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Adoption of Artificial Intelligence Technologies by Often Marginalized Populations

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Social Vulnerability to COVID-19

Abstract

Artificial Intelligence (AI) has found its application in many aspects of our lives. The COVID-19 pandemic has further allowed AI to play an increasingly important and beneficial role in our society, but it has also exposed the limitation of AI, particularly related to marginalized populations. This chapter first provides an overview of AI and equity pre-COVID, and then discusses what we know about AI during COVID-19. At the end, we conduct a systematic literature review to examine marginalized populations and their use of AI technologies during COVID-19. The populations examined in this review are children, older adults, people with disabilities, racial and ethnic minorities (in a country or region), low-income, gender, or general marginalized populations. The results indicate a huge gap for research on the use, adoption, and perception of AI technologies by communities that have previously experienced inequities in AI and COVID-19.

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References

  1. Ali, Z., Saleh, M., Al-Maadeed, S., Abou Elsaud, S., Khalifa, B., AlJa’am, J. M., & Massaro, D. (2021, April). Understand My World: An Interactive App for Children Learning Arabic Vocabulary. In 2021 IEEE Global Engineering Education Conference (EDUCON) (pp. 1143–1148). IEEE.

    Google Scholar 

  2. Atashzar, S. F., Carriere, J., & Tavakoli, M. (2021). How Can Intelligent Robots and Smart Mechatronic Modules Facilitate Remote Assessment, Assistance, and Rehabilitation for Isolated Adults with Neuro-Musculoskeletal Conditions? Frontiers in Robotics and AI, 8.

    Google Scholar 

  3. Bennett Gayle, D., X. Yuan, and T. Knight. (2021). Coronavirus Pandemic: The Use of Technology for Education, Employment, Livelihoods. Journal Assistive Technology. doi: https://doi.org/10.1080/10400435.2021.1980836. Epub ahead of print. PMID: 34813718.

  4. Castets-Renard, C., & Fournier-Tombs, E. (2020). AI must be used responsibly with vulnerable populations. Policy Options. https://policyoptions.irpp.org/magazines/september-2020/ai-must-be-used-responsibly-with-vulnerable-populations/.

  5. Chen, I. Y., Joshi, S., & Ghassemi, M. (2020). Treating health disparities with artificial intelligence. Nature medicine, 26(1), 16–17.

    Article  Google Scholar 

  6. Chuma, E. L., Roger, L. L. B., De Oliveira, G. G., Iano, Y., & Pajuelo, D. (2020, December). Internet of things (IoT) privacy–protected, fall-detection system for the elderly using the radar sensors and deep learning. In 2020 IEEE International Smart Cities Conference (ISC2) (pp. 1–4). IEEE.

    Google Scholar 

  7. Cooper, S., Di Fava, A., Vivas, C., Marchionni, L., & Ferro, F. (2020, August). ARI: The social assistive robot and companion. In 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 745–751). IEEE.

    Google Scholar 

  8. Cui, C., Wang, C., Dong, X., & An, Z. (2021, April). Intelligent Mobile Body Temperature Monitoring & Management System For Nursing Home. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. 812–818).

    Google Scholar 

  9. Dankwa-Mullan, I., Scheufele, E. L., Matheny, M. E., Quintana, Y., Chapman, W. W., Jackson, G., & South, B. R. (2021). A Proposed Framework on Integrating Health Equity and Racial Justice into the Artificial Intelligence Development Lifecycle. Journal of Health Care for the Poor and Underserved, 32(2), 300–317.

    Article  Google Scholar 

  10. Ding, W., Nayak, J., Swapnarekha, H., Abraham, A., Naik, B., & Pelusi, D. (2021). Fusion of intelligent learning for COVID-19: A state-of-the-art review and analysis on real medical data. Neurocomputing, 457, 40–66.

    Google Scholar 

  11. Enache, B. A., Grigorescu, S. D., Adochiei, I. R., Eanche, C. D., Voiculescu, D. I., Argatu, V., ... & Stoica, C. (2020, June). Didactic Implementation of a Real-Time Biomonitoring Platform. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1–4). IEEE.

    Google Scholar 

  12. Evans, M. K. (2020). Covid’s color line—infectious disease, inequity, and racial justice. New England Journal of Medicine, 383(5), 408–410.

    Article  Google Scholar 

  13. Fordham, M., Lovekamp, W. E., Thomas, D. S., & Phillips, B. D. (2013). Understanding social vulnerability. Social vulnerability to disasters, 2, 1–29. CRC Press.

    Google Scholar 

  14. Gil-García, J. Ramón, Sharon S. Dawes, and Theresa A. Pardo. 2018. Digital government and public management research: finding the crossroads. Public Management Review 20 (5): 633–646.

    Article  Google Scholar 

  15. Glauser, W. (2020). AI in health care: Improving outcomes or threatening equity? Can Med Assoc. DOI: https://doi.org/10.1503/cmaj.1095838.

    Article  Google Scholar 

  16. Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., & Greenspan, H. (2020). Coronavirus detection and analysis on chest ct with deep learning. arXiv preprint arXiv:2004.02640.

  17. Holstein, K., & Doroudi, S. (2021). Equity and Artificial Intelligence in Education: Will” AIEd” Amplify or Alleviate Inequities in Education? arXiv preprint arXiv:2104.12920.

  18. Ionescu, V. M., & Enescu, F. M. (2021, July). Using information technology in Romanian preschool environment during the COVID-19 pandemic. In 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1–6). IEEE.

    Google Scholar 

  19. Jaana, M., & Paré, G. (2020). Comparison of mobile health technology use for Self-Tracking between older adults and the general adult population in Canada: cross-sectional survey. JMIR mHealth and uHealth, 8(11), e24718.

