Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market

Mahmut Özer; Matjaz Perc; H. Eren Suna

doi:10.26650/JECS2023-1415085

Derleme

Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market

Yıl 2024, Sayı: 69, 159 - 168, 13.06.2024

Mahmut Özer , Matjaz Perc , H. Eren Suna

https://doi.org/10.26650/JECS2023-1415085

Öz

Artificial intelligence (AI) is now present in nearly every aspect of our daily lives. Furthermore, while this AI augmentation is generally beneficial, or at worst, nonproblematic, some instances warrant attention. In this study, we argue that AI bias resulting from training data sets in the labor market can significantly amplify minor inequalities, which later in life manifest as permanently lost opportunities and social status and wealth segregation. The Matthew effect is responsible for this phenomenon, except that the focus is not on the rich getting richer, but on the poor becoming even poorer. We demonstrate how frequently changing expectations for skills, competencies, and knowledge lead to AI failing to make impartial hiring decisions. Specifically, the bias in the training data sets used by AI affects the results, causing the disadvantaged to be overlooked while the privileged are frequently chosen. This simple AI bias contributes to growing social inequalities by reinforcing the Matthew effect, and it does so at much faster rates than previously. We assess these threats by studying data from various labor fields, including justice, security, healthcare, human resource management, and education.

Anahtar Kelimeler

artificial intelligence, bias, Matthew effect, social inequality, misinformation

Kaynakça

Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. NBER Working Paper 24196. Cambridge: National Bureau of Economic Research. google scholar
Aldoseri, A., Al-Khalifa, K.N., & Hamouda, A.M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. google scholar
Aldoseri, A., Al-Khalifa, K.N., & Hamouda, A.M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. google scholar
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. google scholar
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. google scholar
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31-50. google scholar
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31-50. google scholar
Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. google scholar
Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. google scholar
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284-286. google scholar
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284-286. google scholar
Bagaric, M. (2016). Three things that a baseline study shows don’t cause indigenous over-imprisonment: Three things that might but shouldn’t and three reforms that will reduce indigenous over-imprisonment. Harvard J Racial & Ethnic Just, 103. google scholar
Bagaric, M. (2016). Three things that a baseline study shows don’t cause indigenous over-imprisonment: Three things that might but shouldn’t and three reforms that will reduce indigenous over-imprisonment. Harvard J Racial & Ethnic Just, 103. google scholar
Bagaric, M., Hunter, D., & Stobbs, N. (2019). Erasing the bias against using artificial intelligence to predict future criminality: Algorithms are color blind and never dash. University of Cincinnati Law Review, 88(4), 1037-1081. google scholar
Bagaric, M., Hunter, D., & Stobbs, N. (2019). Erasing the bias against using artificial intelligence to predict future criminality: Algorithms are color blind and never dash. University of Cincinnati Law Review, 88(4), 1037-1081. google scholar
Bagaric, M., Svilar, J., Bull, M., Hunter, D., & Stobbs, N. (2022). The solution to the pervasive bias and discrimination in the criminal justice system: Transparent and fair artificial intelligence. American Criminal Law Review, 59(95), 95-148. google scholar
Bagaric, M., Svilar, J., Bull, M., Hunter, D., & Stobbs, N. (2022). The solution to the pervasive bias and discrimination in the criminal justice system: Transparent and fair artificial intelligence. American Criminal Law Review, 59(95), 95-148. google scholar
Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. google scholar
Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. google scholar
Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. google scholar
Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. google scholar
Bird, K. A., Castleman, B. L., & Song, Y. (2023). Are algorithms biased in education? Exploring racial bias in predicting community college student success. Ed Working Paper No. 23-717. Annenberg Institute: Brown University. google scholar
Bird, K. A., Castleman, B. L., & Song, Y. (2023). Are algorithms biased in education? Exploring racial bias in predicting community college student success. Ed Working Paper No. 23-717. Annenberg Institute: Brown University. google scholar
Bourdieu, P., & Passeron, J. C. (2000). Reproduction in education, society and culture. Sage Publishing. google scholar
Bourdieu, P., & Passeron, J. C. (2000). Reproduction in education, society and culture. Sage Publishing. google scholar
Bozkurt, V., & Gursoy, D. (2023). The artificial intelligence paradox: Opportunity or threat for humanity?, International Journal of Human-Computer Interaction, doi: 10.1080/10447318.2023.2297114 google scholar
Bozkurt, V., & Gursoy, D. (2023). The artificial intelligence paradox: Opportunity or threat for humanity?, International Journal of Human-Computer Interaction, doi: 10.1080/10447318.2023.2297114 google scholar
Castelluccia, C., & Le Metayer, D. (2019). Understanding algorithmic decision-making: opportunities and challenges. European Parliament. google scholar
Castelluccia, C., & Le Metayer, D. (2019). Understanding algorithmic decision-making: opportunities and challenges. European Parliament. google scholar
Celi L. A., Cellini, J., Charpignon, M. L., Dee, E. C., Dernoncourt, F., Eber, R. et al (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities: A global review. PLOS Digit Health, 31; 1(3), e0000022. google scholar
Celi L. A., Cellini, J., Charpignon, M. L., Dee, E. C., Dernoncourt, F., Eber, R. et al (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities: A global review. PLOS Digit Health, 31; 1(3), e0000022. google scholar
Charpignon, M. L., Byers, J., Cabral, S., Celi, L. A., Fernandes, F., Gallifant, J. et al. (2023). Critical bias in critical care devices. Critical Care Clinics, 39(4), 795-813. google scholar
Charpignon, M. L., Byers, J., Cabral, S., Celi, L. A., Fernandes, F., Gallifant, J. et al. (2023). Critical bias in critical care devices. Critical Care Clinics, 39(4), 795-813. google scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. The Journal of Artificial Intelligence Research, 16, 321-357. google scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. The Journal of Artificial Intelligence Research, 16, 321-357. google scholar
Coleman, J. S., Campbell , E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. US Office of Education. google scholar
Coleman, J. S., Campbell , E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. US Office of Education. google scholar
Colon-Rodriguez, C. J. (2023, 12 July). Shedding light on healthcare algorithmic and artificial intelligence bias. US Department of Health and Human Services Office of Minority Health. Retrieved from https://minorityhealth.hhs.gov/news/shedding-light-healthcare-algorithmic-and-artificial-intelligence-bias google scholar
Colon-Rodriguez, C. J. (2023, 12 July). Shedding light on healthcare algorithmic and artificial intelligence bias. US Department of Health and Human Services Office of Minority Health. Retrieved from https://minorityhealth.hhs.gov/news/shedding-light-healthcare-algorithmic-and-artificial-intelligence-bias google scholar
Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4, eaao5580. google scholar
Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4, eaao5580. google scholar
Edwards, B., & Cheok, A. D. (2018). Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence, 32(4), 345-360. google scholar
Edwards, B., & Cheok, A. D. (2018). Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence, 32(4), 345-360. google scholar
Erdi, P. (2020). Ranking: The unwritten rules of the social game we all play. Oxford University Press. google scholar
Erdi, P. (2020). Ranking: The unwritten rules of the social game we all play. Oxford University Press. google scholar
European Commission (2022). Mitigating diversity biases ofAI in the labor market. Horizon Europe Project Report. google scholar
European Commission (2022). Mitigating diversity biases ofAI in the labor market. Horizon Europe Project Report. google scholar
European Network Against Racism (2020). Artificial intelligence in HR: How to address racial biases and algorithmic discrimination in HR?. ENAR Publishing. google scholar
European Network Against Racism (2020). Artificial intelligence in HR: How to address racial biases and algorithmic discrimination in HR?. ENAR Publishing. google scholar
European Parliament (2020). Skills and jobs for future labour markets: European policies and Skills Agendas 2010-2020. EMPL in Focus. google scholar
European Parliament (2020). Skills and jobs for future labour markets: European policies and Skills Agendas 2010-2020. EMPL in Focus. google scholar
Franca, T. J. F., Mamede, H. S., Barroso, J. M. P., & dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organizational context. Heliyon, 9(4), e14694. google scholar
Franca, T. J. F., Mamede, H. S., Barroso, J. M. P., & dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organizational context. Heliyon, 9(4), e14694. google scholar
Hale, K. E. (2020). Using artificial intelligence to circumvent the teacher shortage in special education: A phenomenological investigation. Doctoral Dissertation, Liberty University, USA. google scholar
Hale, K. E. (2020). Using artificial intelligence to circumvent the teacher shortage in special education: A phenomenological investigation. Doctoral Dissertation, Liberty University, USA. google scholar
Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(19), 324-327. google scholar
Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(19), 324-327. google scholar
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. In Data ethics: Building trust: How digital technologies can serve humanity (pp. 621-653). Globethics Publications. google scholar
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. In Data ethics: Building trust: How digital technologies can serve humanity (pp. 621-653). Globethics Publications. google scholar
IFOW (2020). Artificial intelligence in hiring: Assessing impacts on equality. Institute for the Future of Work. Retrieved from https://assets-global.website-files.com/64d5f73a7fc5e8a240310c4d/64d5f73b7fc5e8a240310ea0_5f71d338891671faa84de443_IFOW%2B-%2BAssessing%2Bimpacts%2Bon%2Bequality.pdf google scholar
IFOW (2020). Artificial intelligence in hiring: Assessing impacts on equality. Institute for the Future of Work. Retrieved from https://assets-global.website-files.com/64d5f73a7fc5e8a240310c4d/64d5f73b7fc5e8a240310ea0_5f71d338891671faa84de443_IFOW%2B-%2BAssessing%2Bimpacts%2Bon%2Bequality.pdf google scholar
ILO (2023). Artificial intelligence in human resource management: A challenge for the human-centred agenda?. ILO Working Paper 95. google scholar
ILO (2023). Artificial intelligence in human resource management: A challenge for the human-centred agenda?. ILO Working Paper 95. google scholar
Kamulegeya, L. H., Okello, M., Bwanika, J. M., et al. (2019). Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. African Health Sciences, 23(2), 753-763. google scholar
Kamulegeya, L. H., Okello, M., Bwanika, J. M., et al. (2019). Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. African Health Sciences, 23(2), 753-763. google scholar
King, M. (2022). Harmful biases in artificial intelligence. The Lancet Psychiatry, 9(11), E48. google scholar
King, M. (2022). Harmful biases in artificial intelligence. The Lancet Psychiatry, 9(11), E48. google scholar
Kizilcec, R.F., & Lee, H. (2022). Algorithmic fairness in education. In Holmes W., Porayska-Pomsta, K. (Eds), The ethics of artificial intelligence in education (pp. 174-202). Taylor & Francis. google scholar
Kizilcec, R.F., & Lee, H. (2022). Algorithmic fairness in education. In Holmes W., Porayska-Pomsta, K. (Eds), The ethics of artificial intelligence in education (pp. 174-202). Taylor & Francis. google scholar
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res, 13, 795-848. google scholar
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res, 13, 795-848. google scholar
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci USA, 117(23), 12592-12594. google scholar
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci USA, 117(23), 12592-12594. google scholar
Li, W., Aste, T., Caccioli, F., & Livan, G. (2019). Early co-authorship with top scientists predicts success in academic careers, Nature Communications, 10, 51-70. google scholar
Li, W., Aste, T., Caccioli, F., & Livan, G. (2019). Early co-authorship with top scientists predicts success in academic careers, Nature Communications, 10, 51-70. google scholar
Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. google scholar
Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. google scholar
Mahmud, H. A. K. M., Islam, N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390. google scholar
Mahmud, H. A. K. M., Islam, N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390. google scholar
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. google scholar
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. google scholar
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. google scholar
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. google scholar
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53-63. google scholar
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53-63. google scholar
Mittermaier, M., Raza, M. M., Kvedar, J. C. (2023). Bias in AI-based models for medical applications: Challenges and mitigation strategies. NPJ Digital Medicine, 6, 113. google scholar
Mittermaier, M., Raza, M. M., Kvedar, J. C. (2023). Bias in AI-based models for medical applications: Challenges and mitigation strategies. NPJ Digital Medicine, 6, 113. google scholar
Napierala, J., Kvetan, V. (2023). Changing job skills in a changing world. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of computational social science for policy (pp. 243-259). Springer, Cham. google scholar
Napierala, J., Kvetan, V. (2023). Changing job skills in a changing world. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of computational social science for policy (pp. 243-259). Springer, Cham. google scholar
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., et al. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278. google scholar
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., et al. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278. google scholar
Ntoutsi, E., Fafalios, P., Gadiraju, U., et al. (2020). Bias in data-driven artificial intelligence systems-an introductory survey. WIREs Data Mining Knowl Discov, 10(3), e1356. google scholar
