Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare

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Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare
  • Russell, S. Ai weapons: Russia’s war in Ukraine shows why the world must enact a ban. Nature (2023).

  • U.S. Department of Defense. Dod adopts ethical principles for artificial intelligence (2020).

  • The North Atlantic Treaty Organization. Summary of the NATO artificial intelligence strategy (2021).

  • Hicks, K. What the Pentagon thinks about artificial intelligence. Politico https://www.politico.com/news/magazine/2023/06/15/pentagon-artificial-intelligence-china-00101751.

  • Baker, A. et al. A comparison of artificial intelligence and human doctors for the purpose of triage and diagnosis. Front Artif. Intell. 3, 543405 (2020).

  • Chan, S. & Siegel, E. L. Will machine learning end the viability of radiology as a thriving medical specialty? Br. J. Radiol. 92, 20180416 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Meyer, J. et al. Impact of artificial intelligence on pathologists’ decisions: an experiment. J. Am. Med. Inform. Assoc. 29, 1688–1695 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Langlotz, C. P. Will artificial intelligence replace radiologists? Radiol. Artif. Intell. 1, e190058 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Cacciamani, G. E. et al. Is artificial intelligence replacing our radiology stars? not yet! Eur. Urol. Open Sci. 48, 14–16 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Yang, X. et al. A large language model for electronic health records. npj Digit. Med. 5, 194 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lin, W.-C., Chen, J. S., Chiang, M. F. & Hribar, M. R. Applications of artificial intelligence to electronic health record data in ophthalmology. Transl. Vis. Sci. Technol. 9, 13–13 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rosenthal, S., Barker, K. & Liang, Z. Leveraging medical literature for section prediction in electronic health records. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 4864–4873 (Association for Computational Linguistics, Hong Kong, China, 2019).

  • Singhal, K. et al. Large language models encode clinical knowledge. Nature 620, 172–180 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Organization, T. W. H. Ethics and governance of artificial intelligence for health (2021).

  • Dowling, M. & Lucey, B. Chatgpt for (finance) research: the Bananarama conjecture. Finance Res. Lett. 53, 103662 (2023).

    Article 

    Google Scholar 

  • Lee, M., Liang, P. & Yang, Q. Coauthor: designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, CHI ’22 (Association for Computing Machinery, New York, NY, USA, 2022). https://doi.org/10.1145/3491102.3502030.

  • Thiergart, J., Huber, S. & Übellacker, T. Understanding emails and drafting responses—an approach using gpt-3 (2021). Preprint at https://arxiv.org/abs/2102.03062.

  • Ranade, P., Piplai, A., Mittal, S., Joshi, A. & Finin, T. Generating fake cyber threat intelligence using transformer-based models. In 2021 International Joint Conference on Neural Networks (IJCNN), 1–9 (2021).

  • Liao, W. et al. Differentiate chatgpt-generated and human-written medical texts (2023). Preprint at https://arxiv.org/abs/2304.11567.

  • Chintagunta, B., Katariya, N., Amatriain, X. & Kannan, A. Medically aware GPT-3 as a data generator for medical dialogue summarization. In Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations, (eds Shivade, C. et al.) 66–76 (Association for Computational Linguistics, Online, 2021). https://aclanthology.org/2021.nlpmc-1.9.

  • Sun, Z. et al. Evaluating GPT4 on impressions generation in radiology reports. Radiology 307, e231259 (2023).

    Article 
    PubMed 

    Google Scholar 

  • Peng, Y., Rousseau, J. F., Shortliffe, E. H. & Weng, C. AI-generated text may have a role in evidence-based medicine. Nat. Med. (2023).

  • Gilbert, T. K., Brozek, M. W. & Brozek, A. Beyond bias and compliance: Towards individual agency and plurality of ethics in AI (2023). Preprint at https://arxiv.org/abs/2302.12149.

  • Birhane, A. et al. The forgotten margins of ai ethics. In 2022 ACM Conference on Fairness, Accountability, and Transparency (ACM, 2022).

  • OpenAI. Introducing chatgpt (2022).

  • Hu, K. Chatgpt sets record for fastest-growing user base – analyst note. Reuters https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/.

  • OpenAI. Model index for researchers https://platform.openai.com/docs/model-index-for-researchers.

  • OpenAI. Gpt-4 technical report (2023). Preprint at https://arxiv.org/abs/2303.08774.

  • Radford, A., Narasimhan, K., Salimans, T. & Sutskever, I. Improving language understanding by generative pre-training. (2018).

