Unsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely studied and critically evaluated, often with an emphasis on ethical concerns. In this article, I analyze three canonical unsupervised learning problems: clustering, abstraction, and generative modeling. I argue that these methods raise unique epistemological and (...) ontological questions, providing data-driven tools for discovering natural kinds and distinguishing essence from contingency. This analysis goes some way toward filling the lacuna in contemporary philosophical discourse on unsupervised learning, as well as bringing conceptual unity to a heterogeneous field more often described by what it isnot(i.e., supervised or reinforcement learning) than by what itis. I submit that unsupervised learning is not just a legitimate subject of philosophical inquiry but perhaps the most fundamental branch of all AI. However, an uncritical overreliance on unsupervised methods poses major epistemic and ethical risks. I conclude by advocating for a pragmatic, error-statistical approach that embraces the opportunities and mitigates the challenges posed by this powerful class of algorithms. (shrink)

Internal misconduct is a universal problem in prisons and affects the maintenance of social order. Consequently, correctional institutions often develop rehabilitation programs to reduce the likelihood of inmates committing internal offenses and criminal recidivism after release. Therefore, it is necessary to identify the profile of each offender, both for the appropriate indication of a rehabilitation program and the level of internal security to which he must be submitted. In this context, this work aims to discover the most significant characteristics in (...) predicting inmate misconduct from ML methods and the SHAP approach. A database produced in 2004 through the Survey of Inmates in State and Federal Correctional Facilities in the United States of America was used, which provides nationally representative data on prisoners from state and federal facilities. The predictive model based on Random Forest performed the best, thus, we applied the SHAP to it. Overall, the results showed that features related to victimization, type of crime committed, age and age at first arrest, history of association with criminal groups, education, and drug and alcohol use are most relevant in predicting internal misconduct. Thus, it is expected to contribute to the prior classification of an inmate on time, to use programs and practices that aim to improve the lives of offenders, their reintegration into society, and consequently, the reduction of criminal recidivism. (shrink)

This article explores the trend of increasing automation in law enforcement and criminal justice settings through three use cases: predictive policing, machine evidence and recidivism algorithms. The focus lies on artificial-intelligence-driven tools and technologies employed, whether at pre-investigation stages or within criminal proceedings, in order to decode human behaviour and facilitate decision-making as to whom to investigate, arrest, prosecute, and eventually punish. In this context, this article first underlines the existence of a persistent dilemma between the goal of increasing the (...) operational efficiency of police and judicial authorities and that of safeguarding fundamental rights of the affected individuals. Subsequently, it shifts the focus onto key principles of criminal procedure and the presumption of innocence in particular. Using Article 6 ECHR and the Directive (EU) 2016/343 as a starting point, it discusses challenges relating to the protective scope of presumption of innocence, the burden of proof rule and the _in dubio pro reo_ principle as core elements of it. Given the transformations law enforcement and criminal proceedings go through in the era of algorithms, big data and artificial intelligence, this article advocates the adoption of specific procedural safeguards that will uphold rule of law requirements, and particularly transparency, fairness and explainability. In doing so, it also takes into account EU legislative initiatives, including the reform of the EU data protection _acquis_, the E-evidence Proposal, and the Proposal for an EU AI Act. Additionally, it argues in favour of revisiting the protective scope of key fundamental rights, considering, _inter alia_, the new dimensions suspicion has acquired. (shrink)

Recent works in Explainable AI mostly address the transparency issue of black-box models or create explanations for any kind of models (i.e., they are model-agnostic), while leaving explanations of interpretable models largely underexplored. In this paper, we fill this gap by focusing on explanations for a specific interpretable model, namely pattern-based logistic regression (PLR) for binary text classification. We do so because, albeit interpretable, PLR is challenging when it comes to explanations. In particular, we found that a standard way to (...) extract explanations from this model does not consider relations among the features, making the explanations hardly plausible to humans. Hence, we propose AXPLR, a novel explanation method using (forms of) computational argumentation to generate explanations (for outputs computed by PLR) which unearth model agreements and disagreements among the features. Specifically, we use computational argumentation as follows: we see features (patterns) in PLR as arguments in a form of quantified bipolar argumentation frameworks (QBAFs) and extract attacks and supports between arguments based on specificity of the arguments; we understand logistic regression as a gradual semantics for these QBAFs, used to determine the arguments’ dialectic strength; and we study standard properties of gradual semantics for QBAFs in the context of our argumentative re-interpretation of PLR, sanctioning its suitability for explanatory purposes. We then show how to extract intuitive explanations (for outputs computed by PLR) from the constructed QBAFs. Finally, we conduct an empirical evaluation and two experiments in the context of human-AI collaboration to demonstrate the advantages of our resulting AXPLR method. (shrink)

