Ethical Tensions in Applications of AI for Addressing Human Trafficking: A Human Rights Perspective

被引:6
作者
Deeb-Swihart J. [1 ]
Endert A. [1 ]
Bruckman A. [1 ]
机构
[1] Georgia Institute of Technology, Atlanta, 30332, GA
关键词
artificial intelligence; ethics; human rights; human trafficking;
D O I
10.1145/3555186
中图分类号
学科分类号
摘要
In the last two decades, human trafficking (where individuals are forcibly exploited for the profits of another) has seen increased attention from the artificial intelligence (AI) community. Clear focus on the ethical risks of this research is critical given that those risks are disproportionately born by already vulnerable populations. To understand and subsequently address these risks, we conducted a systematic literature review of computing research leveraging AI to combat human trafficking and apply a framework using principles from international human rights law to categorize ethical risks. This paper uncovers a number of ethical tensions including bias endemic in datasets, privacy risks stemming from data collection and reporting, and issues concerning potential misuse. We conclude by highlighting four suggestions for future research: broader use of participatory design; engaging with other forms of trafficking; developing best practices for harm prevention; and including transparent ethics disclosures in research. We find that there are significant gaps in what aspects of human trafficking researchers have focused on. Most research to date focuses on aiding criminal investigations in cases of sex trafficking, but more work is needed to support other anti-Trafficking activities like supporting survivors, adequately address labor trafficking, and support more diverse survivor populations including transgender and nonbinary individuals. © 2022 ACM.
引用
收藏
相关论文
共 171 条
[21]  
Brayne S., Predict and Surveil: Data, Discretion, and the Future of Policing, (2020)
[22]  
Brelsford J., Parakh S., A systems modeling approach to analyzing human trafficking, 2018 Winter Simulation Conference WSC, pp. 12-21, (2018)
[23]  
Broberg M., Sano H., Strengths and weaknesses in a human rights-based approach to international development-An analysis of a rights-based approach to development assistance based on practical experiences, The International Journal of Human Rights, 22, 5, pp. 664-680, (2018)
[24]  
Bruns A., Weller K., Zimmer M., John Proferes N., A topology of Twitter research: Disciplines, methods, and ethics Aslib, Journal of Information Management, (2014)
[25]  
Burbano D., Hernandez-Alvarez M., Illicit, hidden advertisements on Twitter, 2018 International Conference on EDemocracy & EGovernment (ICEDEG, pp. 317-321, (2018)
[26]  
Carback J., Cybersex Trafficking: Toward a More Effective Prosecutorial Response, Criminal Law Bulletin, 54, 1, (2018)
[27]  
Chancellor S., Birnbaum M.L., Caine E.D., Silenzio V.M.B., De Choudhury M., A taxonomy of ethical tensions in inferring mental health states from social media, Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 79-88, (2019)
[28]  
Kumar Chandaliya P., Nain N., Conditional Perceptual Adversarial Variational Autoencoder for Age Progression and Regression on Child Face, 2019 International Conference on Biometrics (ICB, pp. 1-8, (2019)
[29]  
Chmielinski K.S., Newman S., Taylor M., Joseph J., Thomas K., Yurkofsky J., Qiu Y.C., The Dataset Nutrition Label (2nd Gen): Leveraging Context to Mitigate Harms in Artificial Intelligence, Proceedings of the NeurIPS 2020 Workshop on Dataset Curation and Security, Online, 11, (2020)
[30]  
Chokshi N., Facial Recognition?s Many Controversies, from Stadium Surveillance to Racist Software, New York Times, 15, 2019, (2019)