{"title":"Evaluation Principles","doi":"10.1007/978-981-96-5290-7_26","date_updated":"2026-03-23T08:47:38Z","publisher":"Springer Nature Singapore","date_created":"2026-03-23T08:47:10Z","author":[{"id":"72497","full_name":"Thommes, Kirsten","last_name":"Thommes","first_name":"Kirsten"}],"year":"2026","place":"Singapore","citation":{"short":"K. Thommes, in: Social Explainable AI, Springer Nature Singapore, Singapore, 2026.","bibtex":"@inbook{Thommes_2026, place={Singapore}, title={Evaluation Principles}, DOI={<a href=\"https://doi.org/10.1007/978-981-96-5290-7_26\">10.1007/978-981-96-5290-7_26</a>}, booktitle={Social Explainable AI}, publisher={Springer Nature Singapore}, author={Thommes, Kirsten}, year={2026} }","mla":"Thommes, Kirsten. “Evaluation Principles.” <i>Social Explainable AI</i>, Springer Nature Singapore, 2026, doi:<a href=\"https://doi.org/10.1007/978-981-96-5290-7_26\">10.1007/978-981-96-5290-7_26</a>.","apa":"Thommes, K. (2026). Evaluation Principles. In <i>Social Explainable AI</i>. Springer Nature Singapore. <a href=\"https://doi.org/10.1007/978-981-96-5290-7_26\">https://doi.org/10.1007/978-981-96-5290-7_26</a>","chicago":"Thommes, Kirsten. “Evaluation Principles.” In <i>Social Explainable AI</i>. Singapore: Springer Nature Singapore, 2026. <a href=\"https://doi.org/10.1007/978-981-96-5290-7_26\">https://doi.org/10.1007/978-981-96-5290-7_26</a>.","ieee":"K. Thommes, “Evaluation Principles,” in <i>Social Explainable AI</i>, Singapore: Springer Nature Singapore, 2026.","ama":"Thommes K. Evaluation Principles. In: <i>Social Explainable AI</i>. Springer Nature Singapore; 2026. doi:<a href=\"https://doi.org/10.1007/978-981-96-5290-7_26\">10.1007/978-981-96-5290-7_26</a>"},"publication_status":"published","publication_identifier":{"isbn":["9789819652891","9789819652907"]},"language":[{"iso":"eng"}],"project":[{"name":"TRR 318 - Subproject A3","_id":"113"},{"_id":"125","name":"TRR 318 - Subproject C2"}],"_id":"65089","user_id":"72497","department":[{"_id":"178"},{"_id":"184"}],"abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title>\r\n                  <jats:p>In the past, there has been much research aiming to evaluate XAI practices—that is, explanations that can add to a user’s understanding of “why” or “why not.” However, because there is such a huge amount of diversity in social contexts, optimizing for the mean neglects the social dimensions of to whom, what, why, when, and where explanations are provided. Nonetheless, these dimensions matter. We give some brief examples on the accuracy of the mental model (as an example for who?), on measuring explanation practices (as an example of what?), on human motivation (as an example of why?), on repeated interactions (as an example of when), and on bystander effects (as an example of where?). Importantly, controlling for these factors (or randomizing them) is as important as attempting to perform external validations.</jats:p>"}],"status":"public","type":"book_chapter","publication":"Social Explainable AI"}