# AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps

arXiv: https://arxiv.org/abs/2403.14681 · HTML: https://mlsanity.com/paper/2403.14681/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 6 of 6)

- [Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR](https://mlsanity.com/paper/1711.00399/) (2017)
- [Dynabench: Rethinking Benchmarking in NLP](https://mlsanity.com/paper/2104.14337/) (2021)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [Discrimination in Online Ad Delivery](https://mlsanity.com/paper/1301.6822/) (2013)
- [User-oriented Fairness in Recommendation](https://mlsanity.com/paper/2104.10671/) (2021)
- [AI Ethics in Industry: A Research Framework](https://mlsanity.com/paper/1910.12695/) (2019)

## Built on by (most cited first, 2 of 2)

- [The AI Pentad, the CHARME2D Model, and an Assessment of Current-State AI Regulation](https://mlsanity.com/paper/2503.06353/) (2025)
- [e-person Architecture and Framework for Human-AI Co-adventure Relationship](https://mlsanity.com/paper/2503.22181/) (2025)

## Abstract (arXiv)

Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.