languages in most applications because it can accommodate essential properties, like being a cat, while being more uniform and less cumbersome. (shrink)

Abstract: This study introduces a machine learning-based model for predicting student performance using a comprehensive dataset derived from educational sources, encompassing 15 key features and comprising 62,631 student samples. Our five-layer neural network demonstrated remarkable performance, achieving an accuracy of 89.14% and an average error of 0.000715, underscoring its effectiveness in predicting student outcomes. Crucially, this research identifies pivotal determinants of student success, including factors such as socio-economic background, prior academic history, study habits, and attendance patterns, shedding light on the (...) nuanced dynamics of student performance. The key influential features identified in this study offer valuable insights into the complex factors shaping student achievement. These insights are vital for educators, policymakers, and institutions seeking to enhance educational outcomes and promote equitable access to quality education. This research provides a data-driven foundation for proactive interventions, personalized learning strategies, and support systems, ultimately contributing to improved student performance and academic success. The high accuracy of the predictive model and the feature analysis it provides empower decision-makers in the education sector. This model holds significant potential for applications in student performance monitoring, early intervention, and tailored educational strategies. By adopting a data-driven approach, this work advances the field of educational analytics and contributes to the goal of fostering student success and educational equity. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on (...) developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for this tutorial are (...) the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

Value Sensitive Design (VSD) is an established method for integrating values into technical design. It has been applied to different technologies and, more recently, to artificial intelligence (AI). We argue that AI poses a number of challenges specific to VSD that require a somewhat modified VSD approach. Machine learning (ML), in particular, poses two challenges. First, humans may not understand how an AI system learns certain things. This requires paying attention to values such as transparency, explicability, and accountability. Second, ML (...) may lead to AI systems adapting in ways that ‘disembody’ the values embedded in them. To address this, we propose a threefold modified VSD approach: 1) integrating a known set of VSD principles (AI4SG) as design norms from which more specific design requirements can be derived; 2) distinguishing between values that are promoted and respected by the design to ensure outcomes that not only do no harm but also contribute to good; and 3) extending the VSD process to encompass the whole life cycle of an AI technology in order to monitor unintended value consequences and redesign as needed. We illustrate our VSD for AI approach with an example use case of a SARS-CoV-2 contact tracing app. (shrink)

Machine learning systems (MLS) that model low-level processes are the cornerstones of current AI systems. These ‘indirect’ learners are good at classifying kinds that are distinguished solely by their manifest physical properties. But the more a kind is a function of spatio-temporally extended properties — words, situation-types, social norms — the less likely an MLS will be able to track it. Systems that can interact with objects at the individual level, on the other hand, and that can sustain this interaction, (...) can learn responses to increasingly abstract properties, including representational ones. This representational capacity, arguably the mark of intelligence, then, is not available to current MLS’s. (shrink)

Many philosophers hold that generics (i.e., unquantified generalizations) are pervasive in communication and that when they are about social groups, this may offend and polarize people because generics gloss over variations between individuals. Generics about social groups might be particularly common on Twitter (X). This remains unexplored, however. Using machine learning (ML) techniques, we therefore developed an automatic classifier for social generics, applied it to 1.1 million tweets about people, and analyzed the tweets. While it is often suggested that generics (...) are ubiquitous in everyday communication, we found that most tweets (78%) about people contained no generics. However, tweets with generics received more “likes” and retweets. Furthermore, while recent psychological research may lead to the prediction that tweets with generics about political groups are more common than tweets with generics about ethnic groups, we found the opposite. However, consistent with recent claims that political animosity is less constrained by social norms than animosity against gender and ethnic groups, negative tweets with generics about political groups were significantly more prevalent and retweeted than negative tweets about ethnic groups. Our study provides the first ML-based insights into the use and impact of social generics on Twitter. (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)

Maximum Parsimony (MP) and Maximum Likelihood (ML) are two methods for evaluating which phlogenetic tree is best supported by data on the characteristics of leaf objects (which may be species, populations, or individual organisms). MP has been criticized for assuming that evolution proceeds parsimoniously -- that if a lineage begins in state i and ends in state j, the way it got from i to j is by the smallest number of changes. MP has been criticized for needing to assume (...) some model or other of the evolutionary process even though biologists often do not know what that process really is. This paper critically evaluates both criticisms. (shrink)

Except in extraordinary circumstances, patients' clinical care should reflect their preferences. Incapacitated patients cannot report their preferences. This is a problem. Extant solutions to the problem are inadequate: surrogates are unreliable, and advance directives are uncommon. In response, some authors have suggested developing algorithmic "patient preference predictors" (PPPs) to inform care for incapacitated patients. In a recent paper, Earp et al. propose a new twist on PPPs. Earp et al. suggest we personalize PPPs using modern machine learning (ML) techniques. In (...) this paper, I argue that, contrary to Earp et al.'s claim, personalized patient preference predictors are neither technically feasible nor ethically desirable. (shrink)

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...) problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

