AN EXTENSIVE ANALYSIS OF THE HURDLES IN EMBRACING AI AMONG PEOPLE WITH SPECIAL NEEDS USING AHP

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Published Apr 21, 2024
Dr. Sheetal Mahendher Dr. Sippee Bharadwaj

Abstract

This study aims to uncover the challenges to the mainstream adoption of AI (artificial intelligence) among people with special needs in India. AI has been widely used in real-time healthcare, education, and transportation situations; however, there is a digital divide in the ability to reap the benefits of AI applications for those with special needs due to various socioeconomic factors. The proposed work also intends to examine and undertake in-depth research using the Analytic Hierarchy Process (AHP) to discover, analyze, and offer an accessible overview of the issues surrounding the numerous socioeconomic and technical factors involved with the use of AI. This research will contribute significantly to addressing the ongoing challenges of the special need’s population in their use of AI in various real-time applications by addressing technical infrastructure limitations, cultural differences, and other economic concerns. It will also help to bridge the gap between AI and the special needs population by addressing these limitations. By giving attention to this unexplored field, this piece of research will provide a better foundation for how to take preventive measures and overcome the digital gap of AI among special needs from several perspectives.

How to Cite

Mahendher, D. S., & Bharadwaj, D. S. (2024). AN EXTENSIVE ANALYSIS OF THE HURDLES IN EMBRACING AI AMONG PEOPLE WITH SPECIAL NEEDS USING AHP . International Journal of the Analytic Hierarchy Process, 15(3). https://doi.org/10.13033/ijahp.v15i3.1178

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Keywords

Analytic Hierarchic Process

References
Arora, A., Gupta, S., Devi, C., & Walia, N. (2023). Customer experiences in the era of artificial intelligence (AI) in context to FinTech: a fuzzy AHP approach. Benchmarking: An International Journal 30(10), 4342-4369. Doi: https://doi.org/10.1108/BIJ-10-2021-0621.

Alrawad, M., Lutfi, A., Almaiah, M. A., Alsyouf, A., Al-Khasawneh, A. L., Arafa, H. M., ... & Tork, M. (2023). Managers’ perception and attitude toward financial risks associated with SMEs: Analytic hierarchy process approach. Journal of Risk and Financial Management, 16(2), 1-12. Doi: 10.3390/jrfm16020086.

Burton M.A. (2017). Bridging the digital divide for students with disabilities. Journal of Special Education Technology, 32(1), 46-52.

Burgstahler S. (2015). Universal design in higher education: From principles to practice. Cambridge, MA: Harvard Education Press.

Morrison, C., Cutrell, E., Dhareshwar, A., Doherty, K., Thieme, A., & Taylor, A. (2017, October). Imagining artificial intelligence applications with people with visual disabilities using tactile ideation. In Proceedings of the 19th international ACM sigaccess conference on computers and accessibility (pp. 81-90).

Cook, D.R., Staschak, S., & Green, W.T. (1990). Equitable allocation of livers for orthotopic transplantation: an application of the Analytic Hierarchy Process. European Journal of Operational Research, 48(1), 49-56. Doi: http://dx.doi.org/10.1016/0377-2217(90)90060.

Costa, J. F. S., Borges, A. R., & dos Santos Machado, T. (2016). Analytic hierarchy process applied to industrial location: A Brazilian perspective on jeans manufacturing. International Journal of the Analytic Hierarchy Process, 8(1), 77-91. Doi: https://doi.org/10.13033/ijahp.v8i1.210.

Cheever MA, Allison JP, Ferris AS, Finn OJ, Hastings BM, Hecht TT, et al. (2019). The prioritization of cancer antigens: a national cancer institute pilot project for the acceleration of translational research. Clinical Cancer Research, 15(17), 5323–37. Doi: 10.1158/1078-0432.CCR-09-0737.

Danner, M., Hummel, J. M., Volz, F., Van Manen, J. G., Wiegard, B., Dintsios, C. M., ... & IJzerman, M. J. (2011). Integrating patients' views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences. International Journal of Technology Assessment in Health Care, 27(4), 369–¬375.

