{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:46:15Z","timestamp":1777455975675,"version":"3.51.4"},"reference-count":43,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T00:00:00Z","timestamp":1707004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p>The rapid progress of Artificial intelligence in generative modeling is marred by widespread misuse. In response, researchers turn to use-based restrictions\u2014contractual terms prohibiting certain uses\u2014as a \u201csolution\u201d for abuse. While these restrictions can be beneficial to artificial intelligence governance in API-gated settings, their failings are especially significant in open-source models: not only do they lack any means of enforcement, but they also perpetuate the current proliferation of tokenistic efforts toward ethical artificial intelligence. This observation echoes growing literature that points to useless efforts in \u201cAI ethics,\u201d and underscores the need to shift from this paradigm. This article provides an overview of these drawbacks and argues that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.<\/jats:p>","DOI":"10.1177\/20539517241229699","type":"journal-article","created":{"date-parts":[[2024,2,5]],"date-time":"2024-02-05T01:56:09Z","timestamp":1707098169000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Rethinking use-restricted open-source licenses for regulating abuse of generative models"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5075-3524","authenticated-orcid":false,"given":"Jonathan","family":"Cui","sequence":"first","affiliation":[{"name":"Penn State University, University Park, PA, USA"}],"role":[{"role":"author","vocab":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3321-9195","authenticated-orcid":false,"given":"David A","family":"Araujo","sequence":"additional","affiliation":[{"name":"Penn State University, Harrisburg, PA, USA"}],"role":[{"role":"author","vocab":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,2,4]]},"reference":[{"key":"bibr1-20539517241229699","unstructured":"Bai Y, Jones A, Ndousse K, et al. (2022) Training a helpful and harmless assistant with reinforcement learning from human feedback.\n                      arXiv preprint arXiv:2204.05862\n                      ."},{"key":"bibr2-20539517241229699","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.bigscience-1.9"},{"key":"bibr3-20539517241229699","first-page":"1877","volume":"33","author":"Brown T","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bibr4-20539517241229699","unstructured":"Chen M, Tworek J, Jun H, et al. (2021) Evaluating large language models trained on code.\n                      arXiv preprint arXiv:2107.03374\n                      ."},{"key":"bibr5-20539517241229699","unstructured":"Clark K, Luong MT, Le QV, et al. (2020) ELECTRA: Pre-training text encoders as discriminators rather than generators.\n                      arXiv preprint arXiv:2003.10555\n                      ."},{"key":"bibr6-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533143"},{"key":"bibr7-20539517241229699","unstructured":"Devlin J, Chang MW, Lee K, et al. (2018) BERT: Pre-training of deep bidirectional transformers for language understanding.\n                      arXiv preprint arXiv:1810.04805\n                      ."},{"key":"bibr8-20539517241229699","unstructured":"Dong Y, Jiang X, Jin Z, et al. (2023) Self-collaboration code generation via ChatGPT.\n                      arXiv preprint arXiv:2304.07590\n                      ."},{"key":"bibr9-20539517241229699","unstructured":"Ferrandis CM (2022) OpenRAIL: Towards open and responsible AI licensing frameworks. https:\/\/huggingface.co\/blog\/open_rail"},{"key":"bibr10-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1109\/JICV53222.2021.9600272"},{"key":"bibr11-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1109\/ICME51207.2021.9428429"},{"key":"bibr12-20539517241229699","unstructured":"Grinbaum A, Adomaitis L (2022) The ethical need for watermarks in machine-generated language.\n                      arXiv preprint arXiv:2209.03118\n                      ."},{"key":"bibr13-20539517241229699","unstructured":"Gu C, Huang C, Zheng X, et al. (2022) Watermarking pre-trained language models with backdooring.\n                      arXiv preprint arXiv:2210.07543\n                      ."},{"key":"bibr14-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-020-09517-8"},{"key":"bibr15-20539517241229699","unstructured":"Jiang AQ, Sablayrolles A, Mensch A, et al. (2023) Mistral 7B.\n                      arXiv preprint arXiv:2310.06825\n                      ."},{"key":"bibr16-20539517241229699","unstructured":"Kilcher Y (2022) GPT-4chan: This is the worst AI ever. https:\/\/www.youtube.com\/watch?v=efPrtcLdcdM."},{"key":"bibr17-20539517241229699","unstructured":"Kirchenbauer J, Geiping J, Wen Y, et al. (2023) A watermark for large language models.\n                      arXiv preprint arXiv:2301.10226\n                      ."},{"key":"bibr18-20539517241229699","unstructured":"Lee K, Liu H, Ryu M, et al. (2023) Aligning text-to-image models using human feedback.\n                      arXiv preprint arXiv:2302.12192\n                      ."