Ntoutsi, E., Fafalios, P., Gadiraju, U., et al. (2020). Bias in data-driven artificial intelligence systems-an introductory survey. WIREs Data Mining Knowl Discov, 10(3), e1356. google scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447-453. google scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447-453. google scholar
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. google scholar
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. google scholar
OECD (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. google scholar
OECD (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. google scholar
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books. google scholar
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books. google scholar
Oracle (2019). AI in human resources: The time is now. Retrieved from https://www.oracle.com/a/ocom/docs/applications/hcm/oracle-ai-in-hr-wp.pdf google scholar
Oracle (2019). AI in human resources: The time is now. Retrieved from https://www.oracle.com/a/ocom/docs/applications/hcm/oracle-ai-in-hr-wp.pdf google scholar
Özer, M., & Perc, M. (2022). Improving equality in the education system of Türkiye. İstanbul University Journal of Sociology, 2022, 42(2), 325-334. google scholar
Özer, M., & Perc, M. (2022). Improving equality in the education system of Türkiye. İstanbul University Journal of Sociology, 2022, 42(2), 325-334. google scholar
Özer, M. (2023). The Matthew effect Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. google scholar
Özer, M. (2023). The Matthew effect Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. google scholar
Perc, M. (2014). The Matthew effect in empirical data. Journal of Royal Society Interface, 11(98), 20140378. google scholar
Perc, M. (2014). The Matthew effect in empirical data. Journal of Royal Society Interface, 11(98), 20140378. google scholar
Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. google scholar
Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. google scholar
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. google scholar
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. google scholar
Renz, A., & Hilbig, R. (Eds.). (2023). Digital transformation of educational institutions accelerated by covid-19: A digital dynamic capabilities approach. Emerald Publishing. google scholar
Renz, A., & Hilbig, R. (Eds.). (2023). Digital transformation of educational institutions accelerated by covid-19: A digital dynamic capabilities approach. Emerald Publishing. google scholar
Rigney, D. (2010). The Matthew effect: How advantage begets further advantage. Columbia University Press. New York. google scholar
Rigney, D. (2010). The Matthew effect: How advantage begets further advantage. Columbia University Press. New York. google scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance. Psychological Bulletin, 135(1), 1-22. google scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance. Psychological Bulletin, 135(1), 1-22. google scholar
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Med, 27, 2176-2182. google scholar
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Med, 27, 2176-2182. google scholar
Smith, H. (2020). Algorithmic bias: should students pay the price? AI & Society, 35, 1077-1078. google scholar
Smith, H. (2020). Algorithmic bias: should students pay the price? AI & Society, 35, 1077-1078. google scholar
Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute. google scholar
Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute. google scholar
Srinivas, N. (2023, February 24). The ethical debate of AI in criminal justice: Balancing efficiency and human rights. Manage Engine Insights. google scholar
Srinivas, N. (2023, February 24). The ethical debate of AI in criminal justice: Balancing efficiency and human rights. Manage Engine Insights. google scholar
Retrieved from https://insights.manageengine.com/artificial-intelligence/the-ethical-debate-of-ai-in-criminal-justice-balancing-efficiency-and-human-rights// google scholar
Retrieved from https://insights.manageengine.com/artificial-intelligence/the-ethical-debate-of-ai-in-criminal-justice-balancing-efficiency-and-human-rights// google scholar
Stefan, V. (2019). Artificial intelligence and its impact on young people. Seminar Report from the Council of Europe. google scholar
Stefan, V. (2019). Artificial intelligence and its impact on young people. Seminar Report from the Council of Europe. google scholar
Storm, K. I. L., Reiss, L. K., Guenther, E. A., Clar-Novak, M., & Muhr, S.L. (2023). Unconscious bias in the HRM literature: Towards a critical-reflexive approach. Human Resource Management Review, 33(3), 100969. google scholar
Storm, K. I. L., Reiss, L. K., Guenther, E. A., Clar-Novak, M., & Muhr, S.L. (2023). Unconscious bias in the HRM literature: Towards a critical-reflexive approach. Human Resource Management Review, 33(3), 100969. google scholar
Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown: New York. google scholar
Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown: New York. google scholar
Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economics culture and Society, 61, 41-64. google scholar
Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economics culture and Society, 61, 41-64. google scholar
Tuffaha, M. (2023). The impact of artificial intelligence bias on human resource management functions: Systematic literature review and future research directions. European Journal of Business and Innovation Research, 11(4), 35-58. google scholar
Tuffaha, M. (2023). The impact of artificial intelligence bias on human resource management functions: Systematic literature review and future research directions. European Journal of Business and Innovation Research, 11(4), 35-58. google scholar
Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40, 665-687. google scholar
Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40, 665-687. google scholar
UNESCO (2023). The teachers we need for the education we want: The global imperative to reverse the teacher shortage. UNESCO & Education 2030 Factsheet. google scholar
UNESCO (2023). The teachers we need for the education we want: The global imperative to reverse the teacher shortage. UNESCO & Education 2030 Factsheet. google scholar
Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems-A systematic literature review. International Journal of Information Management Data Insights, 3, 100165. google scholar
Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems-A systematic literature review. International Journal of Information Management Data Insights, 3, 100165. google scholar
Vicente, L., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13, 15737. google scholar
Vicente, L., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13, 15737. google scholar
Zhang, J., Zhang, Z. M. (2023). Ethics and governance of trustwothy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. google scholar
Zhang, J., Zhang, Z. M. (2023). Ethics and governance of trustwothy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. google scholar
Zuckerman, H. A. (1977). Scientific elite: Nobel laureates in the United States. New York: Free Press. google scholar
Zuckerman, H. A. (1977). Scientific elite: Nobel laureates in the United States. New York: Free Press. google scholar
Zuckerman, H. (1989). Accumulation of advantage and disadvantage: The theory and its intellectual biography. In Mongardini, C., Tabboni, S. (Eds), Robert K. Merton and contemporary sociology (pp. 153-176). New Brunswick, NJ: Transaction. google scholar
Zuckerman, H. (1989). Accumulation of advantage and disadvantage: The theory and its intellectual biography. In Mongardini, C., Tabboni, S. (Eds), Robert K. Merton and contemporary sociology (pp. 153-176). New Brunswick, NJ: Transaction. google scholar