    Article  Google Scholar 

  20. Lee, N. T. (2018). Detecting racial bias in algorithms and machine learning. Journal of Information, Communication and Ethics in Society.

    Google Scholar 

  21. Luengo-Oroz, M., Bullock, J., Pham, K. H., Lam, C. S. N., & Luccioni, A. (2021). From Artificial Intelligence Bias to Inequality in the Time of COVID-19. IEEE Technology and Society Magazine, 40(1), 71–79.

    Article  Google Scholar 

  22. Mahdi, H., Saleh, S., Sanoubari, E., & Dautenhahn, K. (2021, August). User-Centered Social Robot Design: Involving Children with Special Needs in an Online World. In 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN) (pp. 844–851). IEEE.

    Google Scholar 

  23. Mitra, R. (2020, May 7). How Artificial Intelligence can help Vulnerable Populations during Pandemics. Medium. https://becominghuman.ai/how-artificial-intelligence-can-help-vulnerable-populations-during-pandemics-aef530154cdd.

  24. Moher, D., Altman, D. G., Liberati, A., & Tetzlaff, J. (2011). PRISMA statement. Epidemiology, 22(1), 128.

    Google Scholar 

  25. NITRD (2019). The National Artificial Intelligence Research and Development Strategic Plan: 2019 Update (nitrd.gov).

    Google Scholar 

  26. Office of the Privacy Commissioner of Canada. (2020, March 20). Privacy and the COVID-19 outbreak. https://www.priv.gc.ca/en/privacy-topics/health-genetic-and-other-body-information/health-emergencies/gd_covid_202003/.

  27. Ondili, M. (2021, January 20). The Impact of the AI Divide on Vulnerable Groups. Centre for Intellectual Property and Information Technology Law. https://cipit.strathmore.edu/the-impact-of-the-ai-divide-on-vulnerable-groups/.

  28. O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (1st edition). Crown.

    Google Scholar 

  29. Pan, D., Sze, S., Minhas, J. S., Bangash, M. N., Pareek, N., Divall, P., ... & Pareek, M. (2020). The impact of ethnicity on clinical outcomes in COVID-19: a systematic review. EClinicalMedicine, 23, 100404.

    Google Scholar 

  30. Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Published in 2019 by the United Nations Educational, Scientific and Cultural Organization, 7, place de Fontenoy, 75352 Paris 07 SP, France.

    Google Scholar 

  31. Röösli, E., Rice, B., & Hernandez-Boussard, T. (2021). Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19. Journal of the American Medical Informatics Association, 28(1), 190–192.

    Article  Google Scholar 

  32. Shi, W., Peng, X., Liu, T., Cheng, Z., Lu, H., Yang, S., ... & Shan, F. (2021). A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. Annals of Translational Medicine, 9(3).

    Google Scholar 

  33. Smith, M. J., Axler, R., Bean, S., Rudzicz, F., & Shaw, J. (2020). Four equity considerations for the use of artificial intelligence in public health. Bulletin of the World Health Organization, 98(4), 290.

    Article  Google Scholar 

  34. Southgate, E. (2020). Artificial intelligence, ethics, equity and higher education: A ‘beginning-of-the-discussion’ paper. National Centre for Student Equity in Higher Education, Curtin University, and the University of Newcastle.

    Google Scholar 

  35. Suresh, H., & Guttag, J. V. (2019). A framework for understanding unintended consequences of machine learning. arXiv preprint arXiv:1901.10002.

  36. Winter, J. S., & Davidson, E. (2019). Governance of artificial intelligence and personal health information. Digital policy, regulation and governance. VOL. 21 NO. 3 2019, pp. 280–29.

    Google Scholar 

  37. Wisner, B., & Blaikie, P. (2004). Cannon T& Davis I. At risk: natural hazards, people's vulnerability and disasters.

    Google Scholar 

  38. Young, M. M., Bullock, J. B., & Lecy, J. D. (2019). Artificial discretion as a tool of governance: a framework for understanding the impact of artificial intelligence on public administration. Perspectives on Public Management and Governance, 2(4), 301–313.

    Google Scholar 

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Acknowledgements

Bennett et al. (2020). NSF CONVERGE Working Group, COVID-19 Global Research Registry for Public Health and Social Sciences Technological Innovations in Response to COVID-19. This COVID-19 Working Group effort was supported by the National Science Foundation-funded Social Science Extreme Events Research (SSEER) Network and the CONVERGE facility at the Natural Hazards Center at the University of Colorado Boulder (NSF Award #1841338).

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Authors and Affiliations

  1. University at Albany, State University of New York, Albany, NY, USA

    Xiaojun Yuan, DeeDee Bennett Gayle, Thora Knight & Elisabeth Dubois

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  1. Xiaojun Yuan
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  2. DeeDee Bennett Gayle
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  3. Thora Knight
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  4. Elisabeth Dubois
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Correspondence to Xiaojun Yuan .

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Editors and Affiliations

  1. College of Emergency Preparedness, Homeland Security, and Cybersecurity, University at Albany, State University of New York, Albany, NY, USA

    Xiaojun Yuan

  2. School of Information Management, Wuhan University, Wuhan, China

    Dan Wu

  3. College of Emergency Preparedness, Homeland Security, and Cybersecurity, University at Albany, State University of New York, Albany, NY, USA

    DeeDee Bennett Gayle

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Yuan, X., Bennett Gayle, D., Knight, T., Dubois, E. (2023). Adoption of Artificial Intelligence Technologies by Often Marginalized Populations. In: Yuan, X., Wu, D., Bennett Gayle, D. (eds) Social Vulnerability to COVID-19. Synthesis Lectures on Information Concepts, Retrieval, and Services. Springer, Cham. https://doi.org/10.1007/978-3-031-06897-3_3

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