Explainability for experts: A design framework for making algorithms supporting expert decisions more explainable
| Autor: | Bronwyn Jones, Rhianne Jones, Auste Simkute, Ewa Luger, Michael Evans |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Design framework
Decision support system business.industry Management science Computer science Mechanism (biology) Journalism Human-in-the-loop journalism Sensemaking Expertise Information technology Decision support systems Explainability T58.5-58.64 human-in-the-loop Domain (software engineering) explainability Health care Agency (sociology) expertise business decision support systems |
| Zdroj: | Journal of Responsible Technology, Vol 7, Iss, Pp 100017-(2021) Simkute, A, Luger, E, Jones, B, Evans, M & Jones, R 2021, ' Explainability for experts : A design framework for making algorithms supporting expert decisions more explainable ', Journal of Responsible Technology, vol. 7-8, 100017 . https://doi.org/10.1016/j.jrt.2021.100017 |
| ISSN: | 2666-6596 |
| Popis: | Algorithmic decision support systems are widely applied in domains ranging from healthcare to journalism. To ensure that these systems are fair and accountable, it is essential that humans can maintain meaningful agency, understand and oversee algorithmic processes. Explainability is often seen as a promising mechanism for enabling human-in-the-loop, however, current approaches are ineffective and can lead to various biases. We argue that explainability should be tailored to support naturalistic decision-making and sensemaking strategies employed by domain experts and novices. Based on cognitive psychology and human factors literature review we map potential decision-making strategies dependent on expertise, risk and time dynamics and propose the conceptual Expertise, Risk and Time Explainability Framework, intended to be used as explainability design guidelines. Finally, we present a worked example in journalism to illustrate the applicability of our framework in practice. |
| Databáze: | OpenAIRE |
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