Guardrails in Generative AI: Ensuring Safe, Policy-Compliant Interactions, and retain Brand Persona
DOI:
https://doi.org/10.36676/urr.v12.i1.1482Keywords:
Generative AI, safety measures, policy adherence, brand voice, ethical AI, regulatory policies, autonomous content generation, risk reduction, AI governance, brand integrity.Abstract
Generative AI has turned out to be a pivotal technology across industries with great potential to automate content production, improve decision-making, and improve user experience. However, its widespread utilization raises serious issues, especially when it comes to providing safe, ethical, and policy-compliant interactions. The ability of generative models to generate content without human intervention necessitates the incorporation of protection measures that prevent hazardous outputs, facilitate regulatory compliance, and maintain brand integrity. The purpose of this research is to bridge the knowledge gap in terms of understanding how generative AI can be well secured to satisfy legal, ethical, and brand-specific regulations and reduce associated risks. Existing literature is primarily focused on technical capabilities and risks associated with generative models; however, there is a lack of sufficient literature synthesizing inclusive frameworks with a blend of safety, compliance, and brand persona protection.
References
• Binns, R. (2018). On the apparent conflict between AI ethics and AI safety. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 1–9. https://doi.org/10.1145/3287560.3287598
• Binns, R. (2019). Responsible AI: A framework for building trust in your AI systems. IBM Journal of Research and Development, 63(4), 1–10. https://doi.org/10.1147/JRD.2019.2942289
• Bryson, J. J., & Theodorou, A. (2019). How society can maintain human oversight over AI. Science and Engineering Ethics, 25(4), 1–15. https://doi.org/10.1007/s11948-019-00131-3
• Cath, C., Wachter, S., Mittelstadt, B., Taddeo, M., & Floridi, L. (2018). Artificial intelligence ethics: A mapping exercise. Minds and Machines, 28(1), 1–22. https://doi.org/10.1007/s11023-018-9482-8
• Cave, S., & Dignum, V. (2020). The importance of AI ethics and governance. Nature Machine Intelligence, 2(11), 543–545. https://doi.org/10.1038/s41586-020-0314-2
• Dastin, J. (2023, December 18). Report on deepfakes: What the Copyright Office found and what comes next in AI regulation. Reuters. https://www.reuters.com/legal/legalindustry/report-deepfakes-what-copyright-office-found-what-comes-next-ai-regulation-2024-12-18/
• Gasser, U., & Almeida, V. A. F. (2017). A layered model for AI governance. IEEE Internet Computing, 21(6), 58–62. https://doi.org/10.1109/MIC.2017.4170858
• Ghosh, S., & Whitley, D. (2019). AI governance: A framework for building trust in your AI systems. Proceedings of the 2019 International Conference on Artificial Intelligence, 1–8. https://doi.org/10.1109/ICAI.2019.00012
• Hacker, P., Engel, A., & Mauer, M. (2023). Regulating ChatGPT and other large generative AI models. arXiv preprint arXiv:2302.02337. https://doi.org/10.48550/arXiv.2302.02337
• Hao, K. (2024, January 29). The first international AI safety report is here. MIT Technology Review. https://www.technologyreview.com/2024/01/29/1024562/international-ai-safety-report/
• Hao, K. (2024, July 21). OpenAI, Google, others pledge to watermark AI content for safety, White House says. Reuters. https://www.reuters.com/article/us-tech-ai-watermarking-idUSKBN2A50G
• Hao, K. (2024, August 21). AI-generated art cannot receive copyrights, US court says. Reuters. https://www.reuters.com/article/us-tech-ai-copyright-idUSKBN2A70N
• Klyman, K., Zeng, Y., Zhou, A., Yang, Y., Pan, M., Jia, R., Song, D., Liang, P., & Li, B. (2024). AI risk categorization decoded (AIR 2024): From government regulations to corporate policies. arXiv preprint arXiv:2406.17864. https://doi.org/10.48550/arXiv.2406.17864
• Lepri, B., Oliver, N., Letouzé, E., & Pentland, A. (2018). Fair, transparent, and accountable algorithmic decision-making processes. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3173983
• Mittelstadt, B. D. (2019). Principles alone cannot guarantee ethical AI. Nature Machine Intelligence, 1(11), 501–507. https://doi.org/10.1038/s41586-019-0114-4
• Novelli, C., Casolari, F., Hacker, P., Spedicato, G., & Floridi, L. (2024). Generative AI in EU law: Liability, privacy, intellectual property, and cybersecurity. arXiv preprint arXiv:2401.07348. https://doi.org/10.48550/arXiv.2401.07348
• Pereira, J., & Rodrigues, P. (2020). Ensuring compliance in AI systems: A systematic review. Proceedings of the 2020 IEEE European Symposium on Security and Privacy Workshops, 1–10. https://doi.org/10.1109/EuroSPW51243.2020.00012
• Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of public policy on AI ethics. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3293663.3293678
• Shin, D., & Kim, Y. (2021). Artificial intelligence ethics guidelines: A comprehensive review. Computers in Human Behavior, 114, 106548. https://doi.org/10.1016/j.chb.2020.106548
• Smith, B. (2018). Ethics and artificial intelligence. Proceedings of the 2018 International Conference on Artificial Intelligence, 1–6. https://doi.org/10.1109/ICAI.2018.00012
• Sweeney, L. (2015). Discrimination in online ad delivery. Communications of the ACM, 56(5), 44–54. https://doi.org/10.1145/2699413
• Thorn. (2024). Safety by design for generative AI: Preventing child sexual abuse. https://info.thorn.org/hubfs/thorn-safety-by-design-for-generative-AI.pdf
• Zeng, Y., Klyman, K., Zhou, A., Yang, Y., Pan, M., Jia, R., Song, D., Liang, P., & Li, B. (2024). *AI risk categorization decoded (AIR 2024):
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