Auditing Algorithms: Adding Accountability to Automated Authority is a group of events designed to produce a white paper that will help to define and develop the emerging research community for “algorithm auditing.” Algorithmic Auditing is a research design that has shown promise in diagnosing the unwanted consequences of algorithmic systems.
Automated software-based systems in finance, media, information, transportation, learning, or any application of computing can easily create outcomes that are unforeseeable by their designers, so algorithm auditing has the potential to improve the design of these systems by making their consequences visible. Auditing in this sense takes its name from the social scientific “audit study” where one feature is manipulated in a field experiment, although it is also reminiscent of a financial audit.
These events and the resulting white paper proposes to coalesce this new area of inquiry and to produce a report characterizing the state of the art and potential future directions. Participants and white paper co-authors will have opportunities to clarify the potential dangers of algorithmic systems, to specify these dangers as new research problems, to articulate challenges that they face as researchers interested in this area, to present existing methods for auditing or needs for new methods, and to propose research agendas that can provide new insights that advance science and benefit society.
As researchers we are committed to research autonomy and transparency in our relationships. These traits are essential to our credibility. We do not accept grants that limit our ability to carry out research in the way we see fit. We follow best practices in reporting conflicts of interest.
We kindly acknowledge the support of:
- Center for People & Infrastructures, University of Illinois
- Center for Political Studies, Institute for Social Research, University of Michigan
- Department of Communication Studies, University of Michigan
- School of Information, University of Michigan
- Berkman-Klein Center for Internet & Society at Harvard University
- US National Science Foundation
This material is based upon work supported by the National Science Foundation under Grant No. ITR-1665151. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Word used by programmers when they do not want to explain what they did.
–Traditional programmer humor. (example)