Breaking Up (with) AI Ethics

摘要

The ethics of artificial intelligence (AI) have become a matter of public concern. According to a recent Stanford report, the number of research papers in the area given at major conferences such as the annual Conference on Neural Information Processing Systems has increased fivefold since 2014, and ethics officers now abound at global technology firms (Moss and Metcalf 2020). Such major institutions as the US government, the United Nations, and the Vatican have articulated visions for so-called ethical AI.By AI ethics here I mean the study of how human values both shape and are shaped by the development of AI technologies. This definition is capacious: it includes the design and deployment of these systems with human values in mind; assessments and activism around the societal impacts of said technologies and their imbrications within existing asymmetries of power, justice, and equality; and the wider relationship between computing technologies and humans as ethical and moral creatures, for instance, through such phenomena as human emotions. Work in these areas is done by trained “ethicists” only infrequently, rarely involves what a member of the public would first think of when asked to describe AI, and sounds outré yet is all too relevant to contemporary social policy and societal inequity.The definition I offer is expansive, perhaps too much so. However, any definition in this field is perilous. The term AI is a leaky discursive umbrella sheltering heterogeneous and often contradictory ideas and practices. It is a quintessential boundary object of the ideal type, “plastic enough to adapt to local needs and constraints of the several parties employing [it], yet robust enough to maintain a common identity across sites” (Star and Griesemer 1989: 393). Those identifying with the term AI ethics might be expected to at least signal some vague acknowledgment that the development and deployment of AI technologies involve normative stakes or impacts. However, a welter of methods, interests, and political positions operate uneasily within this shallow consensus; given its shortcomings, some scholars working on what would colloquially be understood as “AI ethics” eschew the word ethics entirely.Here, I aim to disaggregate AI ethics discourse through reviews of three recent books whose authors grapple in various ways with its rise and prominence. Those seeking an overview would benefit from consulting the first listed: The Alignment Problem: Machine Learning and Human Values, written for a general audience by Brian Christian. A science journalist, Christian grounds the book in dozens of interviews with academics and practitioners and frames it around the titular “alignment problem”: how to design machine learning (ML) systems “in alignment” with the intentions of their creators, ones which “capture our norms and values, understand what we mean or intend, and, above all, do what we want” (13). This “alignment problem” is presented as an engineering one, a framing that takes as a given the ongoing development and deployment of AI systems and implies it is possible to ameliorate these technologies sufficiently through various technical improvements.The Alignment Problem provides useful background on the contemporary technical landscape for those not already immersed in the field. When picturing an AI, the public might think of the psychotic HAL 9000 of Kubrick’s 2001: A Space Odyssey or Lt. Commander Data of Star Trek, but today’s AI systems are neither sentient or nor particularly charismatic. Christian points to the three main subfields of contemporary ML: unsupervised learning, in which an ML system is provided a mass of data and set to identify statistical patterns within it; supervised learning, in which an ML system takes a mass of already categorized data and uses the correlations it finds there to predict into which categories some new set of data should be sorted; and reinforcement learning, in essence a virtual Skinner box, an