The FAccT is an annual conference that takes place in a different location every year, with this year’s gathering in Chicago, Illinois. This cross-disciplinary conference aims to converge scholars from many backgrounds – computer science, law, humanities, and social sciences, to find innovative and purposeful solutions to relevant challenges in the emerging area as well as encourage “fairness, accountability, and transparency in socio-technical systems.” –ASM FAccT Conference 2023.
At the conference this year, the FAccT chose 6 papers to receive the Best Paper Award. 12 contributors, 6 of which are from MTU (who will be listed below), produced a work entitled “Preventing Discriminatory Decision-making in Evolving Data Streams” and received the accolade on the first day of the conference, which started June 12th and ends on June 15th. Their findings address a growing concern that bias in machine learning is on the rise and has remained relatively unchecked for some time. Firstly, accountability and responsibility must be restored by ensuring that Streaming Machine Learning (SML) algorithms are evolving to match a developing real-time data stream. They assert that preparing for the future can be aided by adapting to changing data distributions to form more pinpoint predictions on incoming data and enforce that fairness in its development should remain paramount by including fairness constraints and metrics.
We congratulate our award winners on their great efforts and deserved accolades. Their contribution to the development of data streams is invaluable and ahead of other literature that has been produced on the subject to date. It embodies the goals of the FaccT Conference’s initiatives to encourage “fairness, accountability, and transparency.”
Contributors from Michigan Technological University: Zichong Wang, Sneha Karki, Tyler Zetty, Shan Zhou, Dukka Kc, and Wenbin Zhang