Artificial Intelligence for Human Life: A Critical Opinion from Medical Bioethics Perspective – Part I

Urfa Khairatun Hisan; Muhammad Miftahul Amri

doi:10.56741/jphs.v1i02.214

Authors

Urfa Khairatun Hisan
urfa.hisan@med.uad.ac.id
Universitas Ahmad Dahlan https://orcid.org/0000-0003-4952-3542
Muhammad Miftahul Amri Universitas Ahmad Dahlan https://orcid.org/0000-0001-5501-8484

Keywords:

Artificial Intelligence (AI), Human Life, Ethics, Trustworthy AI, Medical Bioethics

Abstract

Since the end of the second artificial intelligence (AI) winter in 1993, AI popularity has been skyrocketing. Catalyzed by the rapid advancements of supporting technologies such as computational power and storage capacity, AI has been widely developed for various sectors of human lives. Although there is a common global consensus stating that the development of AI should consider ethical principles, until when this paper was written, the debates on how we can define an AI as ‘ethical’, how the AI ethical guidelines should be formulated, who has the rights to determine the ethical aspects of AI, and how can we reinforce the guidelines to the AI developer/operator is still going on. In this article, we summarized the research and studies related to AI development from the ethical perspective that have been conducted within the last 10 years. We discussed several cases of AI development misconduct and ‘unethical’ AI, such as biased algorithms and privacy breaches during data gathering.

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Author Biographies

Urfa Khairatun Hisan, Universitas Ahmad Dahlan

Urfa Khairatun Hisan is a lecturer at the Faculty of Medicine, Universitas Ahmad Dahlan, Yogyakarta, Indonesia, and a graduate student of the Department of Bioethics, Universitas Gadjah Mada, Indonesia. She received her B.Med. and M.D. degrees from the Faculty of Medicine, Universitas Gadjah Mada, Indonesia, in 2017 and 2019, respectively. Her research interests include public health and bioethics in medicine. She can be contacted at email: urfa.hisan@med.uad.ac.id.

Muhammad Miftahul Amri, Universitas Ahmad Dahlan

Muhammad Miftahul Amri received his B.S. degree from the Department of Computer Science and Electronics, Universitas Gadjah Mada Indonesia in 2018, and the M.S. degree from the Department of Electrical and Computer Engineering, Sungkyunkwan University South Korea in 2021, where he is currently pursuing the Ph.D. degree. In 2022, he received the M.M. degree and professional engineer title, respectively, from Universitas Terbuka and Universitas Muhammadiyah Yogyakarta Indonesia. In 2021, he joined the faculty at Universitas Ahmad Dahlan, Indonesia, where he is currently a lecturer in the Department of Electrical Engineering. His research interests include wireless communications and reconfigurable intelligent surfaces.

References

Crawford, K., Whittaker, M., Elish, M. C., Barocas, S., Plasek, A., & Ferryman, K. (2016). The AI now report. The Social and Economic Implications of Artificial Intelligence Technologies in the Near-Term.

Smith, R. G., & Eckroth, J. (2017). Building AI applications: Yesterday, today, and tomorrow. AI Magazine, 38(1), 6–22.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.

Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., Bonnefon, J.-F., & Rahwan, I. (2018). The moral machine experiment. Nature, 563(7729), 59–64.

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: addressing ethical challenges. PLoS Medicine, 15(11), e1002689.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.

Bessen, J. (2018). AI and Jobs: The role of demand. National Bureau of Economic Research.

Korinek, A., & Stiglitz, J. E. (2021). Covid-19 driven advances in automation and artificial intelligence risk exacerbating economic inequality. Bmj, 372.

Takefuji, Y. (2018). In the long run, the gap between the rich and poor will dramatically increase by artificial intelligence. Science, 1.

Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2), 2053951717743530.

O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group.

Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99–120.

Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019). The role and limits of principles in AI ethics: towards a focus on tensions. Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 195–200.

Siau, K., & Wang, W. (2020). Artificial intelligence (AI) ethics: ethics of AI and ethical AI. Journal of Database Management (JDM), 31(2), 74–87.

Feng, Z. (2018). Does AI Share Same Ethic with Human Being? International Conference on Intelligence Science, 465–472.