I examine two startling claims attributed to some philosophers associated with Megara on the Isthmus of Corinth, namely: Ml. Something possesses a capacity at t if and only if it is exercising that capacity at t. M2. One can speak of a thing only by using its own proper A6yor;. In what follows, I will call the conjunction of Ml and M2 'Megaricism' .1 The lit­ erature on ancient philosophy contains several valuable discussions of Ml and M2 taken individually .2 (...) But there is no discussion of them together, much less of their logical relations. I intend to remedy that lack, and to show why it is a lack worth remedying. My aims are both philosophical and historical. Inevitably, in saying anything new and interesting about Megaric views, I will be adopting assumptions scarcely free from controversy, and indulging in quite a bit of charitable recon­ struction. But the resulting picture is of considerable interest. For it explains why, although Ml and M2 seem to have little to do with one another, it is in fact small wonder that anyone who held one would hold the other, for, as I attempt to show, they entail each other. It explains why the Megarics are associated in the primary literature with the names of such diverse philosophical ancestors as Parmenides and Protagoras. And it explains why, although they are both false, Ml and M2 were claims taken seriously by both Plato and Aristotle, who in developing their own views on modality and language saw reason to mention Megaricism, and to argue against it. (shrink)

In a recent reply to our article, “What is Interpretability?,” Prasetya argues against our position that artificial neural networks are explainable. It is claimed that our indefeasibility thesis—that adding complexity to an explanation of a phenomenon does not make the phenomenon any less explainable—is false. More precisely, Prasetya argues that unificationist explanations are defeasible to increasing complexity, and thus, we may not be able to provide such explanations of highly complex AI models. The reply highlights an important lacuna in our (...) original paper, the omission of the unificationist account of explanation, and affords us the opportunity to respond. Here, we argue that artificial neural networks are explainable in a way that should satisfy unificationists and that interpretability methods present ways in which ML theories can achieve unification. (shrink)

As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the (...) theory of AI ethics principles and the practical design of AI systems. In previous work , we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service’. (shrink)

Turkish Epistemoloji rasyonel inançların felsefi analizini konu edinmesi nedeniyle gerekçelendirme edimine merkezi bir önem atfeder. Gerekçelendirme kişinin bir önermeye inanmak için gerekçeye sahip olunmasını sağlayan koşul veya koşullar dizisinin tespit edilmesini içerir. İnançlarımızın birçoğunun çıkarımsal olmayan gerekçelerinin bulunduğu şeklindeki ılımlı/yanılırcı temelci perspektifle uyum sağlayan bir gerekçelendirme teorisi olarak Michael Huemer tarafından ortaya konan görüngüsel muhafazakarlık ilkesi, bu türden bir koşulu tanımlar. GM formülasyonuna göre eğer S’ye p olarak görünüyorsa, çürütücü etmenlerin yokluğunda S’nin p’ye inanmak için en azından bir dereceye kadar (...) gerekçesi vardır. Görüngüler ise rasyonel inanç oluşturmanın olanağına referans veren, inanç ve inanma eğilimlerinden farklı, sui generis deneyimlerdir. Bu makalede görüngülerin doğasından hareketle GM’yi tanıttım ve GM’ye yöneltilen eleştirilerin bir bölümünü tartıştım. Yer verdiğim eleştiriler, GM’nin irrasyonel/tehlikeli görüngüler ve bilişsel nüfuzdan kaynaklanan inançları dahi gerekçelendirilmiş kabul ettiği, GM teorisyenlerinin görüngülere gerekçelendirme edimi özelinde biçtiği başat rolün ve inanç ve görüngüler arasında öngörülen farklılıkların abartılı olduğu itirazlarını içerir. Bana göre sözü edilen eleştirilerin hiçbiri, GM’nin reddedilmesini gerektiren argümanlar içermez. Görüngülerin epistemik gerekçelendirmedeki rolünü tesis eden GM veya benzeri bir ilkeyi kabul etmek için temel neden ise bunun şüphecilik haricindeki olası alternatiflerinin belirsizliğidir. Çünkü eğer görüngüler, inanç oluşturmada epistemik faile rehberlik edecek göstergeler ise bu, failin ilgili göstergelerden yoksun olduğunda herhangi bir şeye rasyonel olarak inanmasının olanaksızlaşacağı anlamına gelir. English Because epistemology deals with the philosophical analysis of rational beliefs, it assigns central importance to justification. Justification involves identifying the condition or set of conditions that enables one to have grounds for believing a proposition. The principle of phenomenal conservatism, put forward by Michael Huemer as a justification theory that accords with the moderate/fallibilist foundationalist perspective that many of our beliefs have non- inferential justifications, is a principle that defines such a condition. According to the PC formulation, if it seems to S that p, then, in the absence of defeaters, S thereby has at least some degree of justification for believing that p. Seemings/appearances are sui generis experiences that refer to the possibility of rational belief formation and are different from beliefs and dispositions to believe. In this article, I introduce PC based on the nature of seemings and evaluate some of the criticisms directed at PC. The criticisms I discuss include objections that PC even justifies beliefs stemming from irrational/dangerous seemings and cognitive penetration, and that the dominant role that PC theorists assign to seemings in the act of justification and the differences envisaged between beliefs and seemings are exaggerated. In my opinion, none of these criticisms contain arguments that would lead to the rejection of PC. The main reason to accept PC or a similar principle that establishes the role of seemings in epistemic justification is the vagueness of its possible alternatives other than skepticism. For, if seemings are indicators that guide the epistemic agent in forming beliefs, this means that it is impossible for the agent to believe anything rationally when she lacks the relevant indicators. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets of (...) options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)

