Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these (...) principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...) positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...) resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems. (shrink)

-/- Artificial intelligence powers Google’s search engine, enables Facebook to target advertising, and allows Alexa and Siri to do their jobs. AI is also behind self-driving cars, predictive policing, and autonomous weapons that can kill without human intervention. These and other AI applications raise complex ethical issues that are the subject of ongoing debate. This volume in the MIT Press Essential Knowledge series offers an accessible synthesis of these issues. Written by a philosopher of technology, AI Ethics goes beyond the (...) usual hype and nightmare scenarios to address concrete questions. -/- Mark Coeckelbergh describes influential AI narratives, ranging from Frankenstein’s monster to transhumanism and the technological singularity. He surveys relevant philosophical discussions: questions about the fundamental differences between humans and machines and debates over the moral status of AI. He explains the technology of AI, describing different approaches and focusing on machine learning and data science. He offers an overview of important ethical issues, including privacy concerns, responsibility and the delegation of decision making, transparency, and bias as it arises at all stages of data science processes. He also considers the future of work in an AI economy. Finally, he analyzes a range of policy proposals and discusses challenges for policymakers. He argues for ethical practices that embed values in design, translate democratic values into practices and include a vision of the good life and the good society . (shrink)

The idea of artificial intelligence for social good is gaining traction within information societies in general and the AI community in particular. It has the potential to tackle social problems through the development of AI-based solutions. Yet, to date, there is only limited understanding of what makes AI socially good in theory, what counts as AI4SG in practice, and how to reproduce its initial successes in terms of policies. This article addresses this gap by identifying seven ethical factors that are (...) essential for future AI4SG initiatives. The analysis is supported by 27 case examples of AI4SG projects. Some of these factors are almost entirely novel to AI, while the significance of other factors is heightened by the use of AI. From each of these factors, corresponding best practices are formulated which, subject to context and balance, may serve as preliminary guidelines to ensure that well-designed AI is more likely to serve the social good. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

The rise of AI-based systems has been accompanied by the belief that these systems are impartial and do not suffer from the biases that humans and older technologies express. It becomes evident, however, that gender and racial biases exist in some AI algorithms. The question is where the bias is rooted—in the training dataset or in the algorithm? Is it a linguistic issue or a broader sociological current? Works in feminist philosophy of technology and behavioral economics reveal the gender bias (...) in AI technologies as a multi-faceted phenomenon, and the linguistic explanation as too narrow. The next step moves from the linguistic aspects to the relational ones, with postphenomenology. One of the analytical tools of this theory is the “I-technology-world” formula that models our relations with technologies, and through them—with the world. Realizing that AI technologies give rise to new types of relations in which the technology has an “enhanced technological intentionality”, a new formula is suggested: “I-algorithm-dataset.” In the third part of the article, four types of solutions to the gender bias in AI are reviewed: ignoring any reference to gender, revealing the considerations that led the algorithm to decide, designing algorithms that are not biased, or lastly, involving humans in the process. In order to avoid gender bias, we can recall a feminist basic understanding—visibility matters. Users and developers should be aware of the possibility of gender and racial biases, and try to avoid them, bypass them, or exterminates them altogether. (shrink)

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other (...) stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. (shrink)

Racism, sexism, and other forms of injustice are more than just bad attitudes; after all, such injustice involves unfair distributions of goods and resources. But attitudes play a role. How central is that role? Tommie Shelby, among others, argues that racism is an ideology and takes a cognitivist approach suggesting that ideologies consist in false beliefs that arise out of and serve pernicious social conditions. In this paper I argue that racism is better understood as a set of practices, attitudes, (...) social meanings, and material conditions, that systematically reinforce one another. Attitudes play a role, but even the cognitive/affective component of ideologies should include culturally shared habits of mind and action. These habits of mind distort, obscure, and occlude important facts about subordinated groups and result in a failure to recognize their interests. How do we disrupt such practices to achieve greater justice? I argue that this is sometimes, but not always, best achieved by argument or challenging false beliefs, so social movements legitimately seek other means. (shrink)

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

New technologies from artificial intelligence to drones, and biomedical enhancement make the future of the human family increasingly hard to predict and protect. This book explores how the philosophical tradition of virtue ethics can help us to cultivate the moral wisdom we need to live wisely and well with emerging technologies.

Argues that the War on Drugs and policies that deny convicted felons equal access to employment, housing, education and public benefits create a permanent under-caste based largely on race. Reprint. 12,500 first printing.