Yıl 2024, Sayı: 69, 159 - 168, 13.06.2024

Mahmut Özer , Matjaz Perc , H. Eren Suna

https://doi.org/10.26650/JECS2023-1415085

Öz

Kaynakça

Acemoglu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. NBER Working Paper 24196. Cambridge: National Bureau of Economic Research. google scholar
Aldoseri, A., Al-Khalifa, K.N., & Hamouda, A.M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. google scholar
Aldoseri, A., Al-Khalifa, K.N., & Hamouda, A.M. (2023). Re-thinking data strategy and integration for artificial intelligence: Concepts, opportunities, and challenges. Applied Sciences, 13(12), 7082. google scholar
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. google scholar
Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. ProPublica. google scholar
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31-50. google scholar
Agrawal, A., Gans, J. S., & Goldfarb, A. (2019). Artificial intelligence: The ambiguous labor market impact of automating prediction. Journal of Economic Perspectives, 33(2), 31-50. google scholar
Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. google scholar
Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. google scholar
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284-286. google scholar
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284-286. google scholar
Bagaric, M. (2016). Three things that a baseline study shows don’t cause indigenous over-imprisonment: Three things that might but shouldn’t and three reforms that will reduce indigenous over-imprisonment. Harvard J Racial & Ethnic Just, 103. google scholar
Bagaric, M. (2016). Three things that a baseline study shows don’t cause indigenous over-imprisonment: Three things that might but shouldn’t and three reforms that will reduce indigenous over-imprisonment. Harvard J Racial & Ethnic Just, 103. google scholar
Bagaric, M., Hunter, D., & Stobbs, N. (2019). Erasing the bias against using artificial intelligence to predict future criminality: Algorithms are color blind and never dash. University of Cincinnati Law Review, 88(4), 1037-1081. google scholar
Bagaric, M., Hunter, D., & Stobbs, N. (2019). Erasing the bias against using artificial intelligence to predict future criminality: Algorithms are color blind and never dash. University of Cincinnati Law Review, 88(4), 1037-1081. google scholar
Bagaric, M., Svilar, J., Bull, M., Hunter, D., & Stobbs, N. (2022). The solution to the pervasive bias and discrimination in the criminal justice system: Transparent and fair artificial intelligence. American Criminal Law Review, 59(95), 95-148. google scholar
Bagaric, M., Svilar, J., Bull, M., Hunter, D., & Stobbs, N. (2022). The solution to the pervasive bias and discrimination in the criminal justice system: Transparent and fair artificial intelligence. American Criminal Law Review, 59(95), 95-148. google scholar
Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. google scholar
Baker, R. S., & Hawn, A. (2021). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 32, 1052-1092. google scholar
Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
Barocas, S., & Selbst, A.D. (2016). Big data’s disparate impact. California Law Review, 104, 671-732. google scholar
Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. google scholar
Bates, D.W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: Using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131. google scholar
Bird, K. A., Castleman, B. L., & Song, Y. (2023). Are algorithms biased in education? Exploring racial bias in predicting community college student success. Ed Working Paper No. 23-717. Annenberg Institute: Brown University. google scholar
Bird, K. A., Castleman, B. L., & Song, Y. (2023). Are algorithms biased in education? Exploring racial bias in predicting community college student success. Ed Working Paper No. 23-717. Annenberg Institute: Brown University. google scholar
Bourdieu, P., & Passeron, J. C. (2000). Reproduction in education, society and culture. Sage Publishing. google scholar
Bourdieu, P., & Passeron, J. C. (2000). Reproduction in education, society and culture. Sage Publishing. google scholar
Bozkurt, V., & Gursoy, D. (2023). The artificial intelligence paradox: Opportunity or threat for humanity?, International Journal of Human-Computer Interaction, doi: 10.1080/10447318.2023.2297114 google scholar
Bozkurt, V., & Gursoy, D. (2023). The artificial intelligence paradox: Opportunity or threat for humanity?, International Journal of Human-Computer Interaction, doi: 10.1080/10447318.2023.2297114 google scholar
Castelluccia, C., & Le Metayer, D. (2019). Understanding algorithmic decision-making: opportunities and challenges. European Parliament. google scholar
Castelluccia, C., & Le Metayer, D. (2019). Understanding algorithmic decision-making: opportunities and challenges. European Parliament. google scholar
Celi L. A., Cellini, J., Charpignon, M. L., Dee, E. C., Dernoncourt, F., Eber, R. et al (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities: A global review. PLOS Digit Health, 31; 1(3), e0000022. google scholar
Celi L. A., Cellini, J., Charpignon, M. L., Dee, E. C., Dernoncourt, F., Eber, R. et al (2022). Sources of bias in artificial intelligence that perpetuate healthcare disparities: A global review. PLOS Digit Health, 31; 1(3), e0000022. google scholar
Charpignon, M. L., Byers, J., Cabral, S., Celi, L. A., Fernandes, F., Gallifant, J. et al. (2023). Critical bias in critical care devices. Critical Care Clinics, 39(4), 795-813. google scholar
Charpignon, M. L., Byers, J., Cabral, S., Celi, L. A., Fernandes, F., Gallifant, J. et al. (2023). Critical bias in critical care devices. Critical Care Clinics, 39(4), 795-813. google scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. The Journal of Artificial Intelligence Research, 16, 321-357. google scholar
