A Survey on the Explainability of Supervised Machine Learning
Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions
Record details
Published: 19 January 2021
Source: OpenAlex
Category: Research
Topics: Healthcare · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (19 January 2021), “A Survey on the Explainability of Supervised Machine Learning,” evidence record 8877, https://ethics.ai/record/8877 (originally published by OpenAlex).
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