Good systems, bad data?: Interpretations of AI hype and failures. Issue 1 (22nd October 2020)

• Record Type: Journal Article
• Authors: Slota, Stephen C.; Fleischmann, Kenneth R.; Greenberg, Sherri; Verma, Nitin; Cummings, Brenna; Li, Lan; Shenefiel, Chris
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 57:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-22
• Subjects: critical infrastructure studies -- ethics of artificial intelligence -- media hype -- value‐sensitive design; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.275
• Languages: English
• ISSNs: 2373-9231

Abstract

Abstract: Artificial intelligence (AI), including machine learning (ML), is widely viewed as having substantial transformative potential across society, and novel implementations of these technologies promise new modes of living, working, and community engagement. Data and the algorithms that operate upon it thus operate under an expansive ethical valence, bearing consequence to both the development of these potentially transformative technologies and our understanding of how best to manage and support its impact. This paper reports upon an interview‐driven study of stakeholders engaged with technology development, policy, and law relating to AI. Among our participating stakeholders, unexpected outcomes and flawed implementations of AI, especially those leading to negative social consequences, are often attributed to ill‐structured, incomplete, or biased data, and the algorithms and interpretations that might produce negative social consequence are seen as neutrally representing the data, or otherwise blameless in that consequence. We propose a more complex infrastructural view of the tools, data, and operation of AI systems as necessary to the production of social good, and explore how representations of the successes and failures of these systems, even among experts, tend to valorize algorithmic analysis and locate fault at the quality of the data rather than the implementation of systems.