  • Radford, A. et al. Language models are unsupervised multitask learners (2019).

  • Brown, T. et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems, Vol. 33 (eds Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. & Lin, H.) 1877–1901 (Curran Associates, Inc., 2020). https://proceedings.neurips.cc/paper_files/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf.

  • Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems, Vol. 30 (eds Guyon, I. et al.) (Curran Associates, Inc., 2017). https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.

  • Rombach, R., Blattmann, A., Lorenz, D., Esser, P. & Ommer, B. High-resolution image synthesis with latent diffusion models. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10674–10685 (IEEE Computer Society, Los Alamitos, CA, USA, 2022). https://doi.ieeecomputersociety.org/10.1109/CVPR52688.2022.01042.

  • Ramesh, A., Dhariwal, P., Nichol, A., Chu, C. & Chen, M. Hierarchical text-conditional image generation with clip latents (2022). Preprint at https://arxiv.org/abs/2204.06125.

  • Luo, C. Understanding diffusion models: A unified perspective (2022). Preprint at https://arxiv.org/abs/2208.11970.

  • Zhao, W. X. et al. A survey of large language models (2023). Preprint at https://arxiv.org/abs/2303.18223.

  • Liu, P. et al. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55 (2023).

  • Kather, J. N., Ghaffari Laleh, N., Foersch, S. & Truhn, D. Medical domain knowledge in domain-agnostic generative ai. npj Digit. Med. 5, 90 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang, C. et al. A complete survey on generative ai (aigc): Is chatgpt from gpt-4 to gpt-5 all you need? (2023). Preprint at https://arxiv.org/abs/2303.11717.

  • Zhang, C., Zhang, C., Zhang, M. & Kweon, I. S. Text-to-image diffusion models in generative ai: A survey (2023). Preprint at https://arxiv.org/abs/2303.07909.

  • Ferrara, E. Should chatgpt be biased? challenges and risks of bias in large language models (2023). Preprint at https://arxiv.org/abs/2304.03738.

  • Rutinowski, J., Franke, S., Endendyk, J., Dormuth, I. & Pauly, M. The self-perception and political biases of chatgpt (2023). Preprint at https://arxiv.org/abs/2304.07333.

  • Ji, Z. et al. Survey of hallucination in natural language generation. ACM Comput. Surv. 55, 1–38 (2023).

    Article 

    Google Scholar 

  • Bang, Y. et al. A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity (2023). Preprint at https://arxiv.org/abs/2302.04023.

  • Bian, N. et al. Chatgpt is a knowledgeable but inexperienced solver: An investigation of commonsense problem in large language models (2023). Preprint at https://arxiv.org/abs/2303.16421.

  • Chen, N. et al. Metrics for deep generative models. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, Vol. 84 of Proceedings of Machine Learning Research, (eds Storkey, A. & Perez-Cruz, F.) 1540–1550 (PMLR, 2018). https://proceedings.mlr.press/v84/chen18e.html.

  • Thoppilan, R. et al. Lamda: Language models for dialog applications (2022). Preprint at https://arxiv.org/abs/2201.08239.

  • Gloria, K., Rastogi, N. & DeGroff, S. Bias impact analysis of AI in consumer mobile health technologies: Legal, technical, and policy (2022). Preprint at https://arxiv.org/abs/2209.05440.

  • Peng, C. et al. A study of generative large language model for medical research and healthcare (2023). Preprint at https://arxiv.org/abs/2305.13523.

  • Wei, J. et al. Chain of thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems (eds Oh, A. H., Agarwal, A., Belgrave, D. & Cho, K.) (2022).

  • Leiter, C. et al. Towards explainable evaluation metrics for natural language generation (2022). Preprint at https://arxiv.org/abs/2203.11131.

  • Priyanshu, A., Vijay, S., Kumar, A., Naidu, R. & Mireshghallah, F. Are chatbots ready for privacy-sensitive applications? an investigation into input regurgitation and prompt-induced sanitization (2023). Preprint at https://arxiv.org/abs/2305.15008.

  • Ayers, J. W. et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern. Med. (2023).

  • Donovan – AI-powered decision-making for defense. Scale (2023).

  • Advanced targeting and lethality aided system (atlas). CoVar (2023).

  • Doctrinaire. CoVar (2023).

  • Choudhury, A. & Asan, O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med. Inform. 8, e18599 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bahl, M. et al. High-risk breast lesions: a machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision. Radiology 286, 170549 (2017).

