Special Session: A Technical Research Agenda in Data Ethics and Responsible Data Management

SESSION DESCRIPTION Recently, there has begun a movement towards fairness, accountability, and transparency (FAT) in algorithmic decision making, and in data science more broadly [1–4]. The database community has not been significantly involved in this movement, despite “owning” the models, languages, and systems that produce the input to the machine learning applications that are often the focus in data science. If training data are biased, or have errors, it stands to reason that the algorithmic result will also be unfair or erroneous. Similarly, transparency of just the algorithm is usually insufficient to understand why certain results were obtained: one needs also to know the data used. In short, FAT depend not just on the algorithm, but also on the data. This observation raises several important questions: What are the core data management issues to which the objectives of fairness, accountability and transparency give rise? What role should the database community play in this movement? Will emphasis on these topics dilute our core competency in techniques and technologies for data, or can it reinforce our central role in technology stacks ranging from startups to the enterprise, and from local non-profits to the federal government? This special session features leading researchers from machine learning, software engineering, security and privacy, and natural language processing, who are doing exciting technical work in FAT. The goal of this session is to outline a technical research agenda in data management foundations and systems around data ethics.