Experiments are presented showing that visual search for Mueller-Lyer (ML) stimuli is based on complete configurations, rather than component segments. Segments easily detected in isolation were difficult to detect when embedded in a configuration, indicating preemption by low-level groups. This preemption—which caused stimulus components to become inaccessible to rapid search—was an all-or-nothing effect, and so could serve as a powerful test of grouping. It is shown that these effects are unlikely to be due to blurring by simple spatial filters at (...) early visual levels. It is proposed instead that they are due to more sophisticated processes that rapidly bind contour fragments into spatially-extended assemblies. These results support the view that rapid visual search cannot access the primitives formed at the earliest stages of visual processing; rather, it can access only higher-level, more ecologically-relevant structures. (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the (...) resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (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)

This article identifies and examines a tension in mental health researchers’ growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The (...) imperatives of the “participatory turn” in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (shrink)

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...) counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do something? (...) And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...) work taking place at the intersection of text mining and mental health so that we can (2) highlight and analyze several pressing ethical issues that are arising in this rapidly growing field and (3) suggest productive directions for how these issues might be better addressed within future interdisciplinary work to ensure the responsible development of text mining approaches in psychology generally, and in mental health fields, specifically. In Section 1, we review some of the recent literature on text-mining and mental health in the contexts of traditional experimental settings, social media, and research involving electronic health records. Then, in Section 2, we introduce and discuss ethical concerns that arise before, during, and after research is conducted. Finally, in Section 3, we offer several suggestions about how ethical oversight of text-mining research might be improved to be more responsive to the concerns mapped out in Section 2. (shrink)

Delicate Tissue Tumors (STT) are a type of sarcoma found in tissues that interface, backing, and encompass body structures. Due to their shallow recurrence in the body and their extraordinary variety, they seem, by all accounts, to be heterogeneous when seen through Magnetic Resonance Imaging (MRI). They are effortlessly mistaken for different infections, for example, fibro adenoma mammae, lymphadenopathy, and struma nodosa, and these indicative blunders have an extensive unfavorable impact on the clinical treatment cycle of patients. Analysts have proposed (...) a few AI models to characterize cancers, however none have sufficiently tended to this misdiagnosis issue. Likewise, comparative investigations that have proposed models for assessment of such cancers generally don't think about the heterogeneity and the size of the information. Thusly, we propose an AI based approach which joins another strategy of pre handling the information for highlights change, resampling methods to dispense with the predisposition and the deviation of precariousness and performing classifier tests in light of the and Deep learning Algorithm as Artificial brain organization. -/- Tumors (STT). (shrink)

In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term “explanation” in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanist models. In this chapter, I show that the authors’ claim that these accounts can be applied to deep neural networks as they would to any natural phenomenon is mistaken. I also (...) provide a more general argument as to why the notion of explainability as it is currently used in the XAI literature bears little resemblance to the traditional concept of scientific explanation. It would be more fruitful to use the label “understandable AI” to avoid the confusion that surrounds the goal and purposes of XAI. In the second half of the chapter, I argue for a pragmatic conception of understanding that is better suited to play the central role attributed to explanation in XAI. Following De Regt (2017) and Kuorikoski and Ylikoski (2015), the conditions of satisfaction for understanding an ML system are fleshed out in terms of an agent’s success in using the system. (shrink)

Despite recent breakthroughs in the field of artificial intelligence (AI) – or more specifically machine learning (ML) algorithms for object recognition and natural language processing – it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catalogue of features which imply that NI works differently from current AI not only in a gradual sense, but in a more substantial way: NI is closely related (...) to consciousness, intentionality and experiential features like qualia (the subjective contents of mental states)1 and allows for understanding (e.g., taking insight into causal relationships instead of ‘blindly’ relying on correlations), as well as aesthetical and ethical judgement beyond what we can put into (explicit or data-induced implicit) rules to program machines with. Additionally, Psychologists find NI to range from unconscious psychological processes to focused information processing, and from embodied and implicit cognition to ‘true’ agency and creativity. NI thus seems to transcend any neurobiological functionalism by operating on ‘bits of meaning’ instead of information in the sense of data, quite unlike both the ‘good old fashioned’, symbolic AI of the past, as well as the current wave of deep neural network based, ‘sub-symbolic’ AI, which both share the idea of thinking as (only) information processing: In symbolic AI, the name explicitly references to its formal system based, i.e. essentially rule-based, nature, but also sub-symbolic AI is (implicitly) rule-based, only now via globally parametrized, nested functions. In the following I propose an alternative view of NI as information processing plus ‘bundle pushing’, discuss an example which illustrates how bundle pushing can cut information processing short,and suggest first ideas for scientific experiments in neuro-biology and information theory as further investigations. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it (...) is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

Belirli betimlemeler, bir belirli tanımlıkla (Türkçede seslendirilmeyen ancak İngilizcede karşılığı "the" olan) başlayan "İngiltere'nin kralı", "Çin'in başkenti" gibi deyimlerdir. Russell bu yazıda belirli betimlemelerin mantıksal olarak nasıl çözümlenmesi gerektiği ile ilgili kendi betimlemeler kuramını ortaya atar. Russell'ın savı, belirli betimlemeler doğru bir biçimde çözümlenirse bir karşılığı olmayan "Fransa'nın şimdiki kralı" gibi deyimlerin yol açtığı türden birçok felsefi bilmecenin ortadan kalkacağıdır.

