# AI and Blackness: Towards moving beyond bias and representation

arXiv: https://arxiv.org/abs/2111.03687 · HTML: https://mlsanity.com/paper/2111.03687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 60 references that are in the map.
- First posted on arXiv 5 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [What are the biases in my word embedding?](https://mlsanity.com/paper/1812.08769/) (2018)
- [Diversity and Inclusion Metrics in Subset Selection](https://mlsanity.com/paper/2002.03256/) (2020)
- [The Criminality From Face Illusion](https://mlsanity.com/paper/2006.03895/) (2020)
- [Oxford Handbook on AI Ethics Book Chapter on Race and Gender](https://mlsanity.com/paper/1908.06165/) (2019)

## Built on by (most cited first, 4 of 4)

- [From Complexity to Clarity: How AI Enhances Perceptions of Scientists and the Public's Understanding of Science](https://mlsanity.com/paper/2405.00706/) (2024)
- [Using a cognitive architecture to consider antiBlackness in design and development of AI systems](https://mlsanity.com/paper/2207.00644/) (2022)
- [LLM-Guided Synthetic Augmentation (LGSA) for Mitigating Bias in AI Systems](https://mlsanity.com/paper/2510.13202/) (2025)
- [Learning a Vector-Symbolic Model for Socio-Cultural Tasks](https://mlsanity.com/paper/2608.02807/) (2026)

## Abstract (arXiv)

In this paper, we argue that AI ethics must move beyond the concepts of race-based representation and bias, and towards those that probe the deeper relations that impact how these systems are designed, developed, and deployed. Many recent discussions on ethical considerations of bias in AI systems have centered on racial bias. We contend that antiblackness in AI requires more of an examination of the ontological space that provides a foundation for the design, development, and deployment of AI systems. We examine what this contention means from the perspective of the sociocultural context in which AI systems are designed, developed, and deployed and focus on intersections with anti-Black racism (antiblackness). To bring these multiple perspectives together and show an example of antiblackness in the face of attempts at de-biasing, we discuss results from auditing an existing open-source semantic network (ConceptNet). We use this discussion to further contextualize antiblackness in design, development, and deployment of AI systems and suggest questions one may ask when attempting to combat antiblackness in AI systems.