Please Explain: Key Questions for Explainable AI research from an Organizational perspective

Ella Hafermalz; Marleen Huysman

doi:10.5771/2747-5174-2021-2-10

Contents
- Chapter Expand | CollapseAuthorsPage
- 1–2 Titelei 1–2
- 3–6 Editorial 3–6
- 7–8 Contributors 7–8
- 9–9 Content 9–9
- 10–23 Please Explain: Key Questions for Explainable AI research from an Organizational perspective Ella Hafermalz, Marleen Huysman Ella Hafermalz, Marleen Huysman 10–23
- 24–39 Deskilling, Upskilling, and Reskilling: a Case for Hybrid Intelligence Janet Rafner, Dominik Dellermann, Arthur Hjorth, Dóra Verasztó, Constance Kampf, Wendy Mackay, Jacob Sherson Janet Rafner, Dominik Dellermann, Arthur Hjorth, Dóra Verasztó, Constance Kampf, Wendy Mackay, Jacob Sherson 24–39
- 40–49 Piercing the Veil of Opacity: Responsibility and Liability for People Analytics Tools at the Workplace Isabelle Wildhaber, Isabel Ebert Isabelle Wildhaber, Isabel Ebert 40–49
- 50–59 Legal Theory and the Problem of Machine Opacity: Spinoza on Intentionality, Prediction and Law Moa De Lucia Dahlbeck Moa De Lucia Dahlbeck 50–59
- 60–69 Recommender Systems in the EU: from Responsibility to Regulation Sebastian Felix Schwemer Sebastian Felix Schwemer 60–69
- 70–77 From Attention-Grabbing to Calm Technology Reinhard Mehring Reinhard Mehring 70–77
- 78–85 Algorithms, Decision Making, and the Human Outside the Code Reinhard Mehring, Florian Krause Reinhard Mehring, Florian Krause 78–85
- 86–92 Collectively Reimagining Technology Andersen, Majken Overgaard, Mirabelle Jones, Irena Shklovski Andersen, Majken Overgaard, Mirabelle Jones, Irena Shklovski 86–92
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Morals & Machines , page 10 - 23

Please Explain: Key Questions for Explainable AI research from an Organizational perspective

Autoren

Ella Hafermalz Marleen Huysman

DOI
- doi.org/10.5771/2747-5174-2021-2-10
- ISSN: 2747-5174
- ISSN online: 2747-5182
Zusammenfassung

There is growing interest in explanations as an ethical and technical solution to the problem of 'opaque' AI systems. In this essay we point out that technical and ethical approaches to Explainable AI (XAI) have different assumptions and aims. Further, the organizational perspective is missing from this discourse. In response we formulate key questions for explainable AI research from an organizational perspective: 1) Who is the 'user' in Explainable AI? 2) What is the 'purpose' of an explanation in Explainable AI? and 3) Where does an explanation 'reside' in Explainable AI? Our aim is to prompt collaboration across disciplines working on Explainable AI.

Abstract

There is growing interest in explanations as an ethical and technical solution to the problem of 'opaque' AI systems. In this essay we point out that technical and ethical approaches to Explainable AI (XAI) have different assumptions and aims. Further, the organizational perspective is missing from this discourse. In response we formulate key questions for explainable AI research from an organizational perspective: 1) Who is the 'user' in Explainable AI? 2) What is the 'purpose' of an explanation in Explainable AI? and 3) Where does an explanation 'reside' in Explainable AI? Our aim is to prompt collaboration across disciplines working on Explainable AI.
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