The article argues that AI can enhance the measurement and implementation of democratic processes within political parties, known as Intra-Party Democracy (IPD). It identifies the limitations of traditional methods for measuring IPD, which often rely on formal parameters, self-reported data, and tools like surveys. Such limitations lead to the collection of partial data, rare updates, and significant demands on resources. To address these issues, the article suggests that specific data management and Machine Learning (ML) techniques, such as natural language processing (...) and sentiment analysis, can improve the measurement (ML about) and practice (ML for) of IPD. The article concludes by considering some of the principal risks of ML for IPD, including concerns over data privacy, the potential for manipulation, and the dangers of overreliance on technology. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even after they have received regulatory (...) approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

RESUMO A alimentação é o fator que mais onera um sistema de produção animal. Assim, a utilização de diferentes estratégias de alimentação dos animais ainda é o grande desafio da nutrição animal, principalmente, levando em consideração as exigências nutricionais de diferentes categorias de ruminantes, em especial bovinos em regiões tropicais, haja vista que a sazonalidade na produção de forragens afeta diretamente a produção bovina, promovendo inadequação no atendimento das exigências nutricionais dos animais principalmente em minerais e vitaminas. Uma alimentação que (...) não atenda as exigências nutricionais dos ruminantes gera prejuízos no sistema de produção, além de, em casos mais graves, ocasionar a perda do animal. Estudos que abordam o efeito e a importância de vitaminas e minerais na nutrição bovina são escassos e necessários. Recentemente a National Research Council (NRC) apresentou novos registros das exigências nutricionais das vitaminas e minerais. Essas alterações proporcionam um impacto econômico significativo em relação à nutrição animal. Nesse sentido, objetivou-se com esta revisão trazer informações acerca de vitaminas e minerais na nutrição de bovinos em pastejo. -/- Palavras-chave: Alimentação animal; forragem; pastagem; requerimentos nutricionais; ruminantes; vitaminas lipossolúveis (Source: AGROVOC). -/- ABSTRACT Animal feed is the factor that most burdens a production system. Thus, the use of different animal feeding strategies is still the great challenge of animal nutrition, taking into account the nutritional requirements of different categories of ruminants, especially cattle in tropical regions, given that seasonality in forage production directly affects cattle production, promoting inadequacy in meeting the nutritional requirements of animals, mainly in minerals and vitamins. A diet that does not meet the nutritional requirements of ruminants generates losses in the production system, in addition to, in more serious cases, causing the loss of the animal. Studies that address the effect and importance of vitamins and minerals in bovine nutrition are scarce and necessary. Recently the National Research Council (NRC) presented new records of the nutritional requirements of vitamins and minerals. These changes provide a significant economic impact on animal nutrition. In this sense, the objective of this review was to bring information about vitamins and minerals in the nutrition of grazing cattle. -/- Keywords: Animal feeding; fat soluble vitamins; forage; nutritional requirements; pasture feeding; ruminants (Source: AGROVOC). -/- REFERÊNCIAS BIBLIOGRÁFICAS -/- 1. Cavalcanti CPL, Silva e Macedo TJ, Gois GC, Menezes VG, Monte APO, Silva AD, Silva DJM, Silva EO, Araújo GGL, Rodrigues RTS, Wischral A, Matos MHT, Queiroz MAA. Licuri oil improves feedlot performance and modifies ruminal fauna of Santa Inês ewes. Liv Sci. 2022; 265:e105093. 2. Busato KC, Chizzotti ML, Rodrigues RTS, Silva TS, Silva IF, Queiroz MAA, Silva KT, Borges JC, Gois GC. Ingestive behavior and growth performance of boer crossbred and indigenous goat kids under different feeding levels in Semiarid Region in Brazil. Trop Agric Res. 2022; 33:299-309. 3. Patton J, Dineen M, Keady TWJ, McGee M, Waters S. Developments in nutrition for pasture-based cattle and sheep systems in Ireland. Irish J Agric Food Res. 2022; 61(1):12-37. 4. McAllister TA, Stanford K, Chaves AV, Evans PR, Figueiredo, EES, Ribeiro G. Nutrition, feeding and management of beef cattle in intensive and extensive production systems. Chapter 5. In: Bazer FW, Lamb GC, Wu G, editors. Animal Agriculture. Sustainability, Challenges and Innovations. 1st ed. Elsevier Inc.: Academic Press; 2020. 5. Byrne L, Murphy RA. Relative bioavailability of trace minerals in production animal nutrition: A review. Anim. 2022; 12:e1981. 6. Arthington JD, Ranches J. Trace mineral nutrition of grazing beef cattle. Anim. 2021; 11:e2767. 7. Silva NCD, Martins TLT, Borges I. Efeito dos microminerais na alimentação de ruminantes. Ci Anim. 2017; 27:75-98. 8. Tufarelli V, Ragni M, Laudadio V. Feeding forage in poultry: A promising alternative for the future of production systems. Agric. 2018; 8:1-10. 9. Elgersma A, Søegaard K, Jensen SK. Fatty acids, α-tocopherol, β-carotene, and lutein contents in forage legumes, forbs, and a grass–clover mixture. J Agric Food Chem. 2013; 61:11913–11920. 10. Collet SG, Demeda MA, Taffarel GV, Taffarel L, Girardini LK, Nesi CNH, et al. Efeito de um suplemento mineral traço e vitaminas A e E injetáveis sobre a produção e composição de leite em vacas Holandesas. Rev Ci Agrovet. 2017; 16:463-472. 11. Silva NCD, Martins TLT, Borges I. Macrominerais: Deficiências E Interações Com A Produção E Nutrição De Ruminantes. Scient Agr Paranaensis. 2018; 17:263-271. 12. Danieli B, Schogor ALB. Uso de aditivos na nutrição de ruminantes: revisão. Vet Zootec. 2020; 27:1-13. 13. Goff JP. Invited review: Mineral absorption mechanisms, mineral interactions that affect acid–base and antioxidant status, and diet considerations to improve mineral status. J Dairy Sci. 2018; 101:2763–2813. 14. NRC. National Research Council. Nutrient requirements of beef cattle. 8th ed.Washington, DC.: The National Academy Press; 2016. 15. Silva LFC, Valadares Filho SC, Engle TE, Rotta PP, Marcondes MI, Silva FAZ, et al. Macrominerals and trace element requirements for beef cattle. Plus One. 2015; 10:e0144464. 16. Miranda MMF, Ferrarezi Junior E, Cavichioli FA. Boas práticas agropecuárias: Os benefícios da suplementação alimentar em ruminantes. Int Tec. 2021; 18:455-465. 17. Bhanderi BM, Goswami A, Garg MR, Samanta S. Study on minerals status of dairy cows and their supplementation through área specific mineral mixture in the state of Jharkhand. J Anim Sci Techn. 2016; 58:1-8. 