# A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM

arXiv: https://arxiv.org/abs/2308.02042 · HTML: https://mlsanity.com/paper/2308.02042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 67 references that are in the map.
- First posted on arXiv 25 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Outsider Oversight: Designing a Third Party Audit Ecosystem for AI Governance](https://mlsanity.com/paper/2206.04737/) (2022)
- [How Different Groups Prioritize Ethical Values for Responsible AI](https://mlsanity.com/paper/2205.07722/) (2022)
- [Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects](https://mlsanity.com/paper/2206.06738/) (2022)
- [Towards Accountability in the Use of Artificial Intelligence for Public Administrations](https://mlsanity.com/paper/2105.01434/) (2021)
- [Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models](https://mlsanity.com/paper/2206.09875/) (2022)
- [Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy](https://mlsanity.com/paper/2112.06833/) (2021)
- [Transparency, Governance and Regulation of Algorithmic Tools Deployed in the Criminal Justice System: a UK Case Study](https://mlsanity.com/paper/2205.15258/) (2022)
- [Prioritizing Policies for Furthering Responsible Artificial Intelligence in the United States](https://mlsanity.com/paper/2212.00740/) (2022)
- [A Framework for Understanding AI-Induced Field Change: How AI Technologies are Legitimized and Institutionalized](https://mlsanity.com/paper/2108.07804/) (2021)

## Abstract (arXiv)

Artificial Intelligence is not a new subject, and business, industry and public sectors have used it in different ways and contexts and considering multiple concerns. This work reviewed research papers published in ACM Digital Library and IEEE Xplore conference proceedings in the last two years supported by fundamental concepts of Natural Language Processing (NLP) and Text Mining (TM). The objective was to capture insights regarding data privacy, ethics, interpretability, explainability, trustworthiness, and fairness in the public sector. The methodology has saved analysis time and could retrieve papers containing relevant information. The results showed that fairness was the most frequent concern. The least prominent topic was data privacy (although embedded in most articles), while the most prominent was trustworthiness. Finally, gathering helpful insights about those concerns regarding A.I. applications in the public sector was also possible.