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Title: TransFAT: Translating Fairness, Accountably and Transparency into Data Science Practice
Data science holds incredible promise for improving people's lives, accelerating scientifi c discovery and innovation, and bringing about positive societal change. Yet, if not used responsibly -- in accordance with legal and ethical norms -- the same technology can reinforce economic and political inequities, destabilize global markets, and reaffirm systemic bias. In this paper I discuss an ongoing regulatory effort in New York City, where the goal is to develop a methodology for enabling responsible use of algorithms and data in city agencies. I then highlight some ongoing work that makes part of the Data, Responsibly project, aiming to operationalize fairness, diversity, accountability, transparency, and data protection at all stages of the data science lifecycle. Additional information about the project, including technical papers, teaching materials, and open-source tools, is available at dataresponsibly.github.io.  more » « less
Award ID(s):
1926250 1916647
PAR ID:
10111425
Author(s) / Creator(s):
Date Published:
Journal Name:
1st International Workshop on Processing Information Ethically, PIE@CAiSE 2019
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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