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Evidence record 1281 · automatically gathered

Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution

In high-stakes settings such as brand compliance, clinical care, and content moderation, machine learning cannot be deployed as opaque oracles: practitioners inspect the features driving model decisions, and models must leverage the expert documentation governing these domains. In practice, the data arrives as unstructured content, and features extracted from it must be interpretable, discriminative, and aligned with what experts consider important. Existing methods fall short: they target tabul

Record details

Published: 7 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Regulation · Healthcare
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.

Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

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Shared topic: Bias & fairness · Shared topic: Regulation

Constance Viehbeck

LSE Data Science Institute · 6 July 2026

How to cite this record

ethics.ai (7 June 2026), “Bridging Expert Knowledge and Automated Feature Engineering via Self-Evolution,” evidence record 1281, https://ethics.ai/record/1281 (originally published by arXiv).

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