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, going (...) to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing (...) that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)

A large part of the explainable AI literature focuses on what explanations are in general, what algorithmic explainability is more specifically, and how to code these principles of explainability into AI systems. Much less attention has been devoted to the question of why algorithmic decisions and systems should be explainable and whether there ought to be a right to explanation and why. We therefore explore the normative landscape of the need for AI to be explainable and individuals having a right (...) to such explanation. This exploration is particularly relevant to the medical domain where the (im)possibility of explainable AI is high on both the research and practitioners’ agenda. The dominant intuition overall is that explainability has and should play a key role in the health context. Notwithstanding the strong normative intuition for having a right to explanation, intuitions can be wrong. So, we need more than an appeal to intuitions when it comes to explaining the normative significance of having a right to explanation when being subject to AI-based decision-making. The aim of the paper is therefore to provide an account of what might underlie the normative intuition. We defend the ‘symmetry thesis’ according to which there is no special normative reason to have a right to explanation when ‘machines’ in the broad sense, make decisions, recommend treatment, discover tumors, and so on. Instead, we argue that we have a right to explanation in cases that involve automated processing that significantly affect our core deliberative agency and which we do not understand, because we have a general moral right to explanation when choices are made which significantly affect us but which we do not understand. (shrink)

Public entities around the world are increasingly deploying artificial intelligence and algorithmic decision-making systems to provide public services or to use their enforcement powers. The rationale for the public sector to use these systems is similar to private sector: increase efficiency and speed of transactions and lower the costs. However, public entities are first and foremost established to meet the needs of the members of society and protect the safety, fundamental rights, and wellbeing of those they serve. Currently AI systems (...) are deployed by the public sector at various administrative levels without robust due diligence, monitoring, or transparency. This paper critically maps out the challenges in procurement of AI systems by public entities and the long-term implications necessitating AI-specific procurement guidelines and processes. This dual-prong exploration includes the new complexities and risks introduced by AI systems, and the institutional capabilities impacting the decision-making process. AI-specific public procurement guidelines are urgently needed to protect fundamental rights and due process. (shrink)

The food security of more than half of the world’s population depends on rice production which is one of the key objectives of precision agriculture. The traditional rice almanac used astronomical and climate factors to estimate yield response. However, this research integrated meteorological, agro-chemical, and soil physiographic factors for yield response prediction. Besides, the impact of those factors on the production of three major rice ecotypes has also been studied in this research. Moreover, this study found a different set of (...) those factors with respect to the yield response of different rice ecotypes. Machine learning algorithms named Extreme Gradient Boosting and Support Vector Regression have been used for predicting the yield response. The SVR shows better results than XGBoost for predicting the yield of the Aus rice ecotype, whereas XGBoost performs better for forecasting the yield of the Aman and Boro rice ecotypes. The result shows that the root mean squared error of three different ecotypes are in between 9.38% and 24.37% and that of R-squared values are between 89.74% and 99.13% on two different machine learning algorithms. Moreover, the explainability of the models is also shown in this study with the help of the explainable artificial intelligence model called Local Interpretable Model-Agnostic Explanations. (shrink)

Explainable artificial intelligence (XAI) aims to help people understand black box algorithms, particularly of their outputs. But what are these explanations and when is one explanation better than another? The manipulationist definition of explanation from the philosophy of science offers good answers to these questions, holding that an explanation consists of a generalization that shows what happens in counterfactual cases. Furthermore, when it comes to explanatory depth this account holds that a generalization that has more abstract variables, is broader in (...) scope and/or more accurate is better. By applying these definitions and contrasting them with alternative definitions in the XAI literature I hope to help clarify what a good explanation is for AI. (shrink)

Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this (...) paper focuses on non-expert users’ perspectives, since users with little technical knowledge are likely to benefit the most from “post-hoc”, everyday explanations. Drawing upon the explainable AI and social sciences literature, this paper investigates how artificial agent’s explainability and trust are interrelated at different stages of an interaction. Specifically, the possibility of implementing explainability as a trust building, trust maintenance and restoration strategy is investigated. To this extent, the paper identifies and discusses the intrinsic limits and fundamental features of explanations, such as structural qualities and communication strategies. Accordingly, this paper contributes to the debate by providing recommendations on how to maximize the effectiveness of explanations for supporting non-expert users’ understanding and trust. (shrink)

