Human Freedom from Algorithmic Bias: What is the role of Accountability in addressing Health Disparities?
Main Article Content
Abstract
While there are many causes of health disparities, the application of Artificial Intelligence tools in healthcare may have mixed results. The purpose of this paper is to investigate the role of human freedoms and accountability to achieving digital inclusion. It discovers the role of algorithmic bias in mediating the relationship between human freedom and mobile health. The following research questions are investigated: 1) How do human freedoms effect digital inclusion and mobile health? 2) Do human freedoms effect mobile health? And 3) Does AI accountability mediate the relationship between human freedoms and mobile health? The findings suggest that human freedoms are central to digital inclusion and mobile health. Accountability does affect the extent to which digital inclusion can be achieved through human freedoms. AI accountability significantly mediates the relationship between human freedoms and the mobile index. This offers an important contribution in uncovering the role of algorithmic bias in human freedom and mobile health, and of accountability between human freedom and digital inclusion.
Keywords:
Mobile Health (mHealth), Algorithmic Bias, Human Freedom, Algorithmic Accountability, Artificial Intelligence (AI), Social Determinants of Health (SDOH), Digital Inclusion
Article Details
How to Cite
QURESHI, Sajda; OLADOKUN, Blessing.
Human Freedom from Algorithmic Bias: What is the role of Accountability in addressing Health Disparities?.
Medical Research Archives, [S.l.], v. 12, n. 8, aug. 2024.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/5635>. Date accessed: 06 sep. 2024.
doi: https://doi.org/10.18103/mra.v12i8.5635.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
1. Sen, A. (2005). Human rights and capabilities. Journal of human development, 6(2), 151-166.
2. Zuboff, S. (2019, January). Surveillance capitalism and the challenge of collective action. In New labor forum (Vol. 28, No. 1, pp. 10-29). Sage CA: Los Angeles, CA: SAGE Publications.
3. Sieck, C.J., Sheon, A., Ancker, J.S., Castek, J., Callahan, B. and Siefer, A. 2021. “Digital inclusion as a social determinant of health,” NPJ digital medicine, 4(1), 52. https://doi.org/10.1038/s41746-021-00413-8.
4. Rogers, C.C., Jang, S.S., Tidwell, W., Shaughnessy, S., Milburn, J., Hauck, F.R. and Valdez, R.S. (2023). “Designing mobile health to align with the social determinants of health,” Frontiers in Digital Health 5, 1193920. https://doi.org/10.3389/fdgth.2023.1193920
5. Ye, J., Ma, Q.: The effects and patterns among mobile health, social determinants, and physical activity: a nationally representative cross-sectional study. In: AMIA Summits on Translational Science Proceedings, vol. 2021, pp. 653. (2021).
6. Qureshi, S. (2022). Creating cycles of prosperity with human digital development for intelligent global health. Information Technology for Development, 28(4), 649- 659.
7. Maghsoudi, T., Cascon-Pereira, R., & Beatriz Hernández Lara, A. (2020). The role of collaborative healthcare in improving social sustainability: A conceptual frame- work. Sustainability, 12(8), 3195.
8. Medina, F.-X. and Sole-Sedeno, J.M. (2023). “Social Sustainability, Social Capital, Health, and the Building of Cultural Capital around the Mediterranean Diet,” Sustainability 15(5):4664. https://doi.org/10.3390/su15054664
9. Seth, R., Dhaliwal, B.K., Miller, E., Best, T., Sullivan, A., Thankachen, B., Shet, A.: Leveling the Research Playing Field: Decolonizing Global Health Research Through Web-Based Platforms. Journal of Medical Internet Research 25, e46897 (2023)
10. Masiero, S., Bailur, S.: Digital identity for development: The quest for justice and a research agenda. Information Technology for Development 27(1), 1-12 (2021). doi: 10.1080/02681102.2021.1859669
11. World Health Organization: Rio Political Declaration on Social Determinants of Health. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4910459/ (2011).
12. Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns (New York, N.Y.), 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347
13. Ueda, D., Kakinuma, T., Fujita, S. et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42, 3–15 (2024). Doi: https://doi.org/10.1007/s11604-023-01474-3
14. Fletcher RR, Nakeshimana A and Olubeko O (2021) Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Front. Artif. Intell. 3:561802. doi: 10.3389/frai.2020.561802
15. Qureshi, S. (2023). Cycles of development in systems of survival with artificial intelligence: a formative research agenda. Information Technology for Development, 29(2-3), 171-183.
16. Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D., Alhassan, J., Galley, J., Kousari, R., Ogbadu-Oladapo, L., Saurav, S. K., Srivastava, A., Tummuru, S. P., Uppala, S., & Vaidya, P. 2023. “Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development,” Information Development, 0(0). https://doi.org/10.1177/02666669231200628
17. Larburu, N. Artetxe V., Escolar A., Lozano, and J. Kerexeta, “Artificial intelli- gence to prevent mobile heart failure patient’s decompensation in real time: mon- itoringbased predictive model,” Mobile Information Systems, vol. 2018, Article ID 1546210, 11 pages, 2018.
18. Wieringa M (2020) What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, p. 1–18. https://doi.org/10.1145/3351095.3372833
19. Horneber, D., Laumer, S. Algorithmic Accountability. Bus Inf Syst Eng 65, 723–730 (2023). https://doi.org/10.1007/s12599-023-00817-8
20. Lepri, B., Oliver, N., Letouzé, E. Pentland, A. and Vinck, P. (2018). “Fair, Transparent, and Accountable Algorithmic Decision-making Processes,” Philos. Technol. 31, 611–627. https://doi.org/10.1007/s13347-017-0279-x
21. Vandelanotte, C., Trost, S., Hodgetts, D., Imam, T., Rashid, M., To, Q. G. and Maher, C. 2023. “Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions,” Journal of Biomedical Informatics, 144, 104435.
22. Han, S., Zhang, R. F., Shi, L., Richie, R., Liu, H., Tseng, A., Quan, W., Ryan, N., Brent, D., and Tsui, F. R. 2022. “Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing,” Journal of biomedical informatics, 127, 103984. https://doi.org/10.1016/j.jbi.2021.103984
23. Sangers, T., Reeder, S., van der Vet, S., Jhingoer, S., Mooyaart, A., Siegel, D. M., & Wakkee, M. (2022). Validation of a market-approved artificial intelligence mo- bile health app for skin cancer screening: a prospective multicenter diagnostic ac- curacy study. Dermatology, 238(4), 649-656.
24. Xu, Z., Biswas, B., Li, L., & Amzal, B. (2023). AI/ML in Precision Medicine: A Look Beyond the Hype. Therapeutic Innovation & Regulatory Science, 1-6.
25. Xiaofeng Wang, Zhenshun Cheng, “Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations”, Chest, Volume 158, Issue 1, Supplement, 2020, Pages S65-S71, ISSN 0012-3692, https://doi.org/10.1016/j.chest.2020.03.012.
2. Zuboff, S. (2019, January). Surveillance capitalism and the challenge of collective action. In New labor forum (Vol. 28, No. 1, pp. 10-29). Sage CA: Los Angeles, CA: SAGE Publications.
3. Sieck, C.J., Sheon, A., Ancker, J.S., Castek, J., Callahan, B. and Siefer, A. 2021. “Digital inclusion as a social determinant of health,” NPJ digital medicine, 4(1), 52. https://doi.org/10.1038/s41746-021-00413-8.
4. Rogers, C.C., Jang, S.S., Tidwell, W., Shaughnessy, S., Milburn, J., Hauck, F.R. and Valdez, R.S. (2023). “Designing mobile health to align with the social determinants of health,” Frontiers in Digital Health 5, 1193920. https://doi.org/10.3389/fdgth.2023.1193920
5. Ye, J., Ma, Q.: The effects and patterns among mobile health, social determinants, and physical activity: a nationally representative cross-sectional study. In: AMIA Summits on Translational Science Proceedings, vol. 2021, pp. 653. (2021).
6. Qureshi, S. (2022). Creating cycles of prosperity with human digital development for intelligent global health. Information Technology for Development, 28(4), 649- 659.
7. Maghsoudi, T., Cascon-Pereira, R., & Beatriz Hernández Lara, A. (2020). The role of collaborative healthcare in improving social sustainability: A conceptual frame- work. Sustainability, 12(8), 3195.