18. Rezende FV, Silva NCD, Florentino LA, Rezende AV. Minerais: fatores que interferem na biodisponibilidade de macro e microminerais para fêmeas ruminantes durante o período de gestação. Nucleus Anim. 2019; 11:123-136. 19. NRC. National Research Council. Nutrient requeriments of dairy cattle. 7th rev.ed. Washinton, DC.: The National Academy Press; 2001. 20. Marku A, Galli A, Marciani P, Dule N, Perego C, Castagna M. Iron metabolism in pancreatic beta-cell function and dysfunction. Cells. 2021; 10:e2841. 21. DA SILVA, Emanuel Isaque Cordeiro. Nutrição e Suplementação Mineral de Bovinos de Corte. Emanuel Isaque Cordeiro da Silva, 2021. 22. Suganya A, Saravanan A, Manivannan N. Role of zinc nutrition for increasing zinc availability, uptake, yield, and quality of maize (Zea mays L.) grains: An overview. Commun Soil Sci Plant An. 2020; 51:2001-2021. 23. Byrne L, Murphy RA. Relative bioavailability of trace minerals in production animal nutrition: A review. Anim. 2022. 12:e1981. 24. Valentim JK, Mendes JP, Przybulinski BB, Serpa FC, Barbosa DK, Castilho VAR, et al. Fatores nutricionais aplicados à reprodução de ruminantes. Rev Uniciên. 2019; 23:77-82. 25. Martins KPF, Fonseca TRS, Silva ES, Munhoz TCP, Dias, GHS, Glauco JN, et al. Bócio em bovino. Pesq Vet Bras. 2018; 38:1030-1037. 26. González-Montaña JR, Escalera-Valente F, Alonso AJ, Lomillos JM, Robles R, Alonso ME. Relationship between vitamin B12 and cobalt metabolism in domestic ruminant: An update. Anim. 2020; 10:e1855; 27. Ventura RA, Cuzzuol GD, Silva TB, Malegoni ACS, Roque LZ, OLiveira SFC, et al. Deficiência de cobalto em bovinos: Revisão. Pubvet. 2021; 15:1-4. 28. Lopes HOS. Suplementação de Baixo Custo para Bovinos Mineral e Alimentar. 1st ed. Brasília, DF: Embrapa Cerrados; 1998. 29. Paul SH, Usman AA, Gana IN, Manase A, Adeniyi OD, Olutoye MA. Comparative study of mineral and nutritional composition of a multifunctional flora composite formulated from seven medicinal plants and their applications to human health. Eng Techn Open Access J. 2018; 1:137-150. 30. Zhou J, Zhang J, Xue B, Yue S, Yang C, Xue B. Effects of premating calcium and phosphorus supplementation on reproduction efficiency of grazing yak heifers. Anim. 2021; 11:1-9. 31. Harder H, Khol-Parisini A, Metzler-Zebeli BU, Klevenhusen F, Zebeli Q. Treatment of grain with organic acids at 2 different dietary phosphorus levels modulates ruminal microbial community structure and fermentation patterns in vitro. J Dairy Sci. 2015; 98:8107–8120. 32. Jain RK, Mudgal V. Phosphorus deficiency influences rumen microbial activity: Review. Int J Zoo Anim Biol. 2021; 4:1-9. 33. Barreto JC, Branco AF, Santos GT, Magalhães VJA, Coneglian SMC, Teixeira S. Avaliação dos efeitos de fontes de fósforo na dieta sobre parâmetros do meio ruminal e eficiência de síntese microbiana, digestibilidade dos nutrientes e fósforo plasmático em bovinos. Rev Bras Zootec. 2009; 38:760-769. 34. Yamada S, Inaba M. Potassium metabolism and management in patients with CKD. Nutr. 2021; 13:1-19. 35. Martinsen TC, Fossmark R, Waldum HL. The phylogeny and biological function of gastric juice—Microbiological consequences of removing gastric acid. Int J Mol Sci. 2019; 20:1-22. 36. González FHD, Silva SC. Minerais e vitaminas no metabolismo animal. 1st ed. Porto Alegre: Universidade Federal do Rio Grande do Sul; 2019. 37. Melo RR, Gonçalves SLS, Baccarin FJB, Vasconcelos WA, Miranda SC. Influência de altas dosagens de cloreto de potássio no desenvolvimento de espécies em cultivo hidropônico. Pubvet. 2020; 14:1-9. 38. Araújo WAG, Rostagno HS, Albino LFT, Carvalho TA, Ribeiro Neto AC. Potássio na nutrição animal. Rev Eletr Nutr. 2010; 7:1280-1291. 39. NRC. National Research Council. Nutrient requeriments of dairy cattle. 8th ed. Washinton, DC.: The National Academy Press; 2021. 40. Palomares RA. Trace Minerals Supplementation with Great Impact on Beef Cattle Immunity and Health. Anim. 2022; 12:e2839. 41. Tariq M, Abbas G, Ahmad S, Mustafa R, Sultan H, Abdullah M, Hussain I, Arshad M, Cheema UB, Hassan SU. Effect of supplementation of minerals on the productive and reproductive performance of lactating cattle and buffalo. Pakistan J Sci. 2022; 74:e425. 42. Pagno KCA Barbosa JD, Salvarani FM, Bomjardim HA, Faial KCF, Souza RS, Gava A, Perotta JH, Barros Filho IR. Micromineral concentrations (copper, cobalt, iron, molybdenum and zinc) in the liver of dairy cows from Campos Gerais Region, Paraná state, Brazil. Pesq Vet Bras. 2023; 43:e07137. 43. Jeong H, Vacanti NM. Systemic vitamin intake impacting tissue proteomes. Nut Met. 2020; 17:1-16. 44. Spears JW, Weiss WP. Invited Review: Mineral and vitamin nutrition in ruminants. Appl Anim Sci. 2014; 30:180-191. 45. Erickson PS, Kalscheur KF. Nutrition and feeding of dairy cattle. Chapter 9. (pp. 157-180). In: Bazer FW, Lamb GC, Wu G, editors. Animal Agriculture. Sustainability, Challenges and Innovations. 1st ed. Elsevier Inc.: Academic Press; 2020. 46. Kareem MM, Jabir MS. Vitamins. Chapter 3. In: Imran M, Imran A, editors. Malnutrition. 1st ed. London, UK.: IntechOpen; 2020. 47. Akram M, Munir N, Daniyal M, Egbuna C, Găman MA, Onyekere PF, et al. Vitamins and minerals: Types, sources and their functions. Chapter 9. (pp 149–172). In: Egbuna C, Tupas GD, editors. Functional foods and nutraceuticals. Switzerland, AG.: Springer, Cham; 2020. 48. Marino CT, Medeiros SR. Minerais e vitaminas na nutrição de bovinos de corte. Chapter 6. (pp. 79-93). In. Medeiros SR, Gomes RC, Bungenstab DJ, editors. Nutrição de bovinos de corte: fundamentos e aplicações. Brasília, DF: Embrapa; 2015. 49. Hodnik JJ, Ježek J, Starič J. A review of vitamin D and its importance to the health of dairy cattle. J Dairy Res. 2020; 87:84–87. 50. Alves EM, Pedreira MS, Santana CA, Silva FF. Nutrição vitamínica de vacas leiteiras. Pubvet. 2008; 2:1-17. 51. Jiang Q, Lin L, Xie F, Jin W, Zhu W, Wang M, et al. Metagenomic insights into the microbe-mediated B and K2 vitamin biosynthesis in the gastrointestinal microbiome of ruminants. Microb. 2022; 10:1-16. 52. Magan JB, O’Callaghan TF, Zheng J, Zhang L, Mandal R, Hennessy D, et al. Effect of diet on the vitamin B profile of bovine milk-based protein ingredients. Foods. 2020; 9:1-17. 53. Pinedo LA, Berenchtein B, Selem ASMA. Estudo dos processos bioquímicos da fermentação, degradação e absorção de nutrientes dos alimentos em ruminantes. Pubvet. 2008; 2:1-10. 54. DA SILVA, Emanuel Isaque Cordeiro. Formulação e Avaliação de Suplemento Mineral Para Caprinos. Instrução Técnica Para o Produtor Rural de Pernambuco, v. 3, n. 1, p. 1-4, 2023. 55. DA SILVA, Emanuel Isaque Cordeiro. Nutrição Sobre a Reprodução e Fertilidade dos Bovinos. Emanuel Isaque Cordeiro da Silva, 2021. 56. DA SILVA, Emanuel Isaque Cordeiro. Suplementação e formulação de sal mineral e mistura múltipla para bovinos. Revista Universitária Brasileira, v. 2, n. 1, 2024. (shrink)

