# Bias and Discrimination in AI: a cross-disciplinary perspective

**Authors:** Xavier Ferrer, Tom van Nuenen, Jose M. Such, Mark Coté, Natalia Criado  
**arXiv:** [2008.07309](https://arxiv.org/abs/2008.07309)  
**Date:** 2020-08-11  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2008.07309v1/

## Key points

- Authors Xavier Ferrer, Tom van Nuenen, Jose M. Such, Mark Coté, and Natalia Criado address bias and discrimination in AI
- Highlight the consequence of discrimination based on specific characteristics
- Advocate for an interdisciplinary approach to address bias and discrimination
- Emphasize the importance of cross-disciplinary collaborations in finding sustainable solutions
- Call to action for researchers, policymakers, and practitioners to work together for fairer and more equitable AI technologies

## Summary

In their paper titled "Bias and Discrimination in AI: a cross-disciplinary perspective," authors Xavier Ferrer, Tom van Nuenen, Jose M. Such, Mark Coté, and Natalia Criado address the pressing issue of bias and discrimination in automated decision-making systems. As AI technology becomes more prevalent in society, the negative implications of bias are becoming increasingly apparent. One significant consequence highlighted by the authors is discrimination based on specific characteristics. The paper emphasizes that while the connection between bias and discrimination is evident, addressing it is not always straightforward. To effectively tackle this complex issue, the authors advocate for an interdisciplinary approach that incorporates technical, legal, social, and ethical perspectives. By surveying relevant literature from these diverse fields, they underscore the importance of robust cross-disciplinary collaborations in finding sustainable solutions to mitigate bias and discrimination in AI systems. Through their comprehensive analysis , Ferrer et al. shed light on the critical need for a holistic understanding  in AI . Their work serves as a call to action for researchers, policymakers, and practitioners to work together across disciplines to create fairer and more equitable AI technologies that uphold principles of justice and equality for all individuals.