The explanation game: a formal framework for interpretable machine learning

Synthese 198 (10):9211-9242 (2021)
  Copy   BIBTEX

Abstract

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealisedexplanation gamein 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.

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 90,616

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

The Logic of Learning.Christian Bennet - 2019 - Axiomathes 29 (2):173-187.
A Conceptual Framework Over Contextual Analysis of Concept Learning Within Human-Machine Interplays.Farshad Badie - 2017 - In Emerging Technologies for Education. Cham, Switzerland: pp. 65-74.
Concept Representation Analysis in the Context of Human-Machine Interactions.Farshad Badie - 2016 - In 14th International Conference on e-Society. pp. 55-61.
What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2021 - Philosophy and Technology 34:833–862.
Game logic and its applications I.Mamoru Kaneko & Takashi Nagashima - 1996 - Studia Logica 57 (2-3):325 - 354.

Analytics

Added to PP
2021-09-23

Downloads
23 (#584,666)

6 months
8 (#158,054)

Historical graph of downloads
How can I increase my downloads?

Author Profiles

David Watson
University College London
Luciano Floridi
Yale University