De Martino A., Forcelloni M. (2017). Technology-enhanced learning to overcome the digital divide. Computers & Education, 106, 12-29.

Dolan J.G., Bordley D.R., Miller H. (1993). Diagnostic strategies in the management of acute upper gastrointestinal bleeding: patient and physician preferences. Journal of General Internal Medicine, 8, 525–529. Doi: https://doi.org/10.1007/BF02599632.

Dolan J.G. (1989). Medical decision making using the analytic hierarchy process: choice of initial antimicrobial therapy for acute pyelonephritis. Medical Decision Making, 9(1), 51–56. Doi: 10.1177/0272989X8900900109.
Dolan J.G., Boohaker E., Allison J., Imperiale T.F. (2013). Patients’ preferences and priorities regarding colorectal cancer screening. Medical Decision Making, 33(1), 59–70. Doi: 10.1177/0272989X12453502.
Drigas, S., & Ioannidou, R. (2012). Artificial Intelligence in special education: A decade review. International Journal of Engineering Education, 28(6), 1366-1372.

Engelbrecht, L., & De Beer, J.J. (2014). Access constraints experienced by physically disabled students at a South African higher education institution. Africa Education Review, 11(4), 544-562. Doi: http://dx.doi.org/10.1080/18146627.2014.935003.

Garg, S., & Sharma, S. (2020). Impact of artificial intelligence in special need education to promote inclusive pedagogy. International Journal of Information and Education Technology, 10(7), 523-527.Doi: http://dx.doi.org/10.18178/ijiet.2020.10.7.1418.

Garuti, C., Sandoval, M. (2006). The AHP: A multicriteria decision-making methodology for shiftwork prioritizing. Journal of Systems Science and Systems Engineering, 15(2), 189-200. Doi:10.1007/s11518-006-5007-5.

Gupta, K. P., Bhaskar, P., & Singh, S. (2017). Prioritization of factors influencing employee adoption of e-government using the analytic hierarchy process. Journal of Systems and Information Technology, 19(1/2), 116-137.

Grewal, D.S. (2014 ). A critical conceptual analysis of definitions of AI as applicable to computers. IOSR Journal of Computer Engineering, 16(2), 9-13.

Gul M., Guneri A.F. (2021). Hospital location selection: A systematic literature review on methodologies and applications. Hindawi Mathematics Problems in Engineering, 6682958. Doi: https://doi.org/10.1155/2021/6682958.

Holloway S.L., Valentine G. (2015). Cyberkids? Exploring children’s identities and social networks in online and offline worlds. Annals of the Association of American Geographers, 95(4), 793-817. Doi: http://dx.doi.org/10.1111/1467-8306.00292.

Hummel M., IJzerman M. (eds.). (2011). The past and future of the AHP in health care decision making. Proceedings of the International Symposium of the Analytic Hierarchy Process, 1-6. Doi: http://dx.doi.org/10.13033/isahp.y2011.079.

Laabidi, M. Jemni, L. J.B. Ayed, H.B. Brahim, & A.B. Jemaa (2013). Learning technologies for people with disabilities. Journal of King Saud University - Computer and Information Sciences, 26(1), 29-45. Doi:10.1016/j.jksuci.2013.10.005

Liberatore M.J., Nydick R.L. (2008). The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research, 189(1), 194–207. Doi: http://dx.doi.org/10.1016/j.ejor.2007.05.001.

Lussier-Desrochers, D., Normand, C. L., Romero-Torres, A., Lachapelle, Y., Godin-Tremblay, V., Dupont, M. È., ... & Bilodeau, P. (2017). Bridging the digital divide for people with intellectual disability. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 11(1), 1-20. Doi: https://doi.org/10.5817/CP2017-1-1

Mahajan, R., Gupta, P., & Singh, T. (2019). Massive open online courses: concept and implications. Indian Pediatrics, 56, 489-495.

Microsoft. (2017). Annual report: Letter to shareholders. [Online]. https://www.microsoft.com/investor/reports/ar17/index.html.

Millet, I., & Saaty, T. L. (2000). On the relativity of relative measures–accommodating both rank preservation and rank reversals in the AHP. European Journal of Operational Research, 121(1), 205-212.