    Google Scholar 

  • Dalal, A. K. et al. Systems engineering and human factors support of a system of novel ehr-integrated tools to prevent harm in the hospital. J. Am. Med. Inform. Assoc. 26, 553–560 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Intercom for Healthcare https://www.intercom.com/drlp/industry/healthcare.

  • Prediction and Early Identification of Disease Through AI—Siemens Healthineers https://www.siemens-healthineers.com/digital-health-solutions/artificial-intelligence-in-healthcare/ai-to-help-predict-disease.

  • Willemink, M. Ai for CT image reconstruction – a great opportunity. AI Blog (2019).

  • Bajgain, B., Lorenzetti, D., Lee, J. & Sauro, K. Determinants of implementing artificial intelligence-based clinical decision support tools in healthcare: a scoping review protocol. BMJ Open 13, e068373 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • David Lat, E. M. Advanced targeting and lethality automated system archives. Breaking Defense https://breakingdefense.com/tag/advanced-targeting-and-lethality-automated-system/.

  • Utegen, A. et al. Development and modeling of intelligent control system of cruise missile based on fuzzy logic. In 2021 16th International Conference on Electronics Computer and Computation (ICECCO), 1–6 (2021).

  • Bohr, A. & Memarzadeh, K. Chapter 2 – the rise of artificial intelligence in healthcare applications. In Artificial Intelligence in Healthcare, (eds Bohr, A. & Memarzadeh, K.) 25–60 (Academic Press, 2020).

  • Morgan, F. E. et al. Military Applications of Artificial Intelligence: Ethical Concerns in an Uncertain World (RAND Corporation, Santa Monica, CA, 2020).

  • Introduction to the law of armed conflict (loac) https://www.genevacall.org/wp-content/uploads/dlm_uploads/2013/11/The-Law-of-Armed-Conflict.pdf.

  • Rule 1. The principle of distinction between civilians and combatants. IHL https://ihl-databases.icrc.org/en/customary-ihl/v1/rule1.

  • Docherty, B. Losing humanity. Human Rights Watch (2012).

  • Generative Artificial Intelligence and data privacy: A Primer – CRS Reports https://crsreports.congress.gov/product/pdf/R/R47569.

  • Journal, H. Hipaa, healthcare data, and artificial intelligence. HIPAA J. (2023).

  • Patel, V. L., Kannampallil, T. G. & Kaufman, D. R. Cognitive informatics for biomedicine: human computer interaction in healthcare (Springer, 2015).

  • II, W. N. P. Risks and remedies for artificial intelligence in health care. Brookings (2022).

  • Lyons, J. B. & Stokes, C. K. Human-human reliance in the context of automation. Hum. Factors 54, 112–121 (2012).

    Article 
    PubMed 

    Google Scholar 

  • Asan, O., Bayrak, E. & Choudhury, A. Artificial intelligence and human trust in healthcare: Focus on clinicians (preprint) (2019).

  • Lewis, M., Sycara, K. & Walker, P. The Role of Trust in Human–Robot Interaction, 135–159 (Springer International Publishing, 2018).

  • Hawley, J. K. Looking back at 20 years of manprint on patriot: Observations and lessons (2007).

  • Parikh, R. B., Obermeyer, Z. & Navathe, A. S. Regulation of predictive analytics in medicine. Science 363, 810–812 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Richardson, J. P. et al. Patient apprehensions about the use of artificial intelligence in healthcare. npj Digit. Med. 4, 140 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Christian, R. Mind the gap the lack of accountability for killer robots. Human Rights Watch (2015).

  • Habli, I., Lawton, T. & Porter, Z. Artificial intelligence in health care: accountability and safety. Bull. World Health Organ. 98, 251–256 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • N, O. et al. Addressing racial and ethnic inequities in data-driven health technologies 1–53 (2022).

  • Char, D. S., Shah, N. H. & Magnus, D. Implementing machine learning in health care—addressing ethical challenges. N. Engl. J. Med. 378, 981–983 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Frisk, A. What is Project Maven? The Pentagon ai project Google employees want out of – -national. Global News (2018). https://globalnews.ca/news/4125382/google-pentagon-ai-project-maven/.

  • Shane, S. & Wakabayashi, D. The business of war’: Google employees protest work for the Pentagon. The New York Times (2018).

  • Our principles. Google AI https://ai.google/principles.

  • Augmented intelligence in Health Care*1 – American Medical Association https://www.ama-assn.org/system/files/2019-01/augmented-intelligence-policy-report.pdf.

