# Should Machine Learning Models Report to Us When They Are Clueless?

arXiv: https://arxiv.org/abs/2203.12131 · HTML: https://mlsanity.com/paper/2203.12131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Learning in High Dimension Always Amounts to Extrapolation](https://mlsanity.com/paper/2110.09485/) (2021)
- [Learning Representations that Support Extrapolation](https://mlsanity.com/paper/2007.05059/) (2020)
- [Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models](https://mlsanity.com/paper/2112.03411/) (2021)

## Abstract (arXiv)

The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which describes the extent to which AI models can be clueless when they encounter unfamiliar samples (i.e., samples outside the convex hull of their training sets, as we will explain). We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency and accountability. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our analysis commentary accompanying practical clauses useful to include in AI regulations such as the National AI Initiative Act in the US and the AI Act by the European Commission.