The ongoing debate on the ethics of self-driving cars typically focuses on two approaches to answering ethical questions: moral philosophy and social science. I argue that these two approaches are both lacking. We should neither deduce answers from individual moral theories nor should we expect social science to give us complete answers. To supplement these approaches, we should turn to political philosophy. The issues we face are collective decisions that we make together rather than individual decisions we make in light of what we each have reason to value. Political philosophy adds three basic concerns to our conceptual toolkit: reasonable pluralism, human agency, and legitimacy. These three concerns have so far been largely overlooked in the debate on the ethics of self-driving cars.
What does it mean for a machine learning model to be `fair', in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematic…
Recent interest in codifying fairness in Automated Decision Systems (ADS) has resulted in a wide range of formulations of what it means for an algorithmic system to be fair. Most of these propositions are inspired by, but inadequately grounded in, political philosophy scholarship. This paper aims to correct that deficit. We introduce a taxonomy of fairness ideals using doctrines of Equality of Opportunity (EOP) from political philosophy, clarifying their conceptions in philosophy and the proposed codification in fair machine learning. We arrange these fairness ideals onto an EOP spectrum, whic…
Data Ethics in the Era of Healthcare Artificial Intelligence in Africa: An Ubuntu Philosophy Perspective
Stanford Center for Biomedical Ethics, Stanford University（斯坦福大学生物医学伦理中心）；Department of Bioethics and Interdisciplinary Studies, Brody School of Medicine, East Carolina University（东卡罗来纳大学布罗迪医学院生物伦理与跨学科研究系）；Department of Biomedical Data Science, Stanford University（斯坦福大学生物医学数据科学系）；Department of Bioengineering, Stanford University（斯坦福大学生物工程系）；Department of Genetics, Stanford University（斯坦福大学遗传学系）；Department of Medicine, Stanford University（斯坦福大学医学系）
本文针对西方医疗 AI 数据伦理框架忽视非洲文化与殖民历史的问题，从 Ubuntu 哲学出发比较个人主义与社群主义路径，提出面向非洲的医疗数据伦理框架。
Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- ideals of …
The debate on AI ethics largely focuses on technical improvements and stronger regulation to prevent accidents or misuse of AI, with solutions relying on holding individual actors accountable for responsible AI development. While useful and necessary, we argue that this "agency" approach disregards more indirect and complex risks resulting from AI's interaction with the socio-economic and political context. This paper calls for a "structural" approach to assessing AI's effects in order to understand and prevent such systemic risks where no individual can be held accountable for the broader neg…
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to learn the difference between right and wrong. This is typically done in a bottom-up fashion, by exposing the model to different scenarios, annotated with human moral judgements. One question, however, is whether the trained models actually learn any consistent, higher-level ethical principles from these datasets -- and if so, what? Here, we probe the Allen AI Delphi model with a set of standardized morality questionnaire…
The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated …
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research and have been developed for a variety of tasks ranging from question answering to facial recognition. An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system. In this paper, drawing upon research in moral philosophy and metaethics, we argue that it is impossible to develop such a benchmark. As such, alternative mechanisms are necessary for evaluating whether an AI syste…
The expansion of computer science (CS) education in K--12 and higher-education in the United States has prompted deeper engagement with equity that moves beyond inclusion toward a more critical CS education. Rather than frame computing as a value-neutral tool, a justice-centered approach to equitable CS education draws on critical pedagogy to ensure the rightful presence of political struggles by emphasizing the development of not only knowledge and skills but also CS disciplinary identities. While recent efforts have integrated ethics into several areas of the undergraduate CS curriculum, cri…
University of Toronto（多伦多大学）；Australian National University（澳大利亚国立大学）
The social implications of algorithmic decision-making in sensitive contexts have generated lively debates among multiple stakeholders, such as moral and political philosophers, computer scientists, and the public. Yet, the lack of a common language and a conceptual framework for an appropriate bridging of the moral, technical, and political aspects of the debate prevents the discussion to be as effective as it can be. Social scientists and psychologists are contributing to this debate by gathering a wealth of empirical data, yet a philosophical analysis of the social implications of algorithm…