Assistive systems based on Artificial Intelligence (AI) are bound to reshape decision-making in all areas of society. One of the most intricate challenges arising from their implementation in high-stakes environments such as medicine concerns their frequently unsatisfying levels of explainability, especially in the guise of the so-called black-box problem: highly successful models based on deep learning seem to be inherently opaque, resisting comprehensive explanations. This may explain why some scholars claim that research should focus on rendering AI systems understandable, rather (...) than explainable. Yet, there is a grave lack of agreement concerning these terms in much of the literature on AI. We argue that the seminal distinction made by the philosopher and physician Karl Jaspers between different types of explaining and understanding in psychopathology can be used to promote greater conceptual clarity in the context of Machine Learning (ML). Following Jaspers, we claim that explaining and understanding constitute multi-faceted epistemic approaches that should not be seen as mutually exclusive, but rather as complementary ones as in and of themselves they are necessarily limited. Drawing on the famous example of Watson for Oncology we highlight how Jaspers’ methodology translates to the case of medical AI. Classical considerations from the philosophy of psychiatry can therefore inform a debate at the centre of current AI ethics, which in turn may be crucial for a successful implementation of ethically and legally sound AI in medicine. (shrink)

As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their (...) creators beyond those commonly discussed (e.g., trolley problems and variants of it) and to which solutions cannot be hard-coded and are often still up for debate. Given the sensitivity of such social and ethical dilemmas and the implications of these for human society at large, when and if our AI make the “wrong” choice we need to understand how they got there in order to make corrections and prevent recurrences. This is particularly true in situations where human livelihoods are at stake (e.g., health, well-being, finance, law) or when major individual or household decisions are taken. Doing so requires opening up the “black box” of AI; especially as they act, interact, and adapt in a human world and how they interact with other AI in this world. In this article, we argue for the application of cognitive architectures for ethical AI. In particular, for their potential contributions to AI transparency, explainability, and accountability. We need to understand how our AI get to the solutions they do, and we should seek to do this on a deeper level in terms of the machine-equivalents of motivations, attitudes, values, and so on. The path to future AI is long and winding but it could arrive faster than we think. In order to harness the positive potential outcomes of AI for humans and society (and avoid the negatives), we need to understand AI more fully in the first place and we expect this will simultaneously contribute towards greater understanding of their human counterparts also. (shrink)

This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to (...) the SDGs. Second, as a specific instantiation of the SDGIE framework, the SDG Impact Intensity Model (SDGIIM) is theoretically and operationally established. SDGIIM embeds expert decision-making and SDG keyword banks in textual data processing to determine overall SDG “impact intensity.” Ideally, SDGIIM can be applied to textual data sets from any sector or discipline: academia, business, government, non-profit, civil society, etc. Third, the SDGIIM instantiation is applied to the specific domain of academic journal rating systems as a case study. Traditionally, academic journals have been evaluated on loosely conceived and empirically shaky notions of ‘quality.’ Aligned with the trend of AI4SG and broader calls to action, ‘impact’ is rapidly becoming the primary normative consideration for assessing academic journals. We hypothesize and demonstrate that SDGIIM is capable of producing evaluations aligned with experts’ expectations of SDG impact intensity; the consistent analysis and rating of textual data sets that embody the SDGs with varying degrees of meaning and, ultimately, promote positive impact on the actual material conditions of the world. (shrink)

Under the rubric of understanding the problem of explainability of AI in terms of abductive cognition, I propose to review the lessons from AlphaGo and her more powerful successors. As AI players in Baduk have arrived at superhuman level, there seems to be no hope for understanding the secret of their breathtakingly brilliant moves. Without making AI players explainable in some ways, both human and AI players would be less-than omniscient, if not ignorant, epistemic agents. Are we bound to have (...) less explainable AI Baduk players as they make further progress? I shall show that the resolution of this apparent paradox depends on how we understand the crucial distinction between abduction and inference to the best explanation. Some further philosophical issues arising from explainable AI will also be discussed in connection with this distinction. (shrink)

