Aversion vs. Abstinence: Conceptual Distinctions for the Receptivity Toward Algorithmic Decision-Making Systems Within Value-laden Contexts
Whilst algorithmic decision-making systems (ADMS) become increasingly pertinent across several contexts, many remain reluctant to adopt such systems, preferring human alternatives – often explored as “Algorithm Aversion”. However, the associated literature primarily frames this tendency in a utility-focused fashion, based on users’ perceptions of efficacy or accuracy. This framing offers a narrow scope of “aversion” that neglects emotional and experiential elements that may be at play, as well as especially prominent in “value-laden contexts” (e.g., medicine). This study uses an inductive approach to identifying various concepts and themes emerging from open-ended responses to the potential use of a future ADMS in such a context. Different reactions (both reluctant and receptive) of ADMS are then discussed and offered conceptual distinctions that may inform future examinations of the resulting biases. In doing so, we start to respond to the call for qualitative research examining the underlying motives related to Algorithm Aversion.
Hannon, Oliver; Gal, Uri; and Dar-Nimrod, Ilan, "Aversion vs. Abstinence: Conceptual Distinctions for the Receptivity Toward Algorithmic Decision-Making Systems Within Value-laden Contexts" (2022). ACIS 2022 Proceedings. 43.