There is a lot of heat these days in the tech policy and ethics discourse. There is an enormous amount of valuable work being done on all fronts. And yet there is also sometimes bitter disciplinary infighting and political intrigue about who has the moral high ground.
The smartest thing I’ve read on this recently is Irina Raicu’s “False Dilemmas” piece, where she argues:
- “Tech ethics” research, including research explore the space of ethics in algorithm design, is really code for industry self-regulation
- Industry self-regulation and state regulation are complementary
- Any claims that “the field” is dominated by one perspective or agenda or another is overstated
All this sounds very sane but it doesn’t exactly explain why there’s all this heated discussion in the first place. I think Luke Stark gets it right:
But what does it mean to say “the problem is mostly capitalism”? And why is it impolite to say it?
To say “the problem [with technology ethics and policy] is capitalism” is to note that most if not all of the social problems we associate with today’s technology have been problems with technology ever since the industrial revolution. For example, James Beniger‘s The Control Revolution, Horkheimer‘s Eclipse of Reason, and so on all speak to the tight link that there has always been between engineering and the capitalist economy as a whole. The link has persisted through the recent iterations of recognizing first data science, then later artificial intelligence, as disruptive triumphs of engineering with a variety of problematic social effects. These are old problems.
It’s impolite to say this because it cuts down on the urgency that might drive political action. More generally, it’s an embarrassment to anybody in the business of talking as if they just discovered something, which is what journalists and many academics do. The buzz of novelty is what gets people’s attention.
It also suggests that the blame for how technology has gone wrong lies with capitalists, meaning, venture capitalists, financiers, and early stage employees with stock options. But also, since it’s the 21st century, pension funds and university endowments are just as much a part of the capitalist investing system as anybody else. In capitalism, if you are saving, you are investing. Lots of people have a diffuse interest in preserving capitalism in some form.
There’s a lot of interesting work to be done on financial regulation, but it has very little to do with, say, science and technology studies and consumer products. So to acknowledge that the problem with technology is capitalism changes the subject to something remote and far more politically awkward than to say the problem is technology or technologists.
As I’ve argued elsewhere, a lot of what’s happening with technology ethics can be thought of as an extension of what Nancy Fraser called progressive neoliberalism: the alliance of neoliberalism with progressive political movements. It is still hegemonic in the smart, critical, academic and advocacy scene. Neoliberalism, or what is today perhaps better characterized as finance capitalism or surveillance capitalism, is what is causing the money to be invested in projects that design and deploy technology in certain ways. It is a system of economic distribution that is still hegemonic.
Because it’s hegemonic, it’s impolite to say so. So instead a lot of the technology criticism gets framed in terms of the next available moral compass, which is progressivism. Progressivism is a system of distribution of recognition. It calls for patterns of recognizing people for their demographic and, because it’s correlated in a sensitive way, professional identities. Nancy Fraser’s insight is that neoliberalism and progressivism have been closely allied for many years. One way that progressivism is allied with neoliberalism is that progressivism serves as a moral smokescreen for problems that are in part caused by neoliberalism, preventing an effective, actionable critique of the root cause of many technology-related problems.
Progressivism encourages political conflict to be articulated as an ‘us vs. them’ problem of populations and their attitudes, rather than as problem of institutions and their design. This “us versus them” framing is baldly stated than in the 2018 AI Now Report:
The AI accountability gap is growing: The technology scandals of 2018 have shown that the gap between those who develop and profit from AI—and those most likely to suffer the consequences of its negative effects—is growing larger, not smaller. There are several reasons for this, including a lack of government regulation, a highly concentrated AI sector, insufficient governance structures within technology companies, power asymmetries between companies and the people they serve, and a stark cultural divide between the engineering cohort responsible for technical research, and the vastly diverse populations where AI systems are deployed. (Emphasis mine)
There are several institutional reforms called for in the report, but the focus on a particular sector that it constructs as “the technology industry” composed on many “AI systems”, it cannot address broader economic issues such as unfair taxation or gerrymandering. Discussion of the overall economy is absent from the report; it is not the cause of anything. Rather, the root cause is a schism between kinds of people. The moral thrust of this claim hinges on the implied progressivism: the AI/tech people, who are developing and profiting, are a culture apart. The victims are “diverse”, and yet paradoxically unified in their culture as not the developers. This framing depends on the appeal of progressivism as a unifying culture whose moral force is due in large part because of its diversity. The AI developer culture is a threat in part because it is separate from diverse people–code for its being white and male.