Merhi, M.I., & Harfouche, A. (2023). Enablers of artificial intelligence adoption and implementation in production systems. International Journal of Production Research, 1-15. Doi: https://doi.org/10.1080/00207543.2023.2167014.

Michele C. McDonnall, J.L. Cmar, and Zhen S. McKnight. (2022). Beyond employment rates: Earnings of people with visual impairments. Journal of Visual Impairment & Blindness, 116(4), 526–532. Doi: http://dx.doi.org/10.1177/0145482x221121830.

Mohammed, H.J., & Daham, H. A. (2021). Analytic Hierarchy Process for evaluating flipped classroom learning. Computers, Materials & Continua, 66(3), 2229-2239 Doi:10.32604/cmc.2021.014445.

Nickerson, R.S., & Zodhiates, P.P. (Eds.). (2013). Technology in education: Looking toward 2020. London: Routledge.

Paksoy, S. (2017). Current approaches to Multi-Criteria Decision Making. Adana: Karahan Bookstore.

Reza Sadeghi, M., Mohammad Moghimi, S., & Ramezan, M. (2013). Identifying and prioritizing of effective constructs in readiness of knowledge management implementation by using fuzzy analytic hierarchy process (AHP). Journal of Knowledge-based Innovation in China, 5(1), 16-31.

Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology 15(3), 234–81. Doi: http://dx.doi.org/10.1016/0022-2496(77)90033-5.

Saaty, T.L. (1980). The analytic hierarchy processes. McGraw-Hill: New York.

Saaty, R.W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9, 161–76. Doi: http://dx.doi.org/10.1016/0270-0255(87)90473-8.

Saaty, T.L. (1991). Some mathematical concepts of the Analytic Hierarchy Process. Behaviormetrika, 18(29), 1–9. Doi: http://dx.doi.org/10.2333/bhmk.18.29_1

Saaty, T.L., & Vargas, L.G. (2006). Decision-making with the analytic network process (Vol. 282). Berlin, Germany: Springer Science+ Business Media, LLC.

Sharma, N., Yadav, V.P., & Sharma, A. (2021). Attitudes and empathy of youth towards physically disabled persons. Heliyon, 7(8), e07852. Doi: 10.1016/j.heliyon.2021.e07852

Singh, T., Patnaik, A., Chauhan, R., & Chauhan, P. (2016). Selection of brake friction materials using hybrid analytical hierarchy process and vise kriterijumska optimization compromise presence approach. Polymer Composites, 39(5), 1655-1662. Doi: https://doi.org/10.1002/pc.24113

Subasi H. (2011). Comparison of TOPSIS and AHP methods used in multi-criteria Decision process and an application [Thesis]. Marmara University, Istanbul.

Trewin, S., Basson, S., Muller, M., Branham, S., Treviranus, J., Gruen, D., ... & Manser, E. (2019). Considerations for AI fairness for people with disabilities. AI Matters, 5(3), 40-63. Doi: http://dx.doi.org/10.1145/3362077.3362086

UNESCO. (2013). UNESCO global report: Opening new avenues for empowerment: ICTs to access information and knowledge for persons with disabilities. United Nations Educational, Scientific and Cultural Organization.

UNESCO. (2019). The ICT CFT training framework: A guide for policymakers, project managers, and integrated teacher training administrators.[Online]. https://unesdoc.unesco.org/ark:/48223/pf0000369796

Vargas, L. G. (1982). Reciprocal matrices with random coefficients. Mathematical modelling, 3(1), 69-81.

WHO. (2011). World Report on Disability. [Online]. https://www.who.int/disabilities/world_report/2011/report.pdf.

World Bank. (2019). Disability-inclusive education. [Online]. https://www.worldbank.org/en/topic/disability-inclusive-development/brief/disability-inclusive-education

Xu, S.L., Yeyao, T., & Shabaz, M. (2023). Multi-criteria decision making for determining best teaching method using fuzzy analytical hierarchy process. Soft Computing, 27(6), 2795-2807. Doi: http://dx.doi.org/10.1007/s00500-022-07554-2


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