# Person Perception Biases Exposed: Revisiting the First Impressions Dataset

arXiv: https://arxiv.org/abs/2011.14906 · HTML: https://mlsanity.com/paper/2011.14906/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Lost in Transcription: Identifying and Quantifying the Accuracy Biases of Automatic Speech Recognition Systems Against Disfluent Speech](https://mlsanity.com/paper/2405.06150/) (2024)
- [Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding](https://mlsanity.com/paper/2112.12180/) (2021)
- [Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problems](https://mlsanity.com/paper/2202.00993/) (2022)
- [Behind the Screens: Uncovering Bias in AI-Driven Video Interview Assessments Using Counterfactuals](https://mlsanity.com/paper/2505.12114/) (2025)
- [AI Competitions and Benchmarks: Dataset Development](https://mlsanity.com/paper/2404.09703/) (2024)

## Abstract (arXiv)

This work revisits the ChaLearn First Impressions database, annotated for personality perception using pairwise comparisons via crowdsourcing. We analyse for the first time the original pairwise annotations, and reveal existing person perception biases associated to perceived attributes like gender, ethnicity, age and face attractiveness. We show how person perception bias can influence data labelling of a subjective task, which has received little attention from the computer vision and machine learning communities by now. We further show that the mechanism used to convert pairwise annotations to continuous values may magnify the biases if no special treatment is considered. The findings of this study are relevant for the computer vision community that is still creating new datasets on subjective tasks, and using them for practical applications, ignoring these perceptual biases.