Chawla, N. V., Bowyer, K. W., Hall, L. O., Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. The Journal of Artificial Intelligence Research, 16, 321-357. google scholar
Coleman, J. S., Campbell , E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. US Office of Education. google scholar
Coleman, J. S., Campbell , E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. (1966). Equality of educational opportunity. US Office of Education. google scholar
Colon-Rodriguez, C. J. (2023, 12 July). Shedding light on healthcare algorithmic and artificial intelligence bias. US Department of Health and Human Services Office of Minority Health. Retrieved from https://minorityhealth.hhs.gov/news/shedding-light-healthcare-algorithmic-and-artificial-intelligence-bias google scholar
Colon-Rodriguez, C. J. (2023, 12 July). Shedding light on healthcare algorithmic and artificial intelligence bias. US Department of Health and Human Services Office of Minority Health. Retrieved from https://minorityhealth.hhs.gov/news/shedding-light-healthcare-algorithmic-and-artificial-intelligence-bias google scholar
Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4, eaao5580. google scholar
Dressel, J., & Farid, H. (2018). The accuracy, fairness, and limits of predicting recidivism. Science Advances, 4, eaao5580. google scholar
Edwards, B., & Cheok, A. D. (2018). Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence, 32(4), 345-360. google scholar
Edwards, B., & Cheok, A. D. (2018). Why not robot teachers: Artificial intelligence for addressing teacher shortage. Applied Artificial Intelligence, 32(4), 345-360. google scholar
Erdi, P. (2020). Ranking: The unwritten rules of the social game we all play. Oxford University Press. google scholar
Erdi, P. (2020). Ranking: The unwritten rules of the social game we all play. Oxford University Press. google scholar
European Commission (2022). Mitigating diversity biases ofAI in the labor market. Horizon Europe Project Report. google scholar
European Commission (2022). Mitigating diversity biases ofAI in the labor market. Horizon Europe Project Report. google scholar
European Network Against Racism (2020). Artificial intelligence in HR: How to address racial biases and algorithmic discrimination in HR?. ENAR Publishing. google scholar
European Network Against Racism (2020). Artificial intelligence in HR: How to address racial biases and algorithmic discrimination in HR?. ENAR Publishing. google scholar
European Parliament (2020). Skills and jobs for future labour markets: European policies and Skills Agendas 2010-2020. EMPL in Focus. google scholar
European Parliament (2020). Skills and jobs for future labour markets: European policies and Skills Agendas 2010-2020. EMPL in Focus. google scholar
Franca, T. J. F., Mamede, H. S., Barroso, J. M. P., & dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organizational context. Heliyon, 9(4), e14694. google scholar
Franca, T. J. F., Mamede, H. S., Barroso, J. M. P., & dos Santos, V. M. P. D. (2023). Artificial intelligence applied to potential assessment and talent identification in an organizational context. Heliyon, 9(4), e14694. google scholar
Hale, K. E. (2020). Using artificial intelligence to circumvent the teacher shortage in special education: A phenomenological investigation. Doctoral Dissertation, Liberty University, USA. google scholar
Hale, K. E. (2020). Using artificial intelligence to circumvent the teacher shortage in special education: A phenomenological investigation. Doctoral Dissertation, Liberty University, USA. google scholar
Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(19), 324-327. google scholar
Harari, Y. N. (2017). Reboot for the AI revolution. Nature, 550(19), 324-327. google scholar
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. In Data ethics: Building trust: How digital technologies can serve humanity (pp. 621-653). Globethics Publications. google scholar
Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. In Data ethics: Building trust: How digital technologies can serve humanity (pp. 621-653). Globethics Publications. google scholar
IFOW (2020). Artificial intelligence in hiring: Assessing impacts on equality. Institute for the Future of Work. Retrieved from https://assets-global.website-files.com/64d5f73a7fc5e8a240310c4d/64d5f73b7fc5e8a240310ea0_5f71d338891671faa84de443_IFOW%2B-%2BAssessing%2Bimpacts%2Bon%2Bequality.pdf google scholar
IFOW (2020). Artificial intelligence in hiring: Assessing impacts on equality. Institute for the Future of Work. Retrieved from https://assets-global.website-files.com/64d5f73a7fc5e8a240310c4d/64d5f73b7fc5e8a240310ea0_5f71d338891671faa84de443_IFOW%2B-%2BAssessing%2Bimpacts%2Bon%2Bequality.pdf google scholar
ILO (2023). Artificial intelligence in human resource management: A challenge for the human-centred agenda?. ILO Working Paper 95. google scholar
ILO (2023). Artificial intelligence in human resource management: A challenge for the human-centred agenda?. ILO Working Paper 95. google scholar
Kamulegeya, L. H., Okello, M., Bwanika, J. M., et al. (2019). Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. African Health Sciences, 23(2), 753-763. google scholar
Kamulegeya, L. H., Okello, M., Bwanika, J. M., et al. (2019). Using artificial intelligence on dermatology conditions in Uganda: A case for diversity in training data sets for machine learning. African Health Sciences, 23(2), 753-763. google scholar
King, M. (2022). Harmful biases in artificial intelligence. The Lancet Psychiatry, 9(11), E48. google scholar
King, M. (2022). Harmful biases in artificial intelligence. The Lancet Psychiatry, 9(11), E48. google scholar
Kizilcec, R.F., & Lee, H. (2022). Algorithmic fairness in education. In Holmes W., Porayska-Pomsta, K. (Eds), The ethics of artificial intelligence in education (pp. 174-202). Taylor & Francis. google scholar