Bolukbasi, T., Chang, K.-W., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in Neural Information Processing Systems, 29.

John-Mathews, J.-M., Cardon, D., & Balagué, C. (2022). From reality to world. A critical perspective on AI fairness. Journal of Business Ethics, 1–15.

Ethics Guidelines for Trustworthy AI. (2022). FUTURIUM - European Commission. https://ec.europa.eu/futurium/en/ai-alliance-consultation

The OECD Artificial Intelligence (AI) Principles. (2022). OECD.AI. https://oecd.ai/en/ai-principles

Our Principles. (n.d.-t). Google AI. https://ai.google/principles/

Introduction to guidelines for human-AI interaction - Human-AI interaction guidelines. (2022). Microsoft Learn. https://learn.microsoft.com/en-us/ai/guidelines-human-ai-interaction/

Responsible AI principles from Microsoft. (2022). Microsoft. https://www.microsoft.com/en-us/ai/responsible-ai

Ethics of artificial intelligence. (2022). Unesco. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics

Calo, R. (2017). Artificial intelligence policy: a primer and roadmap. UCDL Rev., 51, 399.

Kayser-Bril, N. (2022). Undress or fail: Instagram’s algorithm strong-arms users into showing skin. Algorithmwatch. https://algorithmwatch.org/en/instagram-algorithm-nudity/

Creepy AI can guess what you look like just by listening to a short audio clip of your voice. (2019). Dailymail. https://www.dailymail.co.uk/sciencetech/article-7130409/Creepy-AI-guess-look-like-just-listening-short-audio-clip-voice.html

Google apologises for Photos app’s racist blunder. (2015). BBC News. https://www.bbc.com/news/technology-33347866

Vincent, J. (2018). Google ‘fixed’ its racist algorithm by removing gorillas from its image-labeling tech. Theverge. https://www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognition-algorithm-ai

Lyons, K. (2020). Twitter is looking into why its photo preview appears to favor white faces over Black faces. Theverge. https://www.theverge.com/2020/9/20/21447998/twitter-photo-preview-white-black-faces

Twitter finds racial bias in image-cropping AI. (221 C.E.). BBC News. https://www.bbc.com/news/technology-57192898

Cheng, S. (2016). An algorithm rejected an Asian man’s passport photo for having “closed eyes.” Quartz. https://qz.com/857122/an-algorithm-rejected-an-asian-mans-passport-photo-for-having-closed-eyes

IBM abandons “biased” facial recognition tech. (2020). BBC News. https://www.bbc.com/news/technology-52978191

Liao, S. (2017). Google admits it tracked user location data even when the setting was turned off. Theverge. https://www.theverge.com/2017/11/21/16684818/google-location-tracking-cell-tower-data-android-os-firebase-privacy

Korn, J. (2022). Google agrees to $392 million settlement with 40 states over location tracking practices. CNN Business. https://edition.cnn.com/2022/11/14/tech/google-location-tracking-settlement/index.html

Reuters. (2022). Texas sues Google for allegedly using people’s faces and voices without consent. The Guardian. https://www.theguardian.com/us-news/2022/oct/20/texas-google-lawsuit-privacy-biometric-data

Chang, A. (2018). The Facebook and Cambridge Analytica scandal, explained with a simple diagram. VOX. https://www.vox.com/policy-and-politics/2018/3/23/17151916/facebook-cambridge-analytica-trump-diagram

Hern, A., & Cadwalladr, C. (2018). Revealed: Aleksandr Kogan collected Facebook users’ direct messages. The Guardian. https://www.theguardian.com/uk-news/2018/apr/13/revealed-aleksandr-kogan-collected-facebook-users-direct-messages

Lapowsky, I. (2018). Facebook Exposed 87 Million Users to Cambridge Analytica. Wired. https://www.wired.com/story/facebook-exposed-87-million-users-to-cambridge-analytica/

Schapiro, A.-A., & Bacchi, U. (2020). U.S. protests fuel calls for ban on racially biased facial recognition tools. Reuters. https://www.reuters.com/article/us-usa-protests-tech-trfn-idUSKBN23B3B5

Robertson, A. (2021). Detroit man sues police for wrongfully arresting him based on facial recognition. Theverge. https://www.theverge.com/2021/4/13/22382398/robert-williams-detroit-police-department-aclu-lawsuit-facial-recognition-wrongful-arrest

