Henriette Cramer
ABSTRACT
Potential negative outcomes of machine learning and algorithmic bