The explanation game: a formal framework for interpretable machine learning
By David Watson, Luciano Floridi. 2020
68 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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Citations in the corpus
- Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward (2020, background): “To this end, Watson and Floridi (2019) de fi ned a formal framework for interpretable ML, where explanatory accuracy can be assessed against algorithmic simplicity and relevance.”