La PAI es la “Asociación sobre inteligencia artificial en beneficio de las personas y la sociedad” (Partnership on AI to Benefit People and Society) y es una organización sin fines de lucro con sede en San Francisco, California, que reúne a organizaciones académicas, de la sociedad civil, a empresas tecnológicas y de los medios de comu- nicación para abordar cuestiones sustanciales, básicamente sobre el futuro de la IA, pero también otros importantes retos mundiales como el cambio climático, la alimentación, la (...) desigualdad, la salud y la educación. Tienen cinco programas que contribuyen al desarrollo de recursos, recomendaciones y mejores prácticas para la IA: inteligencia artificial e integridad mediática; IA, trabajo y economía; justicia, transparencia y responsabilidad en aprendizaje automático (ML); investigación y diseño inclusivos; y seguridad para la IA crítica. En este capítulo se destaca que, si bien PAI ha sido criticada por estar dominada en gran medida por grandes empresas tecnológicas, lo que podría limitar su autonomía y autocrítica, es una organización valiosa e influyente en el campo de la ética y la gobernanza de la IA, y desempeña un papel importante en la promoción del desarrollo y el despliegue responsables, no solo de la IA, sino de la tecnología y los medios de comunicación en general. (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging experts. Biomedical experts (...) and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...) posits that reconceptualizing replicability can help bridge the gap. Through a shift from model performance replicability to claim replicability, Machine Learning scientists can be held accountable for producing non-replicable claims that are prone to eliciting harm due to misuse and misinterpretation. In this paper, I make the following contributions. First, I define and distinguish two forms of replicability for ML research that can aid constructive conversations around replicability. Second, I formulate an argument for claim-replicability's advantage over model performance replicability in justifying assigning accountability to Machine Learning scientists for producing non-replicable claims and show how it enacts a sense of responsibility that is actionable. In addition, I characterize the implementation of claim replicability as more of a social project than a technical one by discussing its competing epistemological principles, practical implications on Circulating Reference, Interpretative Labor, and research communication. (shrink)

Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the non-negotiability (...) of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets. (shrink)

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...) gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus. (shrink)

We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our aim is, firstly, to target (...) a model-agnostic distillation approach exemplified with these two frameworks, secondly, to study how these two frameworks interact on a theoretical level, and, thirdly, to investigate use-cases in ML and AI in a comparative manner. Specifically, we envision that user-studies will help determine human understandability of explanations generated using these two frameworks. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target environments. Yet (...) the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

In Philosophy and Technology 36, David Watson discusses the epistemological and metaphysical implications of unsupervised machine learning (ML) algorithms. Watson is sympathetic to the epistemological comparison of unsupervised clustering, abstraction and generative algorithms to human cognition and sceptical about ML’s mechanisms having ontological implications. His epistemological commitments are that we learn to identify “natural kinds through clustering algorithms”, “essential properties via abstraction algorithms”, and “unrealized possibilities via generative models” “or something very much like them.” The same issue contains a commentary (...) on Watson’s paper in which Tom Sterkenburg fiercely opposes the epistemological claim that clustering algorithms can identify natural kinds. Sterkenburg argues there’s nothing about clustering arguments themselves that enables natural kind identification. What’s at stake in the Watson-Sterkenburg debate is whether it is possible for clustering algorithms to identify natural kinds and whether they underly human identification of natural kinds. My contribution to the Watson-Sterkenburg debate is twofold. First, I argue Sterkenburg’s criticism of Watson’s claim is too severe because it denies any clustering algorithm to identify any natural kind. Secondly, I argue Watson’s reply overestimates both clustering algorithms’ and humans’ access to natural kinds and his claim has to be restricted accordingly to ‘We identify kinds via clustering algorithms, or something very much like them’. (shrink)

Background: Increasing resistance of Pseudomonas aeruginosa to ciprofloxacin in ICU/burn units has created a problem in the treatment of infections caused by this microorganism. -/- Methods: Fifty P. aeruginosa strains were isolated from burn patients hospitalized in the Kerman Hospital during May 1999-April 2000 and were tested for in-vitro sensitivity to different antibiotics by disc diffusion breakpoint assay. The isolates were subjected to minimum inhibitory concentration (MIC) test by agar dilution method. Existence of the plasmids was also investigated in the (...) isolates. -/- Results: Thirty-four patients infected with ciprofloxacin strains showed MIC of 8 mg/ml [p<0.001]. Sixteen patients were infected with sensitive strains exhibiting MIC range of 0.0125-0.125 ± 0.033 mg/ml. The isolates were also also resistant to other antibiotics [p<0.001]. Plasmid isolation and agarose gel electrophoresis (0.7%) revealed three plasmid bands in strains 8 and 16, and one band in strain 35. -/- Conclusion: The emergence of ciprofloxacin resistance of P. aeruginosa in burn patients is alarming since this antibiotic has only recently been introduced onto the market in Iran. One important observation was that some isolates exhibited cross resistance to other antibiotics. Furthermore, some strains were carriers of plasmids which might have acted as the potential source of acquired resistance in the hospital setting. -/- . (shrink)

