# Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning

arXiv: https://arxiv.org/abs/2005.06618 · HTML: https://mlsanity.com/paper/2005.06618/

## Key facts

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

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

- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [BIASeD: Bringing Irrationality into Automated System Design](https://mlsanity.com/paper/2210.01122/) (2022)
- [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024)
- [Multi-Objective Few-shot Learning for Fair Classification](https://mlsanity.com/paper/2110.01951/) (2021)

## Abstract (arXiv)

Human society had a long history of suffering from cognitive biases leading to social prejudices and mass injustice. The prevalent existence of cognitive biases in large volumes of historical data can pose a threat of being manifested as unethical and seemingly inhuman predictions as outputs of AI systems trained on such data. To alleviate this problem, we propose a bias-aware multi-objective learning framework that given a set of identity attributes (e.g. gender, ethnicity etc.) and a subset of sensitive categories of the possible classes of prediction outputs, learns to reduce the frequency of predicting certain combinations of them, e.g. predicting stereotypes such as `most blacks use abusive language', or `fear is a virtue of women'. Our experiments conducted on an emotion prediction task with balanced class priors shows that a set of baseline bias-agnostic models exhibit cognitive biases with respect to gender, such as women are prone to be afraid whereas men are more prone to be angry. In contrast, our proposed bias-aware multi-objective learning methodology is shown to reduce such biases in the predictied emotions.