Beware of "Explanations" of AI

Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artificial intelligence (XAI), highlighting the potential of explanations to enhance trust, support adoption, and meet regulatory standards. However, the question of what constitutes a "good" explanation is dependent on the goals, stakeholders, and context. At a high level, psychological insights such as the concept of mental model alignment can offer guidance, but success in practice is challenging due to social and technical factors. As a result of this ill-defined nature of the problem, explanations can be of poor quality (e.g. unfaithful, irrelevant, or incoherent), potentially leading to substantial risks. Instead of fostering trust and safety, poorly designed explanations can actually cause harm, including wrong decisions, privacy violations, manipulation, and even reduced AI adoption. Therefore, we caution stakeholders to beware of explanations of AI: while they can be vital, they are not automatically a remedy for transparency or responsible AI adoption, and their misuse or limitations can exacerbate harm. Attention to these caveats can help guide future research to improve the quality and impact of AI explanations.

Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue

In response to growing recognition of the social, legal, and ethical impacts of new AI-based technologies, major AI and ML conferences and journals now encourage or require submitted papers to include ethics impact statements and undergo ethics reviews. This move has sparked heated debate concerning the role of ethics in AI and data science research, at times devolving into counter-productive name-calling and threats of "cancellation." We diagnose this deep ideological conflict as one between atomists and holists. Among other things, atomists espouse the idea that facts are and should be kept separate from values, while holists believe facts and values are and should be inextricable from one another. With the goals of encouraging civil discourse across disciplines and reducing disciplinary polarization, we draw on a variety of historical sources ranging from philosophy and law, to social theory and humanistic psychology, to describe each ideology's beliefs and assumptions. Finally, we call on atomists and holists within the data science community to exhibit greater empathy during ethical disagreements and propose four targeted strategies to ensure data science research benefits society.

How Personal is Machine Learning Personalization?

Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of the person. In light of the recent calls by the IEEE to consider the effects of ML on human well-being, we ask whether ML personalization can be reconciled with these humanistic views of the person, which highlight the importance of moral and social identity. As human behavior increasingly becomes digitized, analyzed, and predicted, to what extent do our subsequent decisions about what to choose, buy, or do, made both by us and others, reflect who we are as persons? This paper first explicates the term personalization by considering ML personalization and highlights its relation to humanistic conceptions of the person, then proposes several dimensions for evaluating the degree of personalization of ML personalized scores. By doing so, we hope to contribute to current debate on the issues of algorithmic bias, transparency, and fairness in machine learning.