Bottom up democratic deliberation on AI is more effective than top down legal changes

(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)

San Francisco, for example, recently banned the use of facial recognition tools in policing due to the increasing amount of empirical evidence of algorithmic bias in law enforcement technology; the city’s board of supervisors passed the “Stop Secret Surveillance” ordinance in an 8-1 vote. The ordinance, which states that “the technology will exacerbate racial injustice and threaten our ability to live free of continuous government monitoring,” also establishes more wide-ranging accountability mechanisms beyond the use of facial recognition technology in policing, such as a requirement that city agencies obtain approval before purchasing and implementing other types of surveillance technology. Broaching questions of algorithmic justice via the democratic process would give members of communities most impacted by algorithmic bias more direct democratic power over crucial decisions concerning weak AI—not merely after its deployment, but also at the design stage. San Francisco’s new ordinance exemplifies the importance of providing meaningful opportunities for bottom-up democratic deliberation about, and democratic contestation of, algorithmic tools ideally before they are deployed: “Decisions regarding if and how surveillance technologies should be funded, acquired, or used, and whether data from such technologies should be shared, should be made only after meaningful public input has been solicited and given significant weight.” Similar local, bottom-up procedures akin to San Francisco’s model can and should be implemented in other communities, rather than simply waiting for the creation of more comprehensive, top-down regulatory institutions. Significant public oversight over AI development and deployment is already possible. Rather than allowing tech practitioners to navigate the ethics of AI by themselves, we the public should be included in decisions about whether and how AI will be deployed and to what ends. Furthermore, even once we do create new, larger-scale democratic institutions empowered to legally regulate AI in a meaningful way, bottom-up democratic procedures will still be essential: they play a crucial role in identifying which agendas such institutions ought to pursue, and they can shine a light on whose interests are most affected by emerging technologies. Moving to this agenda-setting approach means incorporating an ex ante perspective when we think about algorithmic accountability, rather than resigning ourselves to a callous, ex post, “wait and see” attitude. When we wait and see, we let corporations and practitioners take the lead and set the terms. In the meantime, we expect those who are already experiencing significant social injustice to continue bearing its burden. Of course, shifting democratic agendas toward decisions about algorithmic tools will not entirely resolve the problem of algorithmic bias. To take full responsibility for how technology shapes our lives going forward, we will have to make the deployment of weak AI democratically contestable by putting it on our democratic agendas. That being said, we will eventually have to combine that strategy with an effort to establish institutions that can enforce the just and equitable use of technology after it has been deployed. In other words, a democratic critique of algorithmic injustice requires both an ex ante and an ex post perspective. In order for us to start thinking about ex post accountability in a meaningful way—that is, in a way that actually reflects the concerns and lived experiences of those most affected by algorithmic tools—we need to first make it possible for society as a whole, not just tech industry employees, to ask the deeper ex ante questions (e.g. “Should we even use weak AI in this domain at all?”). Changing the democratic agenda is a prerequisite to tackling algorithmic injustice, not just one policy goal among many. Democratic agenda setting can be a powerful mechanism for exercising popular control over state and corporate use of technology, and for contesting technology’s threats to our rights. Effective agenda-setting, of course, will mean coupling the public’s agenda setting power with tangible bottom-up decision making power, rather than merely exercising our rights of deliberation and consultation. This is where we can learn from other recent democratic innovations, such as participatory budgeting, in which local and municipal decisions about how to allocate resources for infrastructure, energy, healthcare, and environmental policy are being made directly by residents themselves after several rounds of collective deliberation. Enabling more robust democratic participation from the outset helps us identify the kinds of concerns and problems that we ought to prioritize. Rather than rushing to quick, top-down solutions aimed at quality control, optimization, and neutrality, we must first clarify what particular kind of problem we are trying to solve in the first place. Until we do so, algorithmic decision making will continue to entrench social injustice, even as tech optimists herald it as the cure for the very ills it exacerbates.