TY  - GEN 
T1  - What "counts" as explanation in social interaction?
A1  - Albert, Saul
A1  - Buschmeier, Hendrik
A1  - Cyra, Katharina
A1  - Even, Christiane
A1  - Hamann, Magnus
A1  - Licoppe, Christian
A1  - Mlynar, Jakub
A1  - Pelikan, Hannah
A1  - Porcheron, Martin
A1  - Reeves, Stuart
A1  - Rudaz, Damien
A1  - Sormani, Philippe
A1  - Tuncer, Sylvaine
Y1  - 2023
UR  - https://saulalbert.net/blog/what-counts-as-explanation-in-social-interaction/
KW  - AI
KW  - artificial intelligence
KW  - ethnomethodology and conversation analysis
KW  - explainability
KW  - Explainable AI
KW  - explainable AI (XAI)
KW  - explainable artificial intelligence
KW  - eXplainable Artificial Intelligence (XAI)
N2  - Measuring explainability in explainable AI (X-AI) usually involves technical methods for evaluating and auditing automated decision-making processes to highlight and eliminate potential sources of bias. By contrast, human practices of explaining usually involve doing explanation as a social action (Miller, 2019). X-AI’s transparent machine learning models can help to explain the proprietary ‘black boxes’ often used by high-stakes decision support systems in legal, financial, or diagnostic contexts (Rudin, 2019). However, as Rohlfing et al. (2021) point out, effective explanations (however technically accurate they may be), always involve processes of co-construction and mutual comprehension. Explanations usually involve at least two parties: the system and the user interacting with the system at a particular point in time, and ongoing contributions from both explainer and explainee are required. Without accommodating action, X-AI models appear to offer context-free, one-size-fits-all technical solutions that may not satisfy users’ expectations as to what constitutes a proper explanation.
ER  -