The Social Impact of Generative AI: An Analysis on ChatGPT
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Abstract (arXiv)
In recent months, the social impact of Artificial Intelligence (AI) has gained considerable public interest, driven by the emergence of Generative AI models, ChatGPT in particular. The rapid development of these models has sparked heated discussions regarding their benefits, limitations, and associated risks. Generative models hold immense promise across multiple domains, such as healthcare, finance, and education, to cite a few, presenting diverse practical applications. Nevertheless, concerns about potential adverse effects have elicited divergent perspectives, ranging from privacy risks to escalating social inequality. This paper adopts a methodology to delve into the societal implications of Generative AI tools, focusing primarily on the case of ChatGPT. It evaluates the potential impact on several social sectors and illustrates the findings of a comprehensive literature review of both positive and negative effects, emerging trends, and areas of opportunity of Generative AI models. This analysis aims to facilitate an in-depth discussion by providing insights that can inspire policy, regulation, and responsible development practices to foster a human-centered AI.
Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 9 of its 102 references that are in the map. First posted on arXiv 7 Mar 2024.
Builds on
9 of 102 references.
References · 9
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2019Release Strategies and the Social Impacts of Language Modelscited by 513
- 2023A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTcited by 224
- 2021Understanding the Capabilities, Limitations, and Societal Impact of Large Language Modelscited by 160
- 2023The Self-Perception and Political Biases of ChatGPTcited by 66
- 2023The Dark Side of ChatGPT: Legal and Ethical Challenges from Stochastic Parrots and Hallucinationcited by 23
- 2023She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Modelscited by 12
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Citing papers per year: 2024: 6; 2025: 10; 2026: 2.
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- 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluationcited by 42
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- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey3 Aug 2025
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- 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation7 Feb 2025
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making24 Jan 2025
- 2025Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot12 Jan 2025
6 more
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- 2024ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering17 Sep 2024
- 2024Acceptable Use Policies for Foundation Models29 Aug 2024
- 2024Risks and Opportunities of Open-Source Generative AI14 May 2024
- 2024New contexts, old heuristics: How young people in India and the US trust online content in the age of generative AI3 May 2024
- 2024StockGPT: A GenAI Model for Stock Prediction and Trading7 Apr 2024
- 2026CALIBURN: Self-Calibrated LLM Unlearning Alignmentcited by 2
- 2026CALIBURN: Self-Calibrated LLM Unlearning Alignment2 Feb 2026
- 2025IntrinsiX: High-Quality PBR Generation using Image Priorscited by 19
- 2024Risks and Opportunities of Open-Source Generative AIcited by 13
- 2024Generative AI Literacy: Twelve Defining Competenciescited by 13
- 2024Acceptable Use Policies for Foundation Modelscited by 12
- 2024StockGPT: A GenAI Model for Stock Prediction and Tradingcited by 11
- 2024ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answeringcited by 5
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluationscited by 4
- 2025AI Must not be Fully Autonomouscited by 3
- 2025Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilotcited by 2
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Makingcited by 2
- 2025Imposing AI: Deceptive design patterns against sustainabilitycited by 2
- 2024New contexts, old heuristics: How young people in India and the US trust online content in the age of generative AIcited by 1
12 most cited of 15 All 15 (JSON)
- 2026TRUST-ESD: A Risk-Calibrated and Governance-Aware AI Framework for Enterprise Strategic Decision Support Under Uncertainty22 Jul 2026
- 2025Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations6 Nov 2025
- 2025Imposing AI: Deceptive design patterns against sustainability12 Aug 2025
- 2025AI Must not be Fully Autonomous31 Jul 2025
- 2025Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems24 Jul 2025
- 2025Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era9 Jun 2025
- 2025IntrinsiX: High-Quality PBR Generation using Image Priors1 Apr 2025
- 2025Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making24 Jan 2025
- 2025Generative Artificial Intelligence-Supported Pentesting: A Comparison between Claude Opus, GPT-4, and Copilot12 Jan 2025
- 2024Generative AI Literacy: Twelve Defining Competencies29 Nov 2024
- 2024ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering17 Sep 2024
- 2024Acceptable Use Policies for Foundation Models29 Aug 2024
12 newest of 15 All 15 (JSON)