  • Blueprint for trustworthy AI implementation guidance and assurance for healthcare https://www.coalitionforhealthai.org/papers/blueprint-for-trustworthy-ai_V1.0.pdf.

  • Blueprint for an AI bill of rights – ostp. The White House (2023).

  • Naik, N. et al. Legal and ethical consideration in artificial intelligence in healthcare: Who takes responsibility?Front. Surg. 9 (2022).

  • Pifer, R. “hurtling into the future”: The potential and thorny ethics of generative ai in healthcare. Healthcare Dive (2023).

  • Rosenberg, I., Shabtai, A., Elovici, Y. & Rokach, L. Adversarial machine learning attacks and defense methods in the cyber security domain. ACM Comput. Surv. 54 (2021).

  • Sigfrids, A., Leikas, J., Salo-Pöntinen, H. & Koskimies, E. Human-centricity in AI governance: A systemic approach. Front. Artif. Intell. 6 (2023).

  • Developing cyber-resilient systems: A systems security engineering approach https://doi.org/10.6028/NIST.SP.800-160v2r1.

  • Centers for Disease Control and Prevention (2022).

  • Aquino, Y. S. J. et al. Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives. J. Med. Ethics (2023).

  • Hoffman, K. M., Trawalter, S., Axt, J. R. & Oliver, M. N. Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites. Proc. Natl Acad. Sci. 113, 4296–4301 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Oldehoeft, A. E. Foundations of a security policy for use of the national research and educational network https://nvlpubs.nist.gov/nistpubs/Legacy/IR/nistir4734.pdf.

  • Robertson, C. et al. Diverse patients’ attitudes towards artificial intelligence (AI) in diagnosis. PLOS Digital Health https://doi.org/10.1371/journal.pdig.0000237.

  • Habli, I., Lawton, T. & Porter, Z. Artificial intelligence in health care: accountability and safety. Bull. World Health Org. 98, 251 – 256 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mora-Cantallops, M., Sánchez-Alonso, S., García-Barriocanal, E. & Sicilia, M.-A. Traceability for trustworthy AI: a review of models and tools. Big Data Cogn. Comput. 5 (2021).

  • Li, B. et al. Trustworthy ai: From principles to practices. ACM Comput. Surv. 55 (2023).

  • Barker, E., Smid, M., Branstad, D. & Chokhani, S. A framework for designing cryptographic key management systems https://csrc.nist.gov/publications/detail/sp/800-130/final.

  • (OCR), O. f. C. R. Guidance on risk analysis. HHS.gov (2021).

  • Perez, F. & Ribeiro, I. Ignore previous prompt: Attack techniques for language models. In NeurIPS ML Safety Workshop (2022).

  • Liu, Y. et al. Jailbreaking chatgpt via prompt engineering: An empirical study (2023). Preprint at https://arxiv.org/abs/2305.13860.

  • Stanley-Lockman, Z. & Christie, E. H. An artificial intelligence strategy for nato https://www.nato.int/docu/review/articles/2021/10/25/an-artificial-intelligence-strategy-for-nato/index.html.

  • Team, T. F. State of California endorses Asilomar ai principles. Future Life Inst. (2022).

  • Moudatsou, M., Stavropoulou, A., Philalithis, A. & Koukouli, S. The role of empathy in health and social care professionals. Healthcare 8, 26 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhu, Q. & Luo, J. Toward artificial empathy for human-centered design: A framework (2023). Preprint at https://arxiv.org/abs/2303.10583.

  • Asch, D. A. An interview with chatgpt about health care. Catal. Non Issue Content 4 (2023).

  • Holm, S. Principles of biomedical ethics, 5th edn. J. Med. Eth. 28, 332–332 (2002).

    Article 

    Google Scholar 

  • AMA Journal of Ethics 18, 12–17 (2016).

  • Applin, S. & Fischer, M. New technologies and mixed-use convergence: How humans and algorithms are adapting to each other (2016).

  • Human Rights and Biomedicine https://coe.int/en/web/bioethics/report-impact-of-ai-on-the-doctor-patient-relationship.

  • Svikhnushina, E. & Pu, P. Approximating online human evaluation of social chatbots with prompting. In Proceedings of the 24th Meeting of the Special Interest Group on Discourse and Dialogue, (eds Schlangen, D. et al.) 268–281 (Association for Computational Linguistics, 2023). https://aclanthology.org/2023.sigdial-1.25.

  • Raamkumar, A. S. & Yang, Y. Empathetic conversational systems: a review of current advances, gaps, and opportunities (2022). Preprint at

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