Artificial intelligence and machine learning are both a blessing and a curse for governance. In theory, algorithmic governance makes government more efficient, more accurate, and more fair. But the emergence of automation in governance also rests on public-private collaborations that expand both public and private power, aggravate transparency and accountability gaps, and create significant obstacles for those seeking algorithmic justice. In response, a nascent body of law proposes technocratic policy changes to foster algorithmic accountability, ethics, and transparency.
This Article examines an alternative vision of algorithmic governance, one advanced primarily by social and labor movements instead of technocrats and firms. The use of algorithmic governance in increasingly high-stakes settings has generated an outpouring of activism, advocacy, and resistance. This mobilization draws on the same concerns that animate budding policy responses. But social and labor movements offer an alternative source of constraints on algorithmic governance: direct resistance from the bottom up. These movements confront head-on the entanglement of economic power, racial hierarchy, and government surveillance.
Using three case studies, this Article explores how tech workers and social movements are resisting and mobilizing against technologies that expand surveillance and funnel wealth to the private sector. Each case study illustrates how the intermingling of state and private power has required movements to engage both within and outside firms to counteract the growing appeal of automation. Yet the dominant approaches to regulating the government’s uses of technology continue to afford a privileged role to private firms and elite institutions, sidelining movement demands. The fundamental challenge posed by these movements will be whether — and how — law and policy can accommodate demands for bottom-up control. This Article sketches a new vision for algorithmic accountability, with a more vibrant role for workers and for the public in determining how firms and government institutions work together.
© 2022 Brigham Young University Law Review
Algorithmic Governance from the Bottom Up,
48 BYU L. Rev.
Available at: https://digitalcommons.law.byu.edu/lawreview/vol48/iss1/6