Kizilcec, R.F., & Lee, H. (2022). Algorithmic fairness in education. In Holmes W., Porayska-Pomsta, K. (Eds), The ethics of artificial intelligence in education (pp. 174-202). Taylor & Francis. google scholar
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res, 13, 795-848. google scholar
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development. Bus Res, 13, 795-848. google scholar
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci USA, 117(23), 12592-12594. google scholar
Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H., & Ferrante, E. (2020). Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc Natl Acad Sci USA, 117(23), 12592-12594. google scholar
Li, W., Aste, T., Caccioli, F., & Livan, G. (2019). Early co-authorship with top scientists predicts success in academic careers, Nature Communications, 10, 51-70. google scholar
Li, W., Aste, T., Caccioli, F., & Livan, G. (2019). Early co-authorship with top scientists predicts success in academic careers, Nature Communications, 10, 51-70. google scholar
Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. google scholar
Lum, K., & Isaac, W. (2016). To predict and serve?. Significance, 13(5), 14-19. google scholar
Mahmud, H. A. K. M., Islam, N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390. google scholar
Mahmud, H. A. K. M., Islam, N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390. google scholar
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. google scholar
Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46-60. google scholar
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. google scholar
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1-35. google scholar
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53-63. google scholar
Merton, R. K. (1968). The Matthew effect in science. Science, 159, 53-63. google scholar
Mittermaier, M., Raza, M. M., Kvedar, J. C. (2023). Bias in AI-based models for medical applications: Challenges and mitigation strategies. NPJ Digital Medicine, 6, 113. google scholar
Mittermaier, M., Raza, M. M., Kvedar, J. C. (2023). Bias in AI-based models for medical applications: Challenges and mitigation strategies. NPJ Digital Medicine, 6, 113. google scholar
Napierala, J., Kvetan, V. (2023). Changing job skills in a changing world. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of computational social science for policy (pp. 243-259). Springer, Cham. google scholar
Napierala, J., Kvetan, V. (2023). Changing job skills in a changing world. In: Bertoni, E., Fontana, M., Gabrielli, L., Signorelli, S., Vespe, M. (eds) Handbook of computational social science for policy (pp. 243-259). Springer, Cham. google scholar
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., et al. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278. google scholar
Nazer, L. H., Zatarah, R., Waldrip, S., Ke, J. X. C., Moukheiber, M., Khanna, A. K., et al. (2023). Bias in artificial intelligence algorithms and recommendations for mitigation. PLOS Digital Health, 2(6), e0000278. google scholar
Ntoutsi, E., Fafalios, P., Gadiraju, U., et al. (2020). Bias in data-driven artificial intelligence systems-an introductory survey. WIREs Data Mining Knowl Discov, 10(3), e1356. google scholar
Ntoutsi, E., Fafalios, P., Gadiraju, U., et al. (2020). Bias in data-driven artificial intelligence systems-an introductory survey. WIREs Data Mining Knowl Discov, 10(3), e1356. google scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447-453. google scholar
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366, 447-453. google scholar
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. google scholar
Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for educational data mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501. google scholar
OECD (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. google scholar
OECD (2023). OECD employment outlook 2023: Artificial intelligence and the labour market. OECD Publishing. google scholar
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books. google scholar
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books. google scholar
Oracle (2019). AI in human resources: The time is now. Retrieved from https://www.oracle.com/a/ocom/docs/applications/hcm/oracle-ai-in-hr-wp.pdf google scholar
Oracle (2019). AI in human resources: The time is now. Retrieved from https://www.oracle.com/a/ocom/docs/applications/hcm/oracle-ai-in-hr-wp.pdf google scholar
Özer, M., & Perc, M. (2022). Improving equality in the education system of Türkiye. İstanbul University Journal of Sociology, 2022, 42(2), 325-334. google scholar
Özer, M., & Perc, M. (2022). Improving equality in the education system of Türkiye. İstanbul University Journal of Sociology, 2022, 42(2), 325-334. google scholar
Özer, M. (2023). The Matthew effect Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. google scholar
Özer, M. (2023). The Matthew effect Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. google scholar
Perc, M. (2014). The Matthew effect in empirical data. Journal of Royal Society Interface, 11(98), 20140378. google scholar
Perc, M. (2014). The Matthew effect in empirical data. Journal of Royal Society Interface, 11(98), 20140378. google scholar
Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. google scholar
Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. google scholar
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. google scholar
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28, 31-38. google scholar
Renz, A., & Hilbig, R. (Eds.). (2023). Digital transformation of educational institutions accelerated by covid-19: A digital dynamic capabilities approach. Emerald Publishing. google scholar
