As critiques of the centrality of neoliberal economic logic gain traction, we must take care that such work does not simply clear the path for an emerging hegemony of neoliberal computational logic. Instead, we must be attentive to proponents of the epistemic and political dominance of computational mechanisms, and we must critique them on similar grounds and with similar urgency.
Would reconsidering the state/private action divide in First Amendment jurisprudence just unleash a torrent of endlessly abusive communications and misinformation of all kinds? Can antitrust categories help to solve the problem?
Whether or not an information fiduciary model would be the best way to regulate data governance, it is not guilty of many of the accusations that Lina Khan and David Pozen lob at it.
Rather than seeing data as person-like or property-like entity, egalitarians should focus on data relations: the way data’s collection and use puts people (or entities) into relation with one another.
We do not want to have to choose between John Roberts and Mark Zuckerberg as the guardians of democracy, though that is what current doctrine seems to require. Luckily, the contemporary framework is not the only one available to us.
The exact trajectory predictive legal analytics will take in the coming years is really anyone’s guess, but we can be fairly sure that the discourse of cost cutting and increased rationality via technology will be coming for the legal system, piece by piece. As such, it is worthwhile for left legal theory to take legal futurism, and even the idea of a legal singularity, seriously. Doing so, even for critics of liberalism, requires revisiting the liberal principle of the rule of law.
Like all modern organizations, modern states are subject to a “data imperative”: a mandate to mine data and to decide what to manage based on what can be measured. In service of data-hungry machine learning techniques, the state (and its contractors) find themselves compelled not only to seek and demand new kinds of data, but also to mine it in a somewhat agnostic fashion to find the relations that stick. It is no longer necessary to flatten society to make it legible (as high modernism required); instead, ubiquitous data capture means that categories emerge inductively from regularities observed in the data.
Automated hiring systems are paradoxical. Although often the stated reason for adopting them is to curtail human bias, they frequently end up exacerbating the biases they’re meant to correct. Even as employment discrimination continues to morph with the introduction of new technologies, so, too, should the law change to meet it head to head.
Sandeep Vaheesan interviews Frank Pasquale about his forthcoming book, New Laws of Robotics: Defending Human Expertise in the Age of AI.
The Meritocracy Trap’s account of the relationships among elite education, skill-biased technical change, and rising economic inequality is, in my mind, one of the book’s most important arguments, even as it is undoubtedly one of the least discussed. I’m therefore delighted and grateful that Gordon chose to focus his attention on these matters.
This post turns our attention to The Meritocracy Trap’s argument that industrial policy contributed to the technological innovations that over-reward elite workers at the expense of the poor and working class, and that industrial policy will be essential to build a fairer economy.
Economic models produce blindspots, compressing qualitative differences into quantitative measures. Yet, this vice is also the source of their power.
Over the past decade, algorithmic accountability has become an important concern for social scientists, computer scientists, journalists, and lawyers. Exposés have sparked vibrant debates about algorithmic sentencing. Researchers have exposed tech giants showing women ads for lower-paying jobs, discriminating against the aged, deploying deceptive dark patterns to trick consumers into buying things, and manipulating users toward rabbit holes of extremist…
By now, many of the societal, political, and distributive harms caused by large technology companies and so-called “social” media companies (Amazon, Facebook, Google, etc.) have been surfaced. They invade our privacy, decrease market competition, erode our sense of self and, despite their euphemistic label, our sense of community. Shoshana Zuboff’s new book—The Age of Surveillance…
The value of Balkin’s fiduciary framework, I argue, resides not in providing an enforceable legal relationship but providing a framework for privacy legislation. The existing frameworks – the Privacy Principles adopted by the Organization for Economic Co-operation and Development (OECD) in 1980 which rely heavily on notice and consent and the property framework introduced by Louis Brandeis in “The Right To Privacy” (both of which I discuss in this privacy white paper) – have significant limitations. Balkin’s proposed fiduciary framework provides a model for legislation that recognizes that the nature of the relationship between information collectors and aggregators requires imposing additional duties and restrictions to adequately protect consumers, while still enabling commerce and facilitating competition.