Campbell, C. (2019). How China Is Using “Social Credit Scores” to Reward and Punish Its Citizens. Time. https://time.com/collection/davos-2019/5502592/china-social-credit-score/

CNIL Fines Clearview AI 20 Million Euros for Unlawful Use of Facial Recognition Technology. (2022). Hunton Andrews Kurth. https://www.huntonprivacyblog.com/2022/10/24/cnil-fines-clearview-ai-20-million-euros-for-unlawful-use-of-facial-recognition-technology/

Tambini, O. (2020). Your smart speaker could be listening in on your conversations by accident. Techradar. https://www.techradar.com/news/your-smart-speaker-could-be-accidentally-listening-in-on-your-conversations

Lutz, C., & Newlands, G. (2021). Privacy and smart speakers: A multi-dimensional approach. The Information Society, 37(3), 147–162.

Yong-hong, L., & Xiao-Liang, L. (2010). Research of data mining based on e-commerce. 2010 3rd International Conference on Computer Science and Information Technology, 4, 719–722.

Johnson, D. G., & Diakopoulos, N. (2021). What to do about deepfakes. Communications of the ACM, 64(3), 33–35.

Jaiman, A. (2020). Debating the Ethics of Deepfakes. In a Pandemic World, 75.

Hunkenschroer, A. L., & Kriebitz, A. (2022). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. AI and Ethics. https://doi.org/10.1007/s43681-022-00166-4

Crossman, P. (2022). Unfair lending with AI? Don’t point just at us, fintechs and online lenders say. American Banker. https://www.americanbanker.com/news/unfair-lending-with-ai-dont-point-just-at-us-fintechs-and-online-lenders-say

Hale, K. (2021). A.I. Bias Caused 80% Of Black Mortgage Applicants To Be Denied. Forbes. https://www.forbes.com/sites/korihale/2021/09/02/ai-bias-caused-80-of-black-mortgage-applicants-to-be-denied/amp/

Exclusive: Elsevier retracting 500 papers for shoddy peer review. (2022). Retraction Watch. https://retractionwatch.com/2022/10/28/exclusive-elsevier-retracting-500-papers-for-shoddy-peer-review/

A tale of 32 retraction notices: On publishers, paper mill products, and the sleuths that find them. (2022). Retraction Watch. https://retractionwatch.com/2022/08/15/a-tale-of-32-retraction-notices-on-publishers-paper-mill-products-and-the-sleuths-that-find-them/

Exclusive: Hindawi and Wiley to retract over 500 papers linked to peer review rings. (2022). Retraction Watch. https://retractionwatch.com/2022/09/28/exclusive-hindawi-and-wiley-to-retract-over-500-papers-linked-to-peer-review-rings/

More than 300 at once: Publisher retracts entire conference proceedings. (2022). Retraction Watch. https://retractionwatch.com/2022/04/20/more-than-300-at-once-publisher-retracts-entire-conference-proceedings/

Physics publisher retracting nearly 500 likely paper mill papers. (2022). Retraction Watch. https://retractionwatch.com/2022/09/09/physics-publisher-retracting-nearly-500-likely-paper-mill-papers/

‘Our deepest apology’: Journal retracts 30 likely paper mill articles after investigation published by Retraction Watch. (2022). Retraction Watch. https://retractionwatch.com/2022/07/05/our-deepest-apology-journal-retracts-30-likely-paper-mill-articles-after-investigation-published-by-retraction-watch/

Zielinski, D. (2021). Predicting Misconduct: How AI Helps Head Off Harassment, Bias and Other Ethics Issues. SHRM. https://www.shrm.org/hr-today/news/hr-magazine/winter2021/pages/next-generation-technologies-transform-misconduct-reporting-.aspx

Townson, S. (2020). AI Can Make Bank Loans More Fair. Harvard Business Review. https://hbr.org/2020/11/ai-can-make-bank-loans-more-fair

Siddique, Faiz; Lerman, Rachel; Merrill, J. B. (2022). Tesla Running Autopilot Involved in 273 Crashes Reported Since Last year. The Washington Post. https://www.washingtonpost.com/technology/2022/06/15/tesla-autopilot-crashes/