I argue in this paper that habits of moral attention, such as those that sustain racism and xenophobia, should be understood as attempts to deflect responsibility as Levinas describes it. The provocation to responsibility is fragile in the face of these moral habits, which separate the morally considerable from the morally inconsiderable. But in its traumatic quality, responsibility cannot be deflected entirely—it impacts the self prior to and outside of our attempts to manage our obligations. Levinas’s description of the interaction (...) between the conatus and responsibility should thus be read as a supplement to critical race theory, as an account that recognizes the power of moral habits but also the constant possibility of their interruption. (shrink)

Many ethics initiatives have stipulated sets of principles and standards for good technology development in the AI sector. However, several AI ethics researchers have pointed out a lack of practical realization of these principles. Following that, AI ethics underwent a practical turn, but without deviating from the principled approach. This paper proposes a complementary to the principled approach that is based on virtue ethics. It defines four “basic AI virtues”, namely justice, honesty, responsibility and care, all of which represent specific (...) motivational settings that constitute the very precondition for ethical decision making in the AI field. Moreover, it defines two “second-order AI virtues”, prudence and fortitude, that bolster achieving the basic virtues by helping with overcoming bounded ethicality or hidden psychological forces that can impair ethical decision making and that are hitherto disregarded in AI ethics. Lastly, the paper describes measures for successfully cultivating the mentioned virtues in organizations dealing with AI research and development. (shrink)

This commentary argues that, in discussing the racial and cultural identities of cinematic representations of humanoid AI robots, nuances and differentiations are beneficial. It suggests that the essay on which the present text comments does not sufficiently acknowledge the range of identities found in AI films, in particular in Alex Garland's Ex Machina.

Like many fields, bioethics has been constrained to thinking to race in terms of colorblindness, the idea that ideal deliberation would ignore race and hence prevent bias. There are practical and ethically significant problems with colorblind approaches to ethical deliberation, and important reasons why race is ethically relevant. Future discourse needs to understand how and why race is relevant in bioethics.

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms (...) create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use. (shrink)

Like many fields, bioethics has been constrained to thinking to race in terms of colorblindness, the idea that ideal deliberation would ignore race and hence prevent bias. There are practical and e...

Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy in (...) underwriting are different from those of using predictive algorithms in other sectors. Here we focus on the trade-off in the extent to which one can pursue indirect non-discrimination versus predictive accuracy. The moral assessment of this trade-off is related to the context of application—to the consequences of inaccurate risk predictions in the insurance domain. (shrink)

Overt racism and discrimination have been on the decline in the United States for at least two generations. Yet many American institutions continue to produce racial disparities. Sociologists and social critics have predominantly explained continuing disparities as results of continuing racism and discrimination, albeit in increasingly covert, anonymous forms; these critics suggest racism and discrimination have to be understood as historical, systemic problems operating at the level of institutions, culture, and society, even if overt forms are now rare. With increasing (...) reliance upon a proliferation of notions including “institutional racism,” “institutionalized discrimination,” and “glass ceilings,” however, scholars and critics alike have grown increasingly dependent upon statistical data on inequalities and institutional outcomes as grounds for theoretical and political inferences concerning collective motives or prejudices. In this crucial respect, insights from beyond studies of race and inequality, drawing especially on Wittgensteinian and Schutzian contributions to social thought, stand to illuminate the pragmatic, moral reasoning at work in the institutional racism argument and similar approaches. Such reflexive attention to a central conceptual resource of contemporary social criticism stands to bring attention back to the basic empirical and critical questions of how to study and engage with continuing inequalities in the post-civil rights era. These questions can certainly be addressed through theoretical stipulation and political claims-making, but a more viable conceptual and empiricalfoundation for both theory and criticism can be gained by attending more respectfully to foundational issues of meaning and interpretation in the human sciences and human relations. (shrink)

Slavery and slave trade gave birth to racism and society has been struggling towards its prevention and possible elimination with little success. Martin Luther King Jr wrote in his letter from the Birmingham jail: “Injustice anywhere is a threat to justice everywhere. We are caught in an inescapable network of mutuality, tied in a single garment of destiny.” Until this undeniable fact is understood and emphasized our contemporary society is heading towards a state of an uncontrollable wildfire of anarchy. It (...) is obvious that all fingers are not equal but that does not negate the fact that all men irrespective of colour or race are created equal—configured with brain, flesh, water, and blood. Racial discrimination is a moral and systemic sin that must be confronted and vehemently condemned. The main thrust of this paper is to expose various forms of racial discrimination ravaging the contemporary society with a view to postulating ideas geared towards its prevention and possible elimination. Relying on observational and historical methods, relevant data required will be elicited. The paper identified among other things that racism is resurging in the 21st century to a threatening dimension that if a coordinated action is not urgently taken, it will result into an uncontrollable wildfire of anarchy. The researcher therefore recommends the need to reemphasize respect and tolerance for all humanity. (shrink)

This article discusses two central methodological postulates (adequacy and subjective meaning) pertaining to the social sciences brought forward by Alfred Schütz, and as presented by Lester Embree’s ‘Economics in the Context of Alfred Schütz’s Theory of Science’. The relationship between the postulates and the actual practice of economics is discussed. The author shows how Schütz’s writings describe a spectrum of methods that ranges from low abstraction and an attempt to understand individual plans and purposes on the one hand to highly (...) abstract and aggregate modeling on the other. It is argued that the distribution of economic contributions is heavily skewed toward the latter. The second part of the article presents recent work by economists who have resisted this trend, and who have begun to expand our understanding of economic processes by taking seriously the notion of economics as a social science. (shrink)