Panel: A debate on data and algorithmic ethics

Recently, there has begun a movement towards Fairness, Accountability, and Transparency (FAT) in algorithmic decision making, and in data science more broadly. The database community has not been significantly involved in this movement, despite “owning“ the models, languages, and systems that produce the (potentially biased) input to the machine learning applications. What role should the database community play in this movement? Do the objectives of fairness, accountability and transparency give rise to core data management issues that can drive new research questions and new systems, or are these “soft topics“ that are best left to be managed with policy? 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? The goal of this panel is to debate these questions, and to whet the appetite of the data management community for research in this important emerging area.