# Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study

arXiv: https://arxiv.org/abs/2502.00015 · HTML: https://mlsanity.com/paper/2502.00015/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 72 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Fairness in Machine Learning: Lessons from Political Philosophy](https://mlsanity.com/paper/1712.03586/) (2017)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Understanding artificial intelligence ethics and safety](https://mlsanity.com/paper/1906.05684/) (2019)
- [Advancing Requirements Engineering through Generative AI: Assessing the Role of LLMs](https://mlsanity.com/paper/2310.13976/) (2023)
- [Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers](https://mlsanity.com/paper/2311.10242/) (2023)
- [Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives](https://mlsanity.com/paper/2407.14962/) (2024)
- [Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Survey](https://mlsanity.com/paper/2406.05392/) (2024)
- [Global AI Ethics: A Review of the Social Impacts and Ethical Implications of Artificial Intelligence](https://mlsanity.com/paper/1907.07892/) (2019)
- [Ethics of AI: A Systematic Literature Review of Principles and Challenges](https://mlsanity.com/paper/2109.07906/) (2021)
- [Large Language Model (LLM) Bias Index -- LLMBI](https://mlsanity.com/paper/2312.14769/) (2023)
- [AI Ethics: An Empirical Study on the Views of Practitioners and Lawmakers](https://mlsanity.com/paper/2207.01493/) (2022)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [Implementing AI Ethics in Practice: An Empirical Evaluation of the RESOLVEDD Strategy](https://mlsanity.com/paper/2004.10191/) (2020)

## Built on by (most cited first, 1 of 1)

- [Approaches to Responsible Governance of GenAI in Organizations](https://mlsanity.com/paper/2504.17044/) (2025)

## Abstract (arXiv)

[Context] Generative AI technologies, particularly Large Language Models (LLMs), have transformed numerous domains by enhancing convenience and efficiency in information retrieval, content generation, and decision-making processes. However, deploying LLMs also presents diverse ethical challenges, and their mitigation strategies remain complex and domain-dependent. [Objective] This paper aims to identify and categorize the key ethical concerns associated with using LLMs, examine existing mitigation strategies, and assess the outstanding challenges in implementing these strategies across various domains. [Method] We conducted a systematic mapping study, reviewing 39 studies that discuss ethical concerns and mitigation strategies related to LLMs. We analyzed these ethical concerns using five ethical dimensions that we extracted based on various existing guidelines, frameworks, and an analysis of the mitigation strategies and implementation challenges. [Results] Our findings reveal that ethical concerns in LLMs are multi-dimensional and context-dependent. While proposed mitigation strategies address some of these concerns, significant challenges still remain. [Conclusion] Our results highlight that ethical issues often hinder the practical implementation of the mitigation strategies, particularly in high-stake areas like healthcare and public governance; existing frameworks often lack adaptability, failing to accommodate evolving societal expectations and diverse contexts.