The General Data Protection Regulation (GDPR) of the EU confirms the protection of personal data as a fundamental human right and affords data subjects more control over the way their personal information is processed, shared, and analyzed. However, where data are processed by artificial intelligence (AI) algorithms, asserting control and providing adequate explanations is a challenge. Due to massive increases in computing power and big data processing, modern AI algorithms are too complex and opaque to be understood by most data (...) subjects. Articles 15 and 22 of the GDPR provide a modest regulatory framework for automated data processing by, among other things, mandating that data controllers inform data subjects about when it is being used, and its logic and ramifications. Nevertheless, due to the phrasing of the articles and the numerous exceptions they allow, doubts have arisen about their effectiveness. In this paper, we empirically evaluate the quality and effectiveness of AI disclosures as mandated by the GDPR. By means of an online survey (N = 835), we investigated how data subjects expect to be informed about the automated processing of their data. We then conducted a content analysis of the AI disclosures of N = 100 companies and organizations. The combined findings reveal that current GDPR-mandated disclosures do not meet the expectations and needs of data subjects. Explanations drawn up following the guidelines of the generic formulations of the GDPR differ widely and are often vague, incomplete and lack transparency. In our conclusions we identify a path towards standardizing and optimizing AI information notices. (shrink)

Philosophy Compass, Volume 17, Issue 6, June 2022.

Social relationships are constructed by and through the relational communication that people exchange. Relational messages are implicit nonverbal and verbal messages that signal how people regard one another and define their interpersonal relationships—equal or unequal, affectionate or hostile, inclusive or exclusive, similar or dissimilar, and so forth. Such signals can be measured automatically by the latest machine learning software tools and combined into meaningful factors that represent the socioemotional expressions that constitute relational messages between people. Relational messages operate continuously on (...) a parallel track with verbal communication, implicitly telling interactants the current state of their relationship and how to interpret the verbal messages being exchanged. We report an investigation that explored how group members signal these implicit messages through multimodal behaviors measured by sensor data and linked to the socioemotional cognitions interpreted as relational messages. By use of a modified Brunswikian lens model, we predicted perceived relational messages of dominance, affection, involvement, composure, similarity and trust from automatically measured kinesic, vocalic and linguistic indicators. The relational messages in turn predicted the veracity of group members. The Brunswikian Lens Model offers a way to connect objective behaviors exhibited by social actors to the emotions and cognitions being perceived by other interactants and linking those perceptions to social outcomes. This method can be used to ascertain what behaviors and/or perceptions are associated with judgments of an actor’s veracity. Computerized measurements of behaviors and perceptions can replace manual measurements, significantly expediting analysis and drilling down to micro-level measurement in a previously unavailable manner. (shrink)

The human-centered AI approach posits a future in which the work done by humans and machines will become ever more interactive and integrated. This article takes human-centered AI one step further. It argues that the integration of human and machine intelligence is achievable only if human organizations—not just individual human workers—are kept “in the loop.” We support this argument with evidence of two case studies in the area of predictive maintenance, by which we show how organizational practices are needed and (...) shape the use of AI/ML. Specifically, organizational processes and outputs such as decision-making workflows, etc. directly influence how AI/ML affects the workplace, and they are crucial for answering our first and second research questions, which address the pre-conditions for keeping humans in the loop and for supporting continuous and reliable functioning of AI-based socio-technical processes. From the empirical cases, we extrapolate a concept of “keeping the organization in the loop” that integrates four different kinds of loops: AI use, AI customization, AI-supported original tasks, and taking contextual changes into account. The analysis culminates in a systematic framework of keeping the organization in the loop look based on interacting organizational practices. (shrink)

The operations of deep networks are widely acknowledged to be inscrutable. The growing field of Explainable AI has emerged in direct response to this problem. However, owing to the nature of the opacity in question, XAI has been forced to prioritise interpretability at the expense of completeness, and even realism, so that its explanations are frequently interpretable without being underpinned by more comprehensive explanations faithful to the way a network computes its predictions. While this has been taken to be a (...) shortcoming of the field of XAI, I argue that it is broadly the right approach to the problem. (shrink)

The same method that creates adversarial examples to fool image-classifiers can be used to generate counterfactual explanations that explain algorithmic decisions. This observation has led researchers to consider CEs as AEs by another name. We argue that the relationship to the true label and the tolerance with respect to proximity are two properties that formally distinguish CEs and AEs. Based on these arguments, we introduce CEs, AEs, and related concepts mathematically in a common framework. Furthermore, we show connections between current (...) methods for generating CEs and AEs, and estimate that the fields will merge more and more as the number of common use-cases grows. (shrink)

