TransFAT: Translating fairness, accountability and transparency into data science practice

Julia Stoyanovich

    Research output: Contribution to journalConference article

    Abstract

    Data science holds incredible promise for improving peoples lives, accelerating scientific 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.

    Original languageEnglish (US)
    JournalCEUR Workshop Proceedings
    Volume2417
    StatePublished - 2019
    Event1st International Workshop on Processing Information Ethically, PIE 2019 - Rome, Italy
    Duration: Jun 4 2019 → …

    Keywords

    • Diversity
    • Fairness
    • Responsible data science
    • Transparency

    ASJC Scopus subject areas

    • Computer Science(all)

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