This thread continues throughout the report, as various critical perspectives are cited in the report. For example:
A second problem relates to the deeper assumptions and worldviews of the designers of ethical codes in the technology industry. In response to the proliferation of corporate ethics initiatives, Greene et al. undertook a systematic critical review of high-profile “vision statements for ethical AI.” One of their findings was that these statements tend to adopt a technologically deterministic worldview, one where ethical agency and decision making was delegated to experts, “a narrow circle of who can or should adjudicate ethical concerns around AI/ML” on behalf of the rest of us. These statements often assert that AI promises both great benefits and risks to a universal humanity, without acknowledgement of more specific risks to marginalized populations. Rather than asking fundamental ethical and political questions about whether AI systems should be built, these documents implicitly frame technological progress as inevitable, calling for better building.
That systematic critical reviews of corporate policies express self-serving views that ultimately promote the legitimacy of the corporate efforts is a surprise to no one; it is no more a surprise than the fact that critical research institutes staffed by lawyers and soft social scientists write reports recommending that their expertise is vitally important for society and justice. As has been the case in every major technology and ethical scandal for years, the first thing the commentariat does is publish a lot of pieces justifying their own positions and, if they are brave, arguing that other people are getting too much attention or money. But since everybody in either business depends on capitalist finance in one way or another, the economic system is not subject to critique. In other words, once can’t argue that industrial visions of ‘ethical AI’ are favorable to building new AI products because they are written in service to capitalist investors who profit from the sale of new AI products. Rather, one must argue that they are written in this way because the authors have a weird technocratic worldview that isn’t diverse enough. One can’t argue that the commercial AI products neglect marginal populations because these populations have less purchasing power; one has to argue that the marginal populations are not represented or recognized enough.
And yet, the report paradoxically both repeatedly claims that AI developers are culturally and politically out of touch and lauds the internal protests at companies like Google that have exposed wrongdoing within those corporations. The actions of “technology industry” employees belies the idea that problem is mainly cultural; there is a managerial profit-making impulse that is, in large, stable companies in particular, distinct from that the rank-and-file engineer. This can be explained in terms of corporate incentives and so on, and indeed the report does in places call for whistleblower protections and labor organizing. But these calls for change cut against and contradict other politically loaded themes.
There are many different arguments contained in the long report; it is hard to find a reasonable position that has been completely omitted. But as a comprehensive survey of recent work on ethics and regulation in AI, its biases and blind spots are indicative of the larger debate. The report concludes with a call for a change in the intellectual basis for considering AI and its impact:
It is imperative that the balance of power shifts back in the public’s favor. This will require significant structural change that goes well beyond a focus on technical systems, including a willingness to alter the standard operational assumptions that govern the modern AI industry players. The current focus on discrete technical fixes to systems should expand to draw on socially-engaged disciplines, histories, and strategies capable of providing a deeper understanding of the various social contexts that shape the development and use of AI systems.
As more universities turn their focus to the study of AI’s social implications, computer science and engineering can no longer be the unquestioned center, but should collaborate more equally with social and humanistic disciplines, as well as with civil society organizations and affected communities. (Emphasis mine)
The “technology ethics” field is often construed, in this report but also in the broader conversation, as one of tension between computer science on the one hand, and socially engaged and humanistic disciplines on the other. For example, Selbst et al.’s “Fairness and Abstraction in Sociotechnical Systems” presents a thorough account of pitfalls of computer science’s approach to fairness in machine learning, and proposes a Science and Technology Studies. The refrain is that by considering more social context, more nuance, and so on, STS and humanistic disciplines avoids the problems that engineers, who try to provide portable, formal solutions, don’t want to address. As the AI Now report frames it, a benefit of the humanistic approach is that it brings the diverse non-AI populations to the table, shifting the balance of power back to the public. STS and related disciplines claim the status of relevant expertise in matters of technology that is somehow not the kind of expertise that is alienating or inaccessible to the public, unlike engineering, which allegedly dominates the higher education system.