8. Medina, F.-X. and Sole-Sedeno, J.M. (2023). “Social Sustainability, Social Capital, Health, and the Building of Cultural Capital around the Mediterranean Diet,” Sustainability 15(5):4664. https://doi.org/10.3390/su15054664
9. Seth, R., Dhaliwal, B.K., Miller, E., Best, T., Sullivan, A., Thankachen, B., Shet, A.: Leveling the Research Playing Field: Decolonizing Global Health Research Through Web-Based Platforms. Journal of Medical Internet Research 25, e46897 (2023)
10. Masiero, S., Bailur, S.: Digital identity for development: The quest for justice and a research agenda. Information Technology for Development 27(1), 1-12 (2021). doi: 10.1080/02681102.2021.1859669
11. World Health Organization: Rio Political Declaration on Social Determinants of Health. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4910459/ (2011).
12. Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns (New York, N.Y.), 2(10), 100347. https://doi.org/10.1016/j.patter.2021.100347
13. Ueda, D., Kakinuma, T., Fujita, S. et al. Fairness of artificial intelligence in healthcare: review and recommendations. Jpn J Radiol 42, 3–15 (2024). Doi: https://doi.org/10.1007/s11604-023-01474-3
14. Fletcher RR, Nakeshimana A and Olubeko O (2021) Addressing Fairness, Bias, and Appropriate Use of Artificial Intelligence and Machine Learning in Global Health. Front. Artif. Intell. 3:561802. doi: 10.3389/frai.2020.561802
15. Qureshi, S. (2023). Cycles of development in systems of survival with artificial intelligence: a formative research agenda. Information Technology for Development, 29(2-3), 171-183.
16. Mannuru, N. R., Shahriar, S., Teel, Z. A., Wang, T., Lund, B. D., Tijani, S., Pohboon, C. O., Agbaji, D., Alhassan, J., Galley, J., Kousari, R., Ogbadu-Oladapo, L., Saurav, S. K., Srivastava, A., Tummuru, S. P., Uppala, S., & Vaidya, P. 2023. “Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development,” Information Development, 0(0). https://doi.org/10.1177/02666669231200628
17. Larburu, N. Artetxe V., Escolar A., Lozano, and J. Kerexeta, “Artificial intelli- gence to prevent mobile heart failure patient’s decompensation in real time: mon- itoringbased predictive model,” Mobile Information Systems, vol. 2018, Article ID 1546210, 11 pages, 2018.
18. Wieringa M (2020) What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, p. 1–18. https://doi.org/10.1145/3351095.3372833
19. Horneber, D., Laumer, S. Algorithmic Accountability. Bus Inf Syst Eng 65, 723–730 (2023). https://doi.org/10.1007/s12599-023-00817-8
20. Lepri, B., Oliver, N., Letouzé, E. Pentland, A. and Vinck, P. (2018). “Fair, Transparent, and Accountable Algorithmic Decision-making Processes,” Philos. Technol. 31, 611–627. https://doi.org/10.1007/s13347-017-0279-x
21. Vandelanotte, C., Trost, S., Hodgetts, D., Imam, T., Rashid, M., To, Q. G. and Maher, C. 2023. “Increasing physical activity using an just-in-time adaptive digital assistant supported by machine learning: A novel approach for hyper-personalised mHealth interventions,” Journal of Biomedical Informatics, 144, 104435.
22. Han, S., Zhang, R. F., Shi, L., Richie, R., Liu, H., Tseng, A., Quan, W., Ryan, N., Brent, D., and Tsui, F. R. 2022. “Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing,” Journal of biomedical informatics, 127, 103984. https://doi.org/10.1016/j.jbi.2021.103984
23. Sangers, T., Reeder, S., van der Vet, S., Jhingoer, S., Mooyaart, A., Siegel, D. M., & Wakkee, M. (2022). Validation of a market-approved artificial intelligence mo- bile health app for skin cancer screening: a prospective multicenter diagnostic ac- curacy study. Dermatology, 238(4), 649-656.
24. Xu, Z., Biswas, B., Li, L., & Amzal, B. (2023). AI/ML in Precision Medicine: A Look Beyond the Hype. Therapeutic Innovation & Regulatory Science, 1-6.
25. Xiaofeng Wang, Zhenshun Cheng, “Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations”, Chest, Volume 158, Issue 1, Supplement, 2020, Pages S65-S71, ISSN 0012-3692, https://doi.org/10.1016/j.chest.2020.03.012.