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Evidence record 8338 · automatically gathered

Explaining Explanations: An Overview of Interpretability of Machine Learning

There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training

Record details

Published: 1 October 2018
Source: OpenAlex
Category: Research
Topics: Bias & fairness · Safety & alignment · Transparency
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (1 October 2018), “Explaining Explanations: An Overview of Interpretability of Machine Learning,” evidence record 8338, https://ethics.ai/record/8338 (originally published by OpenAlex).

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