Responsible data management

Julia Stoyanovich, Serge Abiteboul, Bill Howe, H. V. Jagadish, Sebastian Schelter

    Research output: Contribution to journalArticlepeer-review


    Responsible data management involves incorporating ethical and legal considerations across the life cycle of data collection, analysis, and use in all data-intensive systems, whether they involve machine learning and AI or not. Decisions during data collection and preparation profoundly impact the robustness, fairness, and interpretability of data-intensive systems. Experts must consider these earlier life cycle stages to improve data quality, control for bias, and allow humans to oversee the operation of these systems. Data alone is insufficient to distinguish between a distorted reflection of a perfect world, a perfect reflection of a distorted world, or a combination of both. The assumed or externally verified nature of the distortions must be explicitly stated to allow experts to decide whether and how to mitigate their effects.

    Original languageEnglish (US)
    Pages (from-to)64-74
    Number of pages11
    JournalCommunications of the ACM
    Issue number6
    StatePublished - Jun 2022

    ASJC Scopus subject areas

    • General Computer Science


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