Thinking Through and Writing About Research Ethics Beyond "Broader Impact"
Ethics in AI/ML research is increasingly recognized as an important consideration, thanks to the efforts of FAccT and the wider sociotechnical communities. They appear as a set of principles, newly added criterion in calls for papers, and organizational ethics committees. But, how does one “do” research ethics for AI/ML projects? This tutorial invites AI/ML researchers—especially, masters/doctoral students, junior, and early career researchers--to think more expansively, holistically, and critically about research ethics as it applies to their projects. We draw from various research ethics principles and practices in interpretivist and human subject-oriented disciplines to frame research ethics beyond “broader impact” and “harm.” Our goal is to share practical and adaptable primers that can guide researchers to think through ethical considerations throughout a project’s lifecourse and weave them into how they disseminate their research. The tutorial will have the following components: (1) a processual framework for research ethics; (2) imaginative exercises for research fantasies/fears; (3) case studies that raise a range of ethical considerations; (4) practical primers; and (5) discussion about creating disciplinary and organizational norms that encourage and incentivize practicing research ethics in AI/ML.