I am personally baffled by these arguments; so often they appear to conflate academic disciplines with business practices in ways that most practitioners I engage with would not endorse. (Try asking an engineer how much they learned in school, versus on the job, about what it’s like to work in a corporate setting.) But beyond the strange extrapolation from academic disciplinary disputes (which are so often about the internal bureaucracies of universities it is, I’d argue after learning the hard way, unwise to take them seriously from either an intellectual or political perspective), there is also a profound absence of some fields from the debate, as framed in these reports.
I’m referring to the quantitative social sciences, such as economics and quantitative sociology, or what might be more be more generally converging on computational social science. These are the disciplines that one would need to use to understand the large-scale, systemic impact of technology on people, including the ways costs and benefits are distributed. These disciplines deal with social systems and include technology–there is a long tradition within economics studying the relationship between people, goods, and capital that never once requires the term “sociotechnical”–in a systematic way that can be used to predict the impact of policy. They can also connect, through applications of business and finance, the ways that capital flows and investment drive technology design decisions and corporate competition.
But these fields are awkwardly placed in technology ethics and politics. They don’t fit into the engineering vs. humanities dichotomy that entrances so many graduate students in this field. They often invoke mathematics, which makes them another form of suspicious, alien, insufficiently diverse expertise. And yet, it may be that these fields are the only ones that can correctly diagnose the problems caused by technology in society. In a sense, the progressive framing of the problems of technology makes technogy’s ills a problem of social context because it is unequipped to address them as a problem of economic context, and it wouldn’t want know that it is an economic problem anyway, for two somewhat opposed reasons: (a) acknowledging the underlying economic problems is taboo under hegemonic neoliberalism, and (b) it upsets the progressive view that more popularly accessible (and, if you think about it quantitatively, therefore as a result of how it is generated and constructed more diverse) humanistic fields need to be recognized as much as fields of narrow expertise. There is no credence given to the idea that narrow and mathematized expertise might actually be especially well-suited to understand what the hell is going on, and that this is precisely why members of these fields are so highly sought after by investors to work at their companies. (Consider, for example, who would be best positioned to analyze the “full stack supply chain” of artificial intelligence systems, as is called for by the AI Now report: sociologists, electrical engineers trained in the power use and design of computer chips, or management science/operations research types whose job is to optimize production given the many inputs and contingencies of chip manufacture?)
At the end of the day, the problem with the “technology ethics” debate is a dialectic cycle whereby (a) basic research is done by engineers, (b) that basic research is developed in a corporate setting as a product funded by capitalists, (c) that product raises political hackles and makes the corporations a lot of money, (d) humanities scholars escalate the political hackles, (e) basic researchers try to invent some new basic research because the politics have created more funding opportunities, (f) corporations do some PR work trying to CYA and engage in self-regulation to avoid litigation, (g) humanities scholars, loathe to cede the moral high ground, insist the scientific research is inadequate and that the corporate PR is bull. But this cycle is not necessarily productive. Rather, it sustains itself as part of a larger capitalist system that is bigger than any of these debates, structures its terms, and controls all sides of the dialog. Meanwhile the experts on how that larger system works are silent or ignored.
Fraser, Nancy. “Progressive neoliberalism versus reactionary populism: A choice that feminists should refuse.” NORA-Nordic Journal of Feminist and Gender Research 24.4 (2016): 281-284.
Greene, Daniel, Anna Laura Hoffman, and Luke Stark. “Better, Nicer, Clearer, Fairer: A Critical Assessment of the Movement for Ethical Artificial Intelligence and Machine Learning.” Hawaii International Conference on System Sciences, Maui, forthcoming. Vol. 2019. 2018.
Raicu, Irina. “False Dilemmas”. 2018.
Selbst, Andrew D., et al. “Fairness and Abstraction in Sociotechnical Systems.” ACM Conference on Fairness, Accountability, and Transparency (FAT*). 2018.
Whittaker, Meredith et al. “AI Now Report 2018”. 2018.