Kunstmatige intelligentie (AI) en systemen die met machine learning (ML) werken, kunnen veel onderdelen van het medische besluitvormingsproces ondersteunen of vervangen. Ook zouden ze artsen kunnen helpen bij het omgaan met klinische, morele dilemma’s. AI/ML-beslissingen kunnen zo in de plaats komen van professionele beslissingen. We betogen dat dit belangrijke gevolgen heeft voor de relatie tussen een patiënt en de medische professie als instelling, en dat dit onvermijdelijk zal leiden tot uitholling van het institutionele vertrouwen in de geneeskunde.

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)

Artificial Intelligence (AI) technologies are now widely used in a variety of fields to aid with knowledge acquisition and decision-making. Health information systems, in particular, can gain the most from AI advantages. Recently, symptoms-based illness prediction research and manufacturing have grown in popularity in the healthcare business. Several scholars and organisations have expressed an interest in applying contemporary computational tools to analyse and create novel approaches for rapidly and accurately predicting illnesses. In this study, we present a paradigm for assessing (...) the efficacy of combining Machine Learning (ML) and Natural Language Processing (NLP) technologies in a disease prediction system. We scraped a disease-symptom dataset with NLP characteristics from one of the UK's most trusted National Health Service (NHS) websites as an example. In addition, we will thoroughly examine our data using symptom frequency, similarity, and clustering analysis. As a consequence, we can observe that the forecast has a high efficiency rate, but there are still some challenges to work out. (shrink)

Mümkün varlıkları aracı kılmadan Vâcibü’l-Vücûd’un varlığını ispatlama çabalarının bir sonucu olan sıddıkîn burhanı ilk defa Müslüman filozoflar tarafından dillendirildi. İbn Sînâ (ö. 428/1037) da Fârâbî’nin etkisiyle yeni bir burhan açıkladı ve buna sıddıkîn adını verdi. Molla Sadrâ (ö. 1050/1641) varlığın asaleti ilkesini mutasavvıflardan, teşkîk ilkesini de Sühreverdî’den iktibas ederek yeni bir sıddıkîn burhanı dillendirdi. Bu burhanın, varlığın asaleti, basîtliği/yalınlığı, teşkîkî ve ma’lûlün illete ihtiyacı olmak üzere bazı öncülleri vardır. O, bu öncülleri açıkladıktan sonra teselsüle ihtiyaç duymadan Vâcibü’l-Vücûd’un varlığını ispatlar. Onun (...) burhanı diğer sıddıkîn burhanlarına nispetle bazı üstün özelliklere sahiptir. Öncelikle bu burhan varlığın asaleti ilkesi üzerine bina edildiğinden dayanağı varlık kavramı değil, varlığın nesnel hakikatidir. İkincisi bu burhan Vâcibü’l-Vücûd’un zâtını ispatlamakla birlikte Vâcibü’l-Vücûd’un birliğini ve kemâlî sıfatlarını ispatlar. Üçüncüsü bu burhana göre mümkün varlıklar hiçbir aracı olmadan Vâcibü’l-Vücûd’a bağımlı olduklarından burhanın açıklanmasında teselsül ve kısır döngüden yararlanma ihtiyacı yoktur. Molla Sadrâ’nın teselsül ve kısır döngüden de yararlanarak Vâcibü’l-Vücûd’un varlığını ispatladığı burhanlar da vardır. Molla Sadrâ bu burhanıyla hem Vâcibü’l-Vücûd’un varlığını ve hem de onun kemâlî sıfatlarını ispatladığını iddia eder. Bu çalışmada bu iddia incelecek ve ispat-ı vacip meselesinde Molla Sadrâ’nın ne gibi yenilikler getirdiği ele alınacaktır. ABSTRACT A result of the attempts to prove the existence of The Necessary Existence without the mediation of possible beings, proof of the truthful was first expressed by Muslim philosophers called it sıddıkin. Mullā Ṣadrā’s “proof” that he explains has certain premises such as the fundamental of existence, indivisibility of the existence, existential gradation, effect’s need for cause. After explanining these premises, Mullā Ṣadrā then proves the Necessary Existence without needing succession. Mullā Ṣadrā’s proof of the truthful has certain superior features tan the proofs of truthful expressed before him. First of all, as this proof is built upon the principle of the fundamental of existence, its foundation is the external truth of existence, not the concept of existence itself. Secondly, this proof proves the unity and perfection attributes of The Necessary Existence as well as proving the existence of the Necessary Existence. Thirdly, according to this proof, as all possible existences are dependent on The Necessary Existence without any mediation, there is no need to benefit from succession and vicious circle in explaining the proof. However, Mullā Ṣadrā proves the Necessary Existence by benefitting from succession and vicious circle in other proofs. Mullā Ṣadrā asserts that this proof demonstrates the presence of The Necessary Existence and its perfection attributions. This study examines this assertion and addresses what kind of innovations was brought by Mullā Ṣadrā in terms of proving The Necessary Existence. (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)