The 1AC portrays AI as a technical/research problem. The narrative of inevitable AGI/Apocalypse depoliticizes development and encourages learned helplessness when we need democratic accountability

(Annette Zimmermann is Lecturer in Philosophy at the University of York and Technology & Human Rights Fellow at the Carr Center for Human Rights, Elena Di Rosa is a PhD student in Philosophy at Princeton University., Hochan "Sonny" Kim is a PhD student in Politics at Princeton University, , 1-9)

In light of these issues, any approach focused on optimizing for procedural fairness—without attention to the social context in which these systems operate—is going to be insufficient. Algorithmic design cannot be fixed in isolation. Developers cannot just ask, “What do I need to do to fix my algorithm?” They must rather ask: “How does my algorithm interact with society at large, and as it currently is, including its structural inequalities?” We must carefully examine the relationship and contribution of AI systems to existing configurations of political and social injustice, lest these systems continue to perpetuate those very conditions under the guise of neutrality. As many critical race theorists and feminist philosophers have argued, neutral solutions might well secure just outcomes in a just society, but only serve to preserve the status quo in an unjust one. What, then, does algorithmic and AI fairness require, when we attend to the place of this technology in society at large? The first—but far from only—step is transparency about the choices that go into AI development and the responsibilities that such choices present. Some might be inclined to absolve AI developers and researchers of moral responsibility despite their expertise on the potential risks of deploying these technologies. After all, the thought goes, if they followed existing regulations and protocols and made use of the best available information and data sets, how can they be held responsible for any errors and accidents that they did not foresee? On this view, such are the inevitable, necessary costs of technological advancement—growing pains that we will soon forget as the technology improves over time. One significant problem with this quietism is the assumption that existing regulations and research are adequate for ethical deployment of AI—an assumption that even industry leaders themselves, including Microsoft’s president and chief legal officer Brad Smith, have conceded is unrealistic. The biggest problem with this picture, though, is its inaccurate portrayal of how AI systems are developed and deployed. Developing algorithmic systems entails making many deliberate choices. For example, machine learning algorithms are often “trained” to navigate massive data sets by making use of certain pre-defined key concepts or variables, such as “creditworthiness” or “high-risk individual.” The algorithm does not define these concepts itself; human beings—developers and data scientists—choose which concepts to appeal to, at least as an initial starting point. It is implausible to think that these choices are not informed by cultural and social context—a context deeply shaped by a history of inequality and injustice. The variables that tech practitioners choose to include, in turn, significantly influence how the algorithm processes the data and the recommendations it ultimately makes. Making choices about the concepts that underpin algorithms is not a purely technological problem. For instance, a developer of a predictive policing algorithm inevitably makes choices that determine which members of the community will be affected and how. Making the right choices in this context is as much a moral enterprise as it is a technical one. This is no less true when the exact consequences are difficult even for developers to foresee. New pharmaceutical products often have unexpected side effects, but that is precisely why they undergo extensive rounds of controlled testing and trials before they are approved for use—not to mention the possibility of recall in cases of serious, unforeseen defect. Unpredictability is thus not an excuse for moral quiescence when the stakes are so high. If AI technology really is unpredictable, this only presents more reason for caution and moderation in deploying these technologies. Such caution is particularly called for when the AI is used to perform such consequential tasks as allocating the resources of a police department or evaluating the creditworthiness of first-time homebuyers. To say that these choices are deliberate is not to suggest that their negative consequences are always, or even often, intentional. There may well be some clearly identifiable “bad” AI developers who must be stopped, but our larger point is that all developers, in general, know enough about these technologies to be regarded as complicit in their outcomes—a point that is obscured when we act as if AI technology is already escaping human control. We must resist the common tendency to think that an AI-driven world means that we are freed not only from making choices, but also from having to scrutinize and evaluate these automated choices in the way that we typically do with human decisions. (This psychological tendency to trust the outputs of an automated decision making system is what researchers call “automation bias.”) Complicity here means that the responsibility for AI is shared by individuals involved in its development and deployment, regardless of their particular intentions, simply because they know enough about the potential harms. As computer scientist Joshua Kroll has argued, “While structural inscrutability frustrates users and oversight entities, system creators and operators always determine that the technologies they deploy are fit for certain uses, making no system wholly inscrutable.” The apocalypse-saturated discourse on AI, by contrast, encourages a mentality of learned helplessness. The popular perception that strong AI will eventually grow out of our control risks becoming a self-fulfilling prophecy, despite the present reality that weak AI is very much the product of human deliberation and decision making. Avoiding learned helplessness and automation bias will require adopting a model of responsibility that recognizes that a variety of (even well-intentioned) agents must share the responsibility for AI given their role in its development and deployment. In the end, the responsible development and deployment of weak AI will involve not just developers and designers, but the public at large. This means that we need, among other things, to scrutinize current narratives about AI’s potential costs and benefits. As we have argued, AI’s alleged neutrality and inevitability are harmful, yet pervasive, myths. Debunking them will require an ongoing process of public, democratic contestation about the social, political, and moral dimensions of algorithmic decision making. This is not an unprecedented proposal: similar suggestions have been made by philosophers and activists seeking to address other complex, collective moral problems, such as climate change and sweatshop labor. Just as their efforts have helped raise public awareness and spark political debate about those issues, it is high time for us as a public to take seriously our responsibilities for the present and looming social consequences of AI. Algorithmic bias is not a purely technical problem for researchers and tech practitioners; we must recognize it as a moral and political problem in which all of us—as democratic citizens—have a stake. Responsibility cannot simply be offloaded and outsourced to tech developers and private corporations.