},{"key":"bibr19-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-022-00209-w"},{"key":"bibr20-20539517241229699","unstructured":"Nguyen HT (2023) A brief report on LawGPT 1.0: A virtual legal assistant based on GPT-3.\n                      arXiv preprint arXiv:2302.05729\n                      ."},{"key":"bibr21-20539517241229699","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.441"},{"key":"bibr22-20539517241229699","first-page":"27730","volume":"35","author":"Ouyang L","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bibr23-20539517241229699","unstructured":"Paul K (2023) Black workers accused Tesla of racism for years. now california is stepping in. https:\/\/www.theguardian.com\/technology\/2022\/feb\/18\/tesla-california-racial-harassment-discrimination-lawsuit"},{"key":"bibr24-20539517241229699","unstructured":"Poritz I (2022) Microsoft, OpenAI, GitHub face copyright suit over coding tool. https:\/\/news.bloomberglaw.com\/privacy-and-data-security\/microsoft-openai-github-face-copyright-suit-over-coding-tool"},{"issue":"8","key":"bibr25-20539517241229699","first-page":"9","volume":"1","author":"Radford A","year":"2019","journal-title":"OpenAI blog"},{"issue":"1","key":"bibr26-20539517241229699","first-page":"5485","volume":"21","author":"Raffel C","year":"2020","journal-title":"The Journal of Machine Learning Research"},{"key":"bibr27-20539517241229699","unstructured":"Ramesh A, Pavlov M, Goh G, et al. (2021) Zero-shot text-to-image generation. In:\n                      International Conference on Machine Learning\n                      . PMLR, pp. 8821\u20138831."},{"key":"bibr28-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1177\/2053951720942541"},{"key":"bibr29-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"bibr30-20539517241229699","unstructured":"Rombach R, Esser P (2022) CreativeML Open RAIL-M. https:\/\/huggingface.co\/spaces\/CompVis\/stable-diffusion-license"},{"key":"bibr31-20539517241229699","unstructured":"Roose K (2022) An A.I.generated picture won an art prize. artists aren\u2019t happy. https:\/\/www.nytimes.com\/2022\/09\/02\/technology\/ai-artificial-intelligence-artists.html"},{"key":"bibr32-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1002\/gamm.202100008"},{"key":"bibr33-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-52737-x"},{"key":"bibr34-20539517241229699","unstructured":"Scao TL, Fan A, Akiki C, et al. (2022) BLOOM: A 176B-parameter open-access multilingual language model.\n                      arXiv preprint arXiv:2211.05100\n                      ."},{"key":"bibr35-20539517241229699","unstructured":"Touvron H, Lavril T, Izacard G, et al. (2023a) LLaMA: Open and efficient foundation language models.\n                      arXiv preprint arXiv:2302.13971\n                      ."},{"key":"bibr36-20539517241229699","unstructured":"Touvron H, Martin L, Stone K, et al. (2023b) Llama 2: Open foundation and fine-tuned chat models.\n                      arXiv preprint arXiv:2307.09288\n                      ."},{"key":"bibr37-20539517241229699","volume-title":"Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech","author":"Wachter-Boettcher S","year":"2017"},{"key":"bibr38-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533779"},{"key":"bibr39-20539517241229699","unstructured":"Wiggers K (2022) Deepfakes for all: Uncensored AI art model prompts ethics questions. https:\/\/techcrunch.com\/2022\/08\/24\/deepfakes-for-all-uncensored-ai-art-model-prompts-ethics-questions"},{"key":"bibr40-20539517241229699","doi-asserted-by":"publisher","DOI":"10.1080\/01900692.2020.1749851"},{"key":"bibr41-20539517241229699","first-page":"5753","volume":"32","author":"Yang Z","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bibr42-20539517241229699","unstructured":"Zhang S, Roller S, Goyal N, et al. (2022) OPT: Open pre-trained transformer language models.\n                      arXiv preprint arXiv:2205.01068\n                      ."},{"key":"bibr43-20539517241229699","unstructured":"Ziegler DM, Stiennon N,  & Wu J\n                      \n                        ,\n                        et al\n                      \n                      . (2019) Fine-tuning language models from human preferences.\n                      arXiv preprint arXiv:1909.08593\n                      ."}],"container-title":["Big Data &amp; Society"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20539517241229699","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20539517241229699","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20539517241229699","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:00:42Z","timestamp":1777381242000},"score":1,"resource":{"primary":{"URL":"http:\/\/journals.sagepub.com\/doi\/10.1177\/20539517241229699"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,4]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1177\/20539517241229699"],"URL":"https:\/\/doi.org\/10.1177\/20539517241229699","relation":{},"ISSN":["2053-9517","2053-9517"],"issn-type":[{"value":"2053-9517","type":"print"},{"value":"2053-9517","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,4]]},"article-number":"20539517241229699"}}