In the last decades, supervised machine learning has seen the widespread growth of highly complex, non-interpretable models, of which deep neural networks are the most typical representative. Due to their complexity, these models have showed an outstanding performance in a series of tasks, as in image recognition and machine translation. Recently, though, there has been an important discussion over whether those non-interpretable models are able to provide any sort of understanding whatsoever. For some scholars, only interpretable models can provide understanding. (...) More popular, however, is the idea that understanding can come from a careful analysis of the dataset or from the model’s theoretical basis. In this paper, I wish to examine the possible forms of obtaining understanding of such non-interpretable models. Two main strategies for providing understanding are analyzed. The first involves understanding without interpretability, either through external evidence for the model’s inner functioning or through analyzing the data. The second is based on the artificial production of interpretable structures, through three main forms: post hoc models, hybrid models, and quasi-interpretable structures. Finally, I consider some of the conceptual difficulties in the attempt to create explanations for these models, and their implications for understanding. (shrink)

Previous experimental findings support the hypothesis that laughter and positive emotions are contagious in face-to-face and mediated communication. To test this hypothesis, we describe four experiments in which participants communicate via a chat tool that artificially adds or removes laughter, without participants being aware of the manipulation. We found no evidence to support the contagion hypothesis. However, artificially exposing participants to more lols decreased participants’ use of hahas but led to more involvement and improved task-performance. Similarly, artificially exposing participants to (...) more hahas decreased use of haha but increased lexical alignment. We conclude that, even though the interventions have effects on coordination, they are incompatible with contagion as a primary explanatory mechanism. Instead, these results point to an interpretation that involves a more sophisticated view of dialogue mechanisms along the lines of Conversational Analysis and similar frameworks and we suggest directions for future research. (shrink)

The problem of epistemic opacity in Artificial Intelligence is often characterised as a problem of intransparent algorithms that give rise to intransparent models. However, the degrees of transparency of an AI model should not be taken as an absolute measure of the properties of its algorithms but of the model’s degree of intelligibility to human users. Its epistemically relevant elements are to be specified on various levels above and beyond the computational one. In order to elucidate this claim, I first (...) contrast computer models and their claims to algorithm-based universality with cybernetics-style analogue models and their claims to structural isomorphism between elements of model and target system. While analogue models aim at perceptually or conceptually accessible model-target relations, computer models give rise to a specific kind of underdetermination in these relations that needs to be addressed in specific ways. I then undertake a comparison between two contemporary AI approaches that, although related, distinctly align with the above modelling paradigms and represent distinct strategies towards model intelligibility: Deep Neural Networks and Predictive Processing. I conclude that their respective degrees of epistemic transparency primarily depend on the underlying purposes of modelling, not on their computational properties. (shrink)

In light of the widespread use of algorithmic (intelligent) systems across numerous domains, there is an increasing awareness about the need to explain their underlying decision-making process and resulting outcomes. Since oftentimes these systems are being considered as black boxes, adding explanations to their outcomes may contribute to the perception of their transparency and, as a result, increase users’ trust and fairness perception towards the system, regardless of its actual fairness, which can be measured using various fairness tests and measurements. (...) Different explanation styles may have a different impact on users’ perception of fairness towards the system and on their understanding of the outcome of the system. Hence, there is a need to understand how various explanation styles may impact non-expert users’ perceptions of fairness and understanding of the system’s outcome. In this study we aimed at fulfilling this need. We performed a between-subject user study in order to examine the effect of various explanation styles on users’ fairness perception and understanding of the outcome. In the experiment we examined four known styles of textual explanations (case-based, demographic-based, input influence-based and sensitivity-based) along with a new style (certification-based) that reflect the results of an auditing process of the system. The results suggest that providing some kind of explanation contributes to users’ understanding of the outcome and that some explanation styles are more beneficial than others. Moreover, while explanations provided by the system are important and can indeed enhance users’ perception of fairness, their perception mainly depends on the outcome of the system. The results may shed light on one of the main problems in explainability of algorithmic systems, which is choosing the best explanation to promote users’ fairness perception towards a particular system, with respect to the outcome of the system. The contribution of this study is reflected in the new and realistic case study that was examined, in the creation and evaluation of a new explanation style that can be used as the link between the actual (computational) fairness of the system and users’ fairness perception and in the need of analyzing and evaluating explanations while taking into account the outcome of the system. (shrink)

The emergence of ethical concerns surrounding artificial intelligence (AI) has led to an explosion of high-level ethical principles being published by a wide range of public and private organizations. However, there is a need to consider how AI developers can be practically assisted to anticipate, identify and address ethical issues regarding AI technologies. This is particularly important in the development of AI intended for healthcare settings, where applications will often interact directly with patients in various states of vulnerability. In this (...) paper, we propose that an ‘embedded ethics’ approach, in which ethicists and developers together address ethical issues via an iterative and continuous process from the outset of development, could be an effective means of integrating robust ethical considerations into the practical development of medical AI. (shrink)

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well (...) as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. (shrink)

In this paper, we point out that explainability is useful but not sufficient to ensure the legitimacy of algorithmic decision systems. We argue that the key requirements for high-stakes decision systems should be justifiability and contestability. We highlight the conceptual differences between explanations and justifications, provide dual definitions of justifications and contestations, and suggest different ways to operationalize justifiability and contestability.

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the (...) approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard societal (...) values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...) computational processes are indeed methodologically opaque to humans, we argue that the reliability of algorithms provides reasons for trusting the outcomes of medical artificial intelligence. To this end, we explain how computational reliabilism, which does not require transparency and supports the reliability of algorithms, justifies the belief that results of medical AI are to be trusted. We also argue that several ethical concerns remain with black box algorithms, even when the results are trustworthy. Having justified knowledge from reliable indicators is, therefore, necessary but not sufficient for normatively justifying physicians to act. This means that deliberation about the results of reliable algorithms is required to find out what is a desirable action. Thus understood, we argue that such challenges should not dismiss the use of black box algorithms altogether but should inform the way in which these algorithms are designed and implemented. When physicians are trained to acquire the necessary skills and expertise, and collaborate with medical informatics and data scientists, black box algorithms can contribute to improving medical care. (shrink)

Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these “stakeholders' desiderata”) in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability (...) of artificial systems and reviews their desiderata. We provide a model that explicitly spells out the main concepts and relations necessary to consider and investigate when evaluating, adjusting, choosing, and developing explainability approaches that aim to satisfy stakeholders' desiderata. This model can serve researchers from the variety of different disciplines involved in XAI as a common ground. It emphasizes where there is interdisciplinary potential in the evaluation and the development of explainability approaches. (shrink)

Amid public health concerns over climate change, “precision public health” is emerging in next generation approaches to practice. These novel methods promise to augment public health operations by using ever larger and more robust health datasets combined with new tools for collecting and analyzing data. Precision strategies to protecting the public health could more effectively or efficiently address the systemic threats of climate change, but may also propagate or exacerbate health disparities for the populations most vulnerable in a changing climate. (...) How PPH interventions collect and aggregate data, decide what to measure, and analyze data pose potential issues around privacy, neglecting social determinants of health, and introducing algorithmic bias into climate responses. Adopting a health justice framework, guided by broader social and climate justice tenets, can reveal principles and policy actions which may guide more responsible implementation of PPH in climate responses. (shrink)

In this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making environments. With the rise of artificial intelligence and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different (...) promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought. (shrink)

This paper focuses on the use of ‘black box’ AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI’s implicit assumptions and an individual patient’s background situation.Pacecurrent clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient’s informed consent or violates a more general obligation (...) to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the ‘nature’ and ‘likelihood’ of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasises their significance. (shrink)

As the average age of the population increases, so too do the number of people living with chronic illnesses. With limited resources available, the development of dialogue-based e-health systems that provide justified general health advice offers a cost-effective solution to the management of chronic conditions. It is however imperative that such systems are responsible in their approach. We present in this paper two main challenges for the deployment of e-health systems, that have a particular relevance to dialogue and argumentation: collecting (...) and handling health data, and trust. For both challenges, we look at specific issues therein, outlining their importance in general, and describing their relevance to dialogue and argumentation. Finally, we go on to propose six recommendations for handling these issues, towards addressing the main challenges themselves, that act both as general advice for dialogue and argumentation research in the e-health domain, and as a foundation for future work on this topic. (shrink)

This is a report on the Doctoral Consortium co-located with the 17th International Conference on Artificial Intelligence and Law in Montreal.

This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws (...) attention to the issues of transparency and explainability. In contrast to standard discussions, however, it is then argued that this knowledge problem regarding agents of responsibility is linked to the other side of the responsibility relation: the addressees or “patients” of responsibility, who may demand reasons for actions and decisions made by using AI. Inspired by a relational approach, responsibility as answerability thus offers an important additional, if not primary, justification for explainability based, not on agency, but on patiency. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)