# Emerging categories in scientific explanations

arXiv: https://arxiv.org/abs/2505.17832 · HTML: https://mlsanity.com/paper/2505.17832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 23 May 2025.

## Builds on (most cited first, 5 of 5)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Contrastive Explanations for Model Interpretability](https://mlsanity.com/paper/2103.01378/) (2021)
- [On the Diversity and Limits of Human Explanations](https://mlsanity.com/paper/2106.11988/) (2021)
- [A survey on improving NLP models with human explanations](https://mlsanity.com/paper/2204.08892/) (2022)

## Abstract (arXiv)

Clear and effective explanations are essential for human understanding and knowledge dissemination. The scope of scientific research aiming to understand the essence of explanations has recently expanded from the social sciences to machine learning and artificial intelligence. Explanations for machine learning decisions must be impactful and human-like, and there is a lack of large-scale datasets focusing on human-like and human-generated explanations. This work aims to provide such a dataset by: extracting sentences that indicate explanations from scientific literature among various sources in the biotechnology and biophysics topic domains (e.g. PubMed's PMC Open Access subset); providing a multi-class notation derived inductively from the data; evaluating annotator consensus on the emerging categories. The sentences are organized in an openly-available dataset, with two different classifications (6-class and 3-class category annotation), and the 3-class notation achieves a 0.667 Krippendorf Alpha value.