Opportunities

Panelists and audience members discussed specific examples of problems of fairness (and justice), including cash bail in the criminal justice system, “bad faith” search phrases (e.g., the question, “Did the Holocaust happen?”), and representational harm in image-labeling. Panelists noted a key challenge that technology, on its own, is not good at explaining when it should not be used or when it has reached its limits. Panelists pointed out that understanding broader historical and sociological debates in the domain of application and investigating contemporary reform efforts, for example in criminal justice, can help to clarify the place of algorithmic prediction and classification tools in a given domain. Partnering with civil-society groups can ensure a sound basis for making tough decisions about when and how to intervene when a platform or software is found to be amplifying societal biases, is being gamed by “bad” actors, or otherwise facilitates harm to users.

In June of 2018, the Algorithmic Fairness and Opacity Working Group (AFOG) held a summer workshop with the theme “Algorithms are Opaque and Unfair: Now What?.” The event was organized by Berkeley I School Professors (and AFOG co-directors) Jenna Burrell and Deirdre Mulligan and postdoc Daniel Kluttz, and Allison Woodruff and Jen Gennai from Google. Our working group is generously sponsored by Google Trust and Safety and hosted at the UC Berkeley School of Information.

Inspired by questions that came up at our biweekly working group meetings during the 2017-2018 academic year, we organized four panels for the workshop. The panel topics raised issues that we felt required deeper consideration and debate. To make progress we brought together a diverse, interdisciplinary group of experts from academia, industry, and civil society in a workshop-style environment. In panel discussions, we considered potential ways of acting on algorithmic (un)fairness and opacity. We sought to consider the fullest possible range of ‘solutions,’ including technical implementations (algorithms, user-interface designs), law and policy, standard-setting, incentive programs, new organizational processes, labor organizing, and direct action.