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

TL;DR

This paper reviews recent research on AI in the public sector, focusing on NLP and Text Mining, highlighting key concerns like fairness, trustworthiness, and privacy, and providing insights into ethical and practical issues.

Contribution

It offers a systematic review of recent literature on AI concerns in the public sector supported by NLP and TM, emphasizing the prominence of fairness and trustworthiness.

Findings

Fairness was the most frequent concern.

Trustworthiness was the most prominent topic.

Data privacy was less emphasized but embedded in most articles.

Abstract

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.