# Automating Ambiguity: Challenges and Pitfalls of Artificial Intelligence

arXiv: https://arxiv.org/abs/2206.04179 · HTML: https://mlsanity.com/paper/2206.04179/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2022.

## Built on by (most cited first, 7 of 7)

- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learning](https://mlsanity.com/paper/2501.14959/) (2025)
- [Truth Machines: Synthesizing Veracity in AI Language Models](https://mlsanity.com/paper/2301.12066/) (2023)
- [Unequal Opportunities: Examining the Bias in Geographical Recommendations by Large Language Models](https://mlsanity.com/paper/2504.05325/) (2025)
- [Amplifying Limitations, Harms and Risks of Large Language Models](https://mlsanity.com/paper/2307.04821/) (2023)
- [From job titles to jawlines: Using context voids to study generative AI systems](https://mlsanity.com/paper/2504.13947/) (2025)
- [A Computational Audit of Demographic Association Encoding in ClinicalBERT Language Predictions](https://mlsanity.com/paper/2606.14460/) (2026)

## Abstract (arXiv)

Machine learning (ML) and artificial intelligence (AI) tools increasingly permeate every possible social, political, and economic sphere; sorting, taxonomizing and predicting complex human behaviour and social phenomena. However, from fallacious and naive groundings regarding complex adaptive systems to datasets underlying models, these systems are beset by problems, challenges, and limitations. They remain opaque and unreliable, and fail to consider societal and structural oppressive systems, disproportionately negatively impacting those at the margins of society while benefiting the most powerful. The various challenges, problems and pitfalls of these systems are a hot topic of research in various areas, such as critical data/algorithm studies, science and technology studies (STS), embodied and enactive cognitive science, complexity science, Afro-feminism, and the broadly construed emerging field of Fairness, Accountability, and Transparency (FAccT). Yet, these fields of enquiry often proceed in silos. This thesis weaves together seemingly disparate fields of enquiry to examine core scientific and ethical challenges, pitfalls, and problems of AI. In this thesis I, a) review the historical and cultural ecology from which AI research emerges, b) examine the shaky scientific grounds of machine prediction of complex behaviour illustrating how predicting complex behaviour with precision is impossible in principle, c) audit large scale datasets behind current AI demonstrating how they embed societal historical and structural injustices, d) study the seemingly neutral values of ML research and put forward 67 prominent values underlying ML research, e) examine some of the insidious and worrying applications of computer vision research, and f) put forward a framework for approaching challenges, failures and problems surrounding ML systems as well as alternative ways forward.