Renz, A., & Hilbig, R. (Eds.). (2023). Digital transformation of educational institutions accelerated by covid-19: A digital dynamic capabilities approach. Emerald Publishing. google scholar
Rigney, D. (2010). The Matthew effect: How advantage begets further advantage. Columbia University Press. New York. google scholar
Rigney, D. (2010). The Matthew effect: How advantage begets further advantage. Columbia University Press. New York. google scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance. Psychological Bulletin, 135(1), 1-22. google scholar
Sackett, P. R., Kuncel, N. R., Arneson, J. J., Cooper, S. R., & Waters, S. D. (2009). Does socioeconomic status explain the relationship between admissions tests and post-secondary academic performance. Psychological Bulletin, 135(1), 1-22. google scholar
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Med, 27, 2176-2182. google scholar
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021). Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Med, 27, 2176-2182. google scholar
Smith, H. (2020). Algorithmic bias: should students pay the price? AI & Society, 35, 1077-1078. google scholar
Smith, H. (2020). Algorithmic bias: should students pay the price? AI & Society, 35, 1077-1078. google scholar
Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute. google scholar
Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute. google scholar
Srinivas, N. (2023, February 24). The ethical debate of AI in criminal justice: Balancing efficiency and human rights. Manage Engine Insights. google scholar
Srinivas, N. (2023, February 24). The ethical debate of AI in criminal justice: Balancing efficiency and human rights. Manage Engine Insights. google scholar
Retrieved from https://insights.manageengine.com/artificial-intelligence/the-ethical-debate-of-ai-in-criminal-justice-balancing-efficiency-and-human-rights// google scholar
Retrieved from https://insights.manageengine.com/artificial-intelligence/the-ethical-debate-of-ai-in-criminal-justice-balancing-efficiency-and-human-rights// google scholar
Stefan, V. (2019). Artificial intelligence and its impact on young people. Seminar Report from the Council of Europe. google scholar
Stefan, V. (2019). Artificial intelligence and its impact on young people. Seminar Report from the Council of Europe. google scholar
Storm, K. I. L., Reiss, L. K., Guenther, E. A., Clar-Novak, M., & Muhr, S.L. (2023). Unconscious bias in the HRM literature: Towards a critical-reflexive approach. Human Resource Management Review, 33(3), 100969. google scholar
Storm, K. I. L., Reiss, L. K., Guenther, E. A., Clar-Novak, M., & Muhr, S.L. (2023). Unconscious bias in the HRM literature: Towards a critical-reflexive approach. Human Resource Management Review, 33(3), 100969. google scholar
Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown: New York. google scholar
Suleyman, M. (2023). The coming wave: Technology, power, and the twenty-first century’s greatest dilemma. Crown: New York. google scholar
Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economics culture and Society, 61, 41-64. google scholar
Suna, H. E., Tanberkan, H., Gür, B. S., Perc, M., & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economics culture and Society, 61, 41-64. google scholar
Tuffaha, M. (2023). The impact of artificial intelligence bias on human resource management functions: Systematic literature review and future research directions. European Journal of Business and Innovation Research, 11(4), 35-58. google scholar
Tuffaha, M. (2023). The impact of artificial intelligence bias on human resource management functions: Systematic literature review and future research directions. European Journal of Business and Innovation Research, 11(4), 35-58. google scholar
Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40, 665-687. google scholar
Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40, 665-687. google scholar
UNESCO (2023). The teachers we need for the education we want: The global imperative to reverse the teacher shortage. UNESCO & Education 2030 Factsheet. google scholar
UNESCO (2023). The teachers we need for the education we want: The global imperative to reverse the teacher shortage. UNESCO & Education 2030 Factsheet. google scholar
Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems-A systematic literature review. International Journal of Information Management Data Insights, 3, 100165. google scholar
Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems-A systematic literature review. International Journal of Information Management Data Insights, 3, 100165. google scholar
Vicente, L., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13, 15737. google scholar
Vicente, L., & Matute, H. (2023). Humans inherit artificial intelligence biases. Scientific Reports, 13, 15737. google scholar
Zhang, J., Zhang, Z. M. (2023). Ethics and governance of trustwothy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. google scholar
Zhang, J., Zhang, Z. M. (2023). Ethics and governance of trustwothy medical artificial intelligence. BMC Medical Informatics and Decision Making, 23, 7. google scholar
Zuckerman, H. A. (1977). Scientific elite: Nobel laureates in the United States. New York: Free Press. google scholar
Zuckerman, H. A. (1977). Scientific elite: Nobel laureates in the United States. New York: Free Press. google scholar
Zuckerman, H. (1989). Accumulation of advantage and disadvantage: The theory and its intellectual biography. In Mongardini, C., Tabboni, S. (Eds), Robert K. Merton and contemporary sociology (pp. 153-176). New Brunswick, NJ: Transaction. google scholar
Zuckerman, H. (1989). Accumulation of advantage and disadvantage: The theory and its intellectual biography. In Mongardini, C., Tabboni, S. (Eds), Robert K. Merton and contemporary sociology (pp. 153-176). New Brunswick, NJ: Transaction. google scholar

Toplam 155 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Sosyoloji (Diğer)
Bölüm	Derleme Makaleleri
Yazarlar	Mahmut Özer 0000-0001-8722-8670 Matjaz Perc 0000-0002-3087-541X H. Eren Suna 0000-0002-6874-7472
Yayımlanma Tarihi	13 Haziran 2024
Gönderilme Tarihi	5 Ocak 2024
Kabul Tarihi	8 Şubat 2024
Yayımlandığı Sayı	Yıl 2024 Sayı: 69

Kaynak Göster

APA	Özer, M., Perc, M., & Suna, H. E. (2024). Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society(69), 159-168. https://doi.org/10.26650/JECS2023-1415085
AMA	Özer M, Perc M, Suna HE. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society. Haziran 2024;(69):159-168. doi:10.26650/JECS2023-1415085
Chicago	Özer, Mahmut, Matjaz Perc, ve H. Eren Suna. “Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market”. Journal of Economy Culture and Society, sy. 69 (Haziran 2024): 159-68. https://doi.org/10.26650/JECS2023-1415085.
EndNote	Özer M, Perc M, Suna HE (01 Haziran 2024) Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society 69 159–168.
IEEE	M. Özer, M. Perc, ve H. E. Suna, “Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market”, Journal of Economy Culture and Society, sy. 69, ss. 159–168, Haziran 2024, doi: 10.26650/JECS2023-1415085.
ISNAD	Özer, Mahmut vd. “Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market”. Journal of Economy Culture and Society 69 (Haziran 2024), 159-168. https://doi.org/10.26650/JECS2023-1415085.
JAMA	Özer M, Perc M, Suna HE. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society. 2024;:159–168.
MLA	Özer, Mahmut vd. “Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market”. Journal of Economy Culture and Society, sy. 69, 2024, ss. 159-68, doi:10.26650/JECS2023-1415085.
Vancouver	Özer M, Perc M, Suna HE. Artificial Intelligence Bias and the Amplification of Inequalities in the Labor Market. Journal of Economy Culture and Society. 2024(69):159-68.

Kapak Resmi İndir

Makale Dosyaları

Tam Metin