دورية أكاديمية

The explanation game: a formal framework for interpretable machine learning

التفاصيل البيبلوغرافية
العنوان: The explanation game: a formal framework for interpretable machine learning
المؤلفون: Watson, DS, Floridi, L
بيانات النشر: Springer Verlag
سنة النشر: 2020
المجموعة: Oxford University Research Archive (ORA)
الوصف: We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealised explanation game in which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterise the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.
نوع الوثيقة: article in journal/newspaper
اللغة: English
العلاقة: https://ora.ox.ac.uk/objects/uuid:a7492962-1053-47ce-b6f2-b02a5af1db5aTest; https://doi.org/10.1007/s11229-020-02629-9Test
DOI: 10.1007/s11229-020-02629-9
الإتاحة: https://doi.org/10.1007/s11229-020-02629-9Test
https://ora.ox.ac.uk/objects/uuid:a7492962-1053-47ce-b6f2-b02a5af1db5aTest
حقوق: info:eu-repo/semantics/openAccess ; CC Attribution (CC BY)
رقم الانضمام: edsbas.94FEAE95
قاعدة البيانات: BASE