Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-22T01:31:59.616Z Has data issue: false hasContentIssue false
  • English
  • Français

Artificial Intelligence and Race: a Systematic Review

Published online by Cambridge University Press:  16 September 2020

Channarong Intahchomphoo  and
Odd Erik Gundersen
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper examines peer-reviewed publications to learn about the relationships between artificial intelligence (AI) and the human race. For this systematic review, papers were collected from three academic databases: Scopus, Web of Science, and Academic Search Complete. From 1,222 papers reviewed, 36 papers were included. The findings indicate that there are four relationships between AI and race (i). AI causes unequal opportunities for people from certain racial groups, (ii). AI helps to detect racial discrimination, (iii). AI is applied to study health conditions of specific racial population groups, and (iv). AI is used to study demographics and facial images of people from different racial backgrounds. To widen the knowledge related to AI and race, all four finding categories in this review included supplementary studies as lessons learned for legal information management research. The authors, Channarong Intahchomphoo and Odd Erik Gundersen, use these findings to discuss how AI could impact libraries and how legal information management professionals might have to cope with the problem of biased AI.

Keywords

artificial intelligenceAIracereview
Type
Current Interests
Information
Legal Information Management , Volume 20 , Issue 2 , June 2020 , pp. 74 - 84
Copyright
Copyright © The Author(s) 2020. Published by British and Irish Association of Law Librarians

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Included Studies Responding to the Review Question

Buist, Henry., Linneman, Peter D., & Megbolugbe, Isaac F. (1999) Residential-mortgage lending discrimination and lender-risk-compensating policies. Real Estate Economics (27:4), 695–717.CrossRefGoogle Scholar
Caliskan, Aylin, Bryson, Joanna J., & Narayanan, Arvind. (2017) Semantics derived automatically from language corpora contain human-like biases. Science (356:6334), 183–186.CrossRefGoogle ScholarPubMed
Cundell, Diana R., Silibovsky, Randy S., Sanders, Robyn, & Sztandera, L.M. (2001) Generation of an intelligent medical system, using a real database, to diagnose bacterial infection in hospitalized patients. International Journal of Medical Informatics (63:1–2), 31–40.CrossRefGoogle Scholar
Dalenberg, David Jacobus. (2018) Preventing discrimination in the automated targeting of job Advertisements. Computer Law & Security Review (34:3), 615–627.CrossRefGoogle Scholar
De Choudhury, Munmun, Jhaver, Shagun, Sugar, Benjamin, & Weber, Ingmar. (2016) “Social Media Participation in an Activist Movement for Racial Equality” in Proceedings of the Tenth International AAAI Conference on Web and Social Media, 2016. 92–101.Google Scholar
Ferrando, Francesca. (2014) Is the post-human a post-woman? Cyborgs, robots, artificial intelligence and the futures of gender: A case study. European Journal of Futures Research 2(43).Google Scholar
Fries, Jason A., Polgreen, Philip M., & Segre, Alberto M. (2014) “Mining the demographics of Craigslist casual sex ads to inform public health policy” in Proceedings of the 2014 IEEE International Conference on Healthcare Informatics, 2014. 61–70.Google Scholar
Gent, Edd. (2015) AI: Fears of ‘playing God’. Engineering & Technology (10:2), 76–79.CrossRefGoogle Scholar
Georgiou, Theodore, El Abbadi, Amr, & Yan, Xifeng. (2017) “Privacy cyborg: Towards protecting the privacy of social media users” in Proceedings of the 2017 IEEE 33rd International Conference on Data Engineering, 2017. 1395–1396.Google Scholar
Greenwald, Anthony G. (2017) An AI stereotype catcher. Science (356:6334), 133–134.CrossRefGoogle ScholarPubMed
Hall, RG II, Pasipanodya, JG, Swancutt, MA, Meek, C., Leff, R., & Gumbo, T. (2017) Supervised machine-learning reveals that old and obese people achieve low dapsone concentrations. CPT: Pharmacometrics & Systems Pharmacology (6:8), 552–559.Google Scholar
Hammarlund, Noah, & Natarajan, Sriraam. (2017) “Does race play a role in invasive procedure treatments? An initial analysis” in Proceedings of the 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies. 2017. 243–244.Google Scholar
Hyun, Changhum, & Park, Hyeyoung. (2017) “Recognition of facial attributes using multi-task learning of deep networks” in Proceedings of the 9th International Conference on Machine Learning and Computing. 2017. 284–288.Google Scholar
Jung, Soon-Gyo, An, Jisun, Kwak, Haewoon, Salminen, Joni, & Jansen, Bernard Jim. (2018) “Assessing the accuracy of four popular face recognition tools for inferring gender, age, and race” in Proceeding of the Twelfth International AAAI Conference on Web and Social Media, 2018. 624–627.Google Scholar
Kogeda, Okuthe P., & Dladlu, Nosipho. (2012) “A decision support system for cancer prevalence in South Africa” in Proceedings of the 2012 8th International Conference on Computing Technology and Information Management, 2012. 532–537.Google Scholar
Kusner, Matt J., Loftus, Joshua, Russell, Chris, & Silva, Ricardo. (2017) “Counterfactual fairness,” in Proceedings of the 31st Conference on Neural Information Processing Systems, 2017. 4066–4076.Google Scholar
López-Martínez, Fernando, Schwarcz, Aron, Núñez-Valdez, Edward Rolando, & García-Díaz, Vicente. (2018) Machine learning classification analysis for a hypertensive population as a function of several risk factors. Expert Systems with Applications (110:15), 206–215.CrossRefGoogle Scholar
Mayo, Margarita, van Knippenberg, Daan, Guillén, Laura, & Firfiray, Shainaz. (2016) Team diversity and categorization salience: Capturing diversity-blind, intergroup-biased, and multicultural perceptions. Organizational Research Methods (19:3), 433–474.CrossRefGoogle Scholar
Montasser, Omar, & Kifer, Daniel. (2017) “Predicting demographics of high-resolution geographies with geotagged tweets” in Proceeding of the Thirty-First AAAI Conference on Artificial Intelligence, 2017. 1460–1466.Google Scholar
Morrison, M. Irene. (2018) Info-topia: Postcolonial cyberspace and artificial intelligence in TRON: Legacy and Nalo Hopkinson’s Midnight Robber. Journal of Postcolonial Writing (54:2), 161–173.CrossRefGoogle Scholar
Olteanu, Alexandra, Weber, Ingmar, & Gatica-Perez, Daniel. (2016) “Characterizing the demographics behind the# Blacklivesmatter movement” in Proceeding of the 2016 AAAI Spring Symposium Series, 2016. 310–313.Google Scholar
Otterbacher, Jahna. (2015) “Linguistic bias in collaboratively produced biographies: crowdsourcing social stereotypes?” in Proceeding of the Ninth international AAAI Conference on Web and Social Media, 2015. 298–307.Google Scholar
Quadrianto, Novi, & Sharmanska, Viktoriia. (2017) “Recycling privileged learning and distribution matching for fairness” in Proceedings of the 31st Conference on Neural Information Processing Systems, 2017. 677–688.Google Scholar
Ragab, Mohammad E., Darwish, Ahmed M., Abed, Ehsan M., & Shaheen, Samir I. (1999) “Face recognition using principal component analysis applied to an Egyptian face database,” in Proceeding of the International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, 1999. 540–549.Google Scholar
Schlesinger, Ari, O'Hara, Kenton P., and Taylor, Alex S. (2018) “Let's talk about race: Identity, chatbots, and AI” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018.Google Scholar
Shapiro, Danny. (2016) “Accelerating the race to autonomous cars” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 415–415.Google Scholar
Singh, Tribhuwan, Jain, Yashvardhan, & Kumar, Vaibhav. (2017) “Predicting parole hearing result using machine learning” in Proceeding of the 2017 International Conference on Emerging Trends in Computing and Communication Technologies, 2017. 1–3.Google Scholar
Singh, Vivek Kumar, Hegde, Saket, & Atrey, Akanksha. (2017) “Towards measuring fine-grained diversity using social media photographs” in Proceeding of the Eleventh International AAAI Conference on Web and Social Media, 2017. 668–671.Google Scholar
Thakkar, Nishith Mukeshkumar, Mookiah, Lenin, Talbert, Doug, & Eberle, William. (2017) “Anomalies in students enrollment using visualization” in Proceeding of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 2017. 544–547.Google Scholar
Ueki, Kazuya, Sugiyama, Masashi, Ihara, Yasuyuki, & Fujita, Mitsuhiro. (2011) “Multi-race age estimation based on the combination of multiple classifiers” in Proceeding of the First Asian Conference on Pattern Recognition, 2011. 633–637.Google Scholar
Watanabe, Hajime, Bouazizi, Mondher, & Ohtsuki, Tomoaki. (2018) Hate speech on Twitter: A pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access (6), 13825–13835.CrossRefGoogle Scholar
Wang, Yu, Li, Yuncheng, and Luo, Jiebo. (2016) “Deciphering the 2016 US presidential campaign in the Twitter sphere: A comparison of the Trumpists and Clintonists” in Proceeding of the Tenth International AAAI Conference on Web and Social Media, 2016. 723–726.Google Scholar
Wiechmann, Paul, Lora, Karina, Branscum, Paul, and Fu, Jicheng. (2017) “Identifying discriminative attributes to gain insights regarding child obesity in Hispanic preschoolers using machine learning techniques” in Proceeding of the 2017 IEEE 29th International Conference on Tools with Artificial Intelligence, 2017. 11–15.Google Scholar
Yang, Diyi, & Counts, Scott. (2018) “Understanding self-narration of personally experienced racism on Reddit,” in Proceeding of the Twelfth International AAAI Conference on Web and Social Media, 2018. 704–707.Google Scholar
Yu, Wei, Liu, Tiebin, Valdez, Rodolfo, Gwinn, Marta, & Khoury, Muin J. (2010) Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes. BMC Medical Informatics and Decision Making, (10:16).CrossRefGoogle ScholarPubMed
Yun, Fu, Nanning, Zheng, Jianyi, Liu, & Ting, Zhang. (2004) “Facetransfer: A system model of facial image rendering” in Proceeding of the 2004 IEEE International Conference on Systems, Man and Cybernetics, 2004. 2180–2185.Google Scholar