Abstract
Through analyzing examples from the COVID-19 pandemic and legislative strategies for the responsible use of AI, we argue that trustworthy science is an intrinsic value with which every good research practice should align. This means that science has to be conducted in a responsible way. Still, this is a necessary but not sufficient condition for building long-lasting trust in science. The social component plays a significant role in this process. Education is important because it increases the scientific literacy of laypeople and the responsible applications of technology by experts. Moreover, when applying scientific research to a larger population one has to keep in mind the individual, cultural and national context of the measures in question. In order to achieve cross-national trust in science, social and natural sciences have to act in synergy with educational institutions over a long period of time. While the normative questions have to be answered by philosophers and policymakers. Especially in the case of AI, science has to be designed for the benefit of humans and for their use, constituting the so-called human-centered application of AI.
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Notes
- 1.
The idea of finding the middle ground when deciding about AI is in line with the approach of virtue ethics (e.g. Hobbs, 2021).
References
Belongia, E. A., & Naleway, A. L. (2003). Smallpox vaccine: The good, the bad, and the ugly. Clinical Medicine & Research, 1(2), 87–92.
Biswas, N., Mustapha, T., Khubchandani, J., & Price, J. H. (2021). The nature and extent of COVID-19 vaccination hesitancy in healthcare workers. Journal of Community Health, 46(6), 1244–1251.
Chiang, C.-H., Chiang, C.-H., & Chiang, C.-H. (2020). Maintaining mask stockpiles in the COVID-19 pandemic: Taiwan as a learning model. Infection Control & Hospital Epidemiology, 42(2), 244–245.
Davies, A., Veličković, P., Buesing, L., Blackwell, S., Zheng, D., Tomašev, N., Tanburn, R., et al. (2021). Advancing mathematics by guiding human intuition with AI. Nature, 600(7887), 70–74.
DIN and DKE. (2020). Standardization roadmap artificial intelligence. Retrieved April 01, 2022, from https://www.din.de/resource/blob/772610/e96c34dd6b12900ea75b460538805349/normungsroadmap-en-data.pdf
Douglas, H. (2000). Inductive risk and values in science. Philosophy of Science, 67(4), 559–579.
Douglas, H. (2009). Science, policy, and the value-free ideal. University of Pittsburgh Press.
Durán, J. M., & Formanek, N. (2018). Grounds for trust: Essential epistemic opacity and computational reliabilism. Minds and Machines, 28(4), 645–666.
Elemento, O., Leslie, C., Lundin, J., & Tourassi, G. (2021). Artificial intelligence in cancer research, diagnosis and therapy. Nature Reviews Cancer, 21(12), 747–752.
European Commission, Directorate-General for Communications Networks, Content and Technology. (2019). Ethics guidelines for trustworthy AI. Publications Office. https://doi.org/10.2759/177365
Funk, C., Hefferon, M., Kennedy, B., & Johnson, C. (2019). Trust and mistrust in Americans’ views of scientific experts. Pew Research Center.
Gamble, V. N. (1997). Under the shadow of Tuskegee: African Americans and health care. American Journal of Public Health, 87(11), 1773–1778.
Haakonsen, J. M. F., & Furnham, A. (2022). COVID-19 vaccination: Conspiracy theories, demography, ideology, and personality disorders. Health Psychology.
Hagendorff, T. (2022). A virtue-based framework to support putting AI ethics into practice. Philosophy & Technology, 35(3), 1–24.
Hobbs, R. (2021). Integrating ethically align design into agile and CRISP-DM. SoutheastCon, 2021, 1–8.
Holman, B., & Elliott, K. C. (2018). The promise and perils of industry funded science. Philosophy Compass, 13(11), e12544.
Hsu, A. L., Johnson, T., Phillips, L., & Nelson, T. B. (2022). Sources of vaccine hesitancy: pregnancy, infertility, minority concerns, and general skepticism. In Open forum infectious diseases (Vol. 9, No. 3, p. ofab433). Oxford University Press.
King, S. (1999). Vaccination policies: Individual rights v community health. BMJ, 319(7223), 1448–1449.
Mahese, E. (2021). Covid-19: WHO says rollout of AstraZeneca vaccine should continue, as Europe divides over safety. BMJ 372, n728. https://doi.org/10.1136/bmj.n728
Maxmen, A. (2019). Science under fire: Ebola researchers fight to test drugs and vaccines in a war zone. Nature, 572(7767), 16–17.
Momplaisir, F., Haynes, N., Nkwihoreze, H., Nelson, M., Werner, R. M., & Jemmott, J. (2021). Understanding drivers of coronavirus disease 2019 vaccine hesitancy among blacks. Clinical Infectious Diseases, 73(10), 1784–1789.
Nguyen, L. H., Joshi, A. D., Drew, D. A., Merino, J., Ma, W., Lo, C.-H., Kwon, S., et al. (2022). Self-reported COVID-19 vaccine hesitancy and uptake among participants from different racial and ethnic groups in the United States and United Kingdom. Nature Communications, 13(1), 1–9.
Privor-Dumm, L., & King, T. (2020). Community-based strategies to engage pastors can help address vaccine hesitancy and health disparities in black communities. Journal of Health Communication, 25(10), 827–830.
Resch, M., & Kaminski, A. (2019). The epistemic importance of technology in computer simulation and machine learning. Minds and Machines, 29(1), 9–17.
Rutjens, B. T., Sutton, R. M., & van der Lee, R. (2018). Not all skepticism is equal: Exploring the ideological antecedents of science acceptance and rejection. Personality and Social Psychology Bulletin, 44(3), 384–405.
Rutjens, B. T., Sengupta, N., van Der Lee, R., van Koningsbruggen, G. M., Martens, J. P., Rabelo, A., & Sutton, R. M. (2021). Science skepticism across 24 countries. Social Psychological and Personality Science, 13(1), 102–117.
Satariano, A. (2020). British grading debacle shows pitfalls of automating government. The New York Times, August 20.
Sikimić, V., Nikitović, T., Vasić, M., & Subotić, V. (2021). Do political attitudes matter for epistemic decisions of scientists? Review of Philosophy and Psychology, 12(4), 775–801.
Sikimić, V. (2022). How to improve research funding in academia? Lessons from the COVID-19 crisis. Frontiers in Research Metrics and Analytics, 7. https://doi.org/10.3389/frma.2022.777781
Sismondo, S. (2021). Epistemic corruption, the pharmaceutical industry, and the body of medical science. Frontiers in Research Metrics and Analytics, 6, 2. https://doi.org/10.3389/frma.2021.614013
Steinert, J. I., Sternberg, H., Prince, H., Fasolo, B., Galizzi, M. M., Büthe, T., & Veltri, G. A. (2022). COVID-19 vaccine hesitancy in eight European countries: Prevalence, determinants, and heterogeneity. Science Advances, 8(17), eabm9825.
Tso, R. V., & Cowling, B. J. (2020). Importance of face masks for COVID-19: A call for effective public education. Clinical Infectious Diseases, 71(16), 2195–2198.
Van Noorden, R. (2020). The ethical questions that haunt facial-recognition research. Nature, 587, 354–358.
Vučković, A., & Sikimić, V. (2022). How to fight linguistic injustice in science: Equity measures and mitigating agents. Social Epistemology, 1–17.
Wang, C. C., Prather, K. A., Sznitman, J., Jimenez, J. L., Lakdawala, S. S., Tufekci, Z., & Marr, L. C. (2021). Airborne transmission of respiratory viruses. Science, 373(6558), eabd9149.
Wonkam, A. (2021). Sequence three million genomes across Africa. Nature, 590, 209–211.
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Sikimić, V. (2024). Trust in Science During Global Challenges: The Pandemic and Trustworthy AI. In: Resch, M.M., Formánek, N., Joshy, A., Kaminski, A. (eds) The Science and Art of Simulation. DD 2022. Springer, Cham. https://doi.org/10.1007/978-3-031-68058-8_9
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