Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping Study Article Swipe
Yutan Huang
,
Chetan Arora
,
Wen Cheng Huong
,
Tanjila Kanij
,
Anuradha Madulgalla
,
John Grundy
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.2003.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.2003.v1
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. 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. 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. 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. 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.2003.v1
- OA Status
- green
- Cited By
- 6
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4407037219Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.20944/preprints202501.2003.v1Digital Object Identifier
- Title
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Ethical Concerns of Generative AI and Mitigation Strategies: A Systematic Mapping StudyWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-28Full publication date if available
- Authors
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Yutan Huang, Chetan Arora, Wen Cheng Huong, Tanjila Kanij, Anuradha Madulgalla, John GrundyList of authors in order
- Landing page
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https://doi.org/10.20944/preprints202501.2003.v1Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.20944/preprints202501.2003.v1Direct OA link when available
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Corporate governance, Context (archaeology), Generative grammar, Engineering ethics, Adaptability, Political science, Business, Computer science, Engineering, Geography, Management, Artificial intelligence, Economics, Archaeology, FinanceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.context-dependent. | 130 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.75 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.95343942 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |