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To engage or not to engage with AI for critical judgments : how professionals deal with opacity when using AI for medical diagnosis
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Lebovitz, Sarah, Lifshitz-Assaf, Hila and Levina, Natalia (2022) To engage or not to engage with AI for critical judgments : how professionals deal with opacity when using AI for medical diagnosis. Organization Science, 33 (1). pp. 126-148. doi:10.1287/orsc.2021.1549 ISSN 1047-7039.
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Official URL: http://dx.doi.org/10.1287/orsc.2021.1549
Abstract
rtificial intelligence (AI) technologies promise to transform how professionals conduct knowledge work by augmenting their capabilities for making professional judgments. We know little, however, about how human-AI augmentation takes place in practice. Yet, gaining this understanding is particularly important when professionals use AI tools to form judgments on critical decisions. We conducted an in-depth field study in a major U.S. hospital where AI tools were used in three departments by diagnostic radiologists making breast cancer, lung cancer, and bone age determinations. The study illustrates the hindering effects of opacity that professionals experienced when using AI tools and explores how these professionals grappled with it in practice. In all three departments, this opacity resulted in professionals experiencing increased uncertainty because AI tool results often diverged from their initial judgment without providing underlying reasoning. Only in one department (of the three) did professionals consistently incorporate AI results into their final judgments, achieving what we call engaged augmentation. These professionals invested in AI interrogation practices—practices enacted by human experts to relate their own knowledge claims to AI knowledge claims. Professionals in the other two departments did not enact such practices and did not incorporate AI inputs into their final decisions, which we call unengaged “augmentation.” Our study unpacks the challenges involved in augmenting professional judgment with powerful, yet opaque, technologies and contributes to literature on AI adoption in knowledge work.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) R Medicine > R Medicine (General) R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Social Sciences > Warwick Business School > Information Systems & Management Faculty of Social Sciences > Warwick Business School |
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| Library of Congress Subject Headings (LCSH): | Artificial intelligence , Artificial intelligence -- Medical applications , Medicine -- Data processing, Medical informatics, Diagnosis -- Decision making -- Data processing | ||||||||
| Journal or Publication Title: | Organization Science | ||||||||
| Publisher: | Institute for Operations Research and the Management Sciences (I N F O R M S) | ||||||||
| ISSN: | 1047-7039 | ||||||||
| Official Date: | 2022 | ||||||||
| Dates: |
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| Volume: | 33 | ||||||||
| Number: | 1 | ||||||||
| Page Range: | pp. 126-148 | ||||||||
| DOI: | 10.1287/orsc.2021.1549 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 10 October 2022 | ||||||||
| Date of first compliant Open Access: | 11 October 2022 |
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