Accountability in Algorithmic Systems: From Principles to Practice

Daricia Wilkinson, Kate Crawford, Hanna Wallach, Deborah Raji, Bogdana Rakova, Ranjit Singh, Angelika Strohmayer, Ethan Zuckerman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
173 Downloads (Pure)


Growing concerns over the societal implications of artificial intelligence has motivated an interdisciplinary push towards mechanisms and tools that hold algorithmic systems accountable. Although there have been considerable strides around defining what it means to hold AI systems accountable, operationalizing those principles have created a barrage of challenges. Researchers, practitioners, and regulators have all raised concerns about the completeness of accountability methods and observed spikes in anxiousness about the potential risk of these tools being manipulated as rubber stamps of approval while harms continue to slip through the cracks. This interactive panel gathers researchers and practitioners with expertise in HCI, Responsible AI, Machine Learning, and Public Policy to critically discuss issues regarding accountability in algorithmic systems to reflect on potential opportunities for re-imagining scalable directions for accountability within these systems.
Original languageEnglish
Title of host publicationCHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
EditorsKaisa Väänänen, Tesh Goyal, Per Ola Kristensson, Anicia Peters
Place of PublicationNew York, NY, USA
Number of pages4
ISBN (Electronic)9781450394222
Publication statusPublished - 19 Apr 2023
EventACM CHI Conference on Human Factors in Computing Systems - Hamburg, Germany
Duration: 23 Apr 202328 Apr 2023


ConferenceACM CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI 2023
Internet address


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