An Axiomatic Approach to Explain Computer Generated Decisions
Martin Strobel · 2018
Recent years have seen the widespread implementation of data-driven algorithms making decisions in increasingly highstakes domains, such as finance, healthcare, transportation and public safety. Using novel ML techniques, these algorithms are able to process massive amounts of data and make highly accurate predictions; however, their inherent complexity makes it increasingly difficult for humans to understand why certain decisions were made. Indeed, these algorithms are black-box decision makers: their underlying decision processes are either hidden from human scrutiny by proprietary law, or (as is often the case) their inner workings are so complicated that even their own designers will be hard-pressed to explain the underlying reasoning behind their decision making processes. By obfuscating their function, data-driven classifiers run the risk of exposing human stakeholders to risks. These may include incorrect decisions (e.g. a loan application that was wrongly rejected due to system error), information leaks (e.g. an algorithm inadvertently uses information it should not have used), or discrimination (e.g. biased decisions against certain ethnic or gender groups). Government bodies and regulatory authorities have recently begun calling for algorithmic transparency: providing human-interpretable explanations of the underlying reasoning behind large-scale decision making algorithms. My thesis research will be concerned with an axiomatic analysis of automatically generated explanations of such classifiers. Especially, I'm interested in how to decide which explanation of a decision to trust given that there are many, potentially conflicting, possible explanations for any given decision.