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Computer Vision and Machine Learning for Human Rights Video Analysis: Case Studies, Possibilities, Concerns, and Limitations

Published online by Cambridge University Press:  27 December 2018

Jay D. Aronson
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Abstract

Citizen video and other publicly available footage can provide evidence of human rights violations and war crimes. The ubiquity of visual data, however, may overwhelm those faced with preserving and analyzing it. This article examines how machine learning and computer vision can be used to make sense of large volumes of video in advocacy and accountability contexts. These technologies can enhance the efficiency and effectiveness of human rights advocacy and accountability efforts, but only if human rights organizations can access the technologies themselves and learn how to use them to promote human rights. As such, computer scientists and software developers working with the human rights community must understand the context in which their products are used and act in solidarity with practitioners. By working together, practitioners and scientists can level the playing field between the human rights community and the entities that perpetrate, tolerate, or seek to cover up violations.

Type
Articles
Information
Law & Social Inquiry , Volume 43 , Issue 4 , Fall 2018 , pp. 1188 - 1209
Copyright
Copyright © American Bar Foundation, 2018 

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References

Amnesty International and Forensic Architecture. “‘Black Friday’: Carnage in Rafah, ‘Methodology’.” n.d. https://blackfriday.amnesty.org/methodology.php (accessed February 15, 2017).Google Scholar
Aronson, Jay D. “Preserving Human Rights Media for Justice, Accountability, and Historical Clarification.” Genocide Studies and Prevention 11, no. 1 (2017): 82–99.Google Scholar
Aronson, Jay D., Xu, Shicheng, and Hauptmann, Alex. “Video Analytics for Conflict Monitoring and Human Rights Documentation.” 2015. https://www.cmu.edu/chrs/documents/ELAMP-Technical-Report.pdf (accessed February 15, 2017).Google Scholar
Azouley, Ariella. The Civil Contract of Photography. Cambridge, MA: MIT Press, 2008.Google Scholar
Baylis, Elena. “Tribunal-Hopping with the Post-Conflict Justice Junkies.” Oregon Review of International Law 10 (2008): 361–90.Google Scholar
Cohen, Stanley. States of Denial: Knowing About Atrocities and Suffering. Cambridge: Polity Press, 2001.Google Scholar
Dubberley, Sam, Griffin, Elizabeth, and Bal, Haluk Mert. “Making Secondary Trauma a Primary Issue: A Study of Eyewitness Media and Vicarious Trauma on the Digital Frontline.” 2015. http://eyewitnessmediahub.com/research/vicarious-trauma (accessed February 15, 2017).Google Scholar
Feigenson, Neal, and Spiesel, Christina. Law on Display: The Digital Transformation of Legal Persuasion and Judgment New York: New York University Press, 2009.Google Scholar
Forensic Architecture. “Rafah: Black Friday.” n.d. http://www.forensic-architecture.org/case/rafah-black-friday/#toggle-id-4 (accessed February 15, 2017).Google Scholar
Forensic Architecture and SITU Research. “Report: Summary of Findings on the April 17, 2009 Death of Bassem Ibrahim Abu Rahma, Bil'in.” 2010. http://www.situstudio.com/blog/wp-content/uploads/2010/07/Abu_Rahma_report_web.pdf (accessed February 15, 2017).Google Scholar
Forensic Architecture and SITU Research. “The Use of White Phosphorus Munitions in Urban Environments” n.d. http://www.situresearch.com/white_phosphorus/index.html.Google Scholar
Garvie, Clare, and Frankle, Jonathan. “Facial-Recognition Software Might Have a Racial Bias Problem.” The Atlantic, April 6, 2016. http://www.theatlantic.com/technology/archive/2016/04/the-underlying-bias-of-facial-recognition-systems/476991/ (accessed February 15, 2017).Google Scholar
Geitgey, Adam. “Machine Learning Is Fun! The World's Easiest Introduction to Machine Learning.” 2014. https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.hxc2prhlu (accessed February 15, 2017).Google Scholar
Georgetown Law Center on Privacy and Technology. The Perpetual Line-Up: Unregulated Police Facial Recognition in America. Washington, DC: Georgetown University Press, 2016.Google Scholar
Juefei-Xu, Felix, Luu, Khoa, and Savvides, Marios. “Spartans: Single-Sample Periocular-Based Alignment-Robust Recognition Technique Applied to Non-Frontal Scenarios.” IEEE Transactions on Image Processing 24, no. 12 (2015): 4780–95.Google Scholar
Lafrance, Adrienne. “Idea: Syncing Multiple Videos from the Same Event on YouTube.” Atlantic, 2014. http://www.theatlantic.com/technology/archive/2014/04/hey-youtube-we-need-a-multi-video-syncing-function/361236/ (accessed February 15, 2017).Google Scholar
Landman, Todd, and Carvalho, Edzia. Measuring Human Rights. London: Routledge, 2009.Google Scholar
Liang, Junwei, Burger, Susanne, Hauptmann, Alex, and Aronson, Jay D. “Video Synchronization and Sound Search for Human Rights Documentation and Conflict Monitoring.” 2016. http://www.cmu.edu/chrs/documents/Video-Synchronization-Technical-Report.pdf (accessed February 15, 2017).Google Scholar
Mnookin, Jennifer L. “The Image of Truth: Photographic Evidence and the Power of Analogy.” Yale Journal of Law & the Humanities 10, no. 1 (1998): 1–74.Google Scholar
Morgan, Oliver W., Sribanditmongkol, Pongruk, Perera, Clifford, Sulasmi, Yeddi, vanAlphen, Dana, and Sondorp, Egbert. “Mass Fatality Management Following the South Asian Tsunami Disaster: Case Studies in Thailand, Indonesia, and Sri Lanka.” PLoS Medicine 3, no. 6 (2006): e195.Google Scholar
New Tactics in Human Rights. “Video as Evidence: To Be Evidence, What Does Video Need?” 2014. https://www.newtactics.org/comment/7427#comment-7427 (accessed February 15, 2017).Google Scholar
Orcutt, Mike. “Are Face Recognition Systems Accurate? Depends on Your Race.” MIT Technology Review, July 6, 2016. https://www.technologyreview.com/s/601786/are-face-recognition-systems-accurate-depends-on-your-race/ (accessed February 15, 2017).Google Scholar
Phillips, P. Jonathon, Jiang, Fang, Narvekar, Abhijit, Ayyad, Julianne, and O'Toole, Alice J. “An Other-Race Effect for Face Recognition Algorithms.” ACM Transactions on Applied Perception 8, no. 2 (2011): 1–11.Google Scholar
Piracés, Enrique. “The Future of Human Rights Technology: A Practitioner's View.” In New Technologies for Human Rights Law and Practice, edited by Land, Molly K. and Aronson, Jay D., 289–308. Cambridge: Cambridge University Press, 2018.Google Scholar
Porter, Elizabeth G. “Taking Images Seriously.” Columbia Law Review 114 (2014): 1687–1782.Google Scholar
Price, Megan, Gohdes, Anita, and Ball, Patrick. “Documents of War: Understanding the Syrian Conflict.” Significance April (2015): 14–19.Google Scholar
Ristovska, Sandra. “The Rise of Eyewitness Video and its Implications for Human Rights: Conceptual and Methodological Approaches.” Journal of Human Rights 15, no. 3 (2016): 347–60.Google Scholar
Sasseen, Jane. “The Video Revolution: A Report to the Center for International Media Assistance.” October 28, 2012. http://www.centroperiodismodigital.org/sitio/sites/default/files/Video%20revolution%20FINAL_0.pdf (accessed December 20, 2017).Google Scholar
Shapin, Steven. “Pump and Circumstance: Robert Boyle's Literary Technology.” Social Studies of Science 14 (1984): 481–520.Google Scholar
Sherwin, Richard K. Visualizing Law in the Age of the Digital Baroque: Arabesques and Entanglements. London: Routledge, 2011.Google Scholar
Szeliski, Richard. Computer Vision: Algorithms and Applications. New York: Springer, 2011.Google Scholar
Tong, Wei, Yang, Yi, Jiang, Lu, Yu, Shoou-I, Lan, ZhenZhong, Ma, Zhigang, Sze, Waito, Younessian, Ehsan, and Hauptmann, Alexander G. “E-LAMP: Integration of Innovative Ideas for Multimedia Event Detection.” Machine Vision and Applications 25, no. 1 (2014): 5–15.Google Scholar
Wardle, Claire, Dubberley, Sam, and Brown, Pete. Amateur Footage: A Global Study of User-Generated Content in TV and Online News Output. New York: Tow Center of the Columbia Journalism School, 2014.Google Scholar
Weizman, Eyal. Forensic Architecture: Violence at the Threshold of Detectability. New York: Zone Books, 2017.Google Scholar
Wexler, Rebecca. “Censorship Through Forensics: Video Evidence in Post-War Crises.” In Global Censorship: Shifting Modes, Persisting Paradigms, edited by Prakash, Pranesh, Rizk, Nagla, and Souza, Carlos Affonso, 85–108. New Haven, CT: Yale Law School Information Society Project, 2015a.Google Scholar
Wexler, Rebecca “Convicted by Code.” Future Tense, October 6, 2015b. http://www.slate.com/blogs/future_tense/2015/10/06/defendants_should_be_able_to_inspect_software_code_used_in_forensics.html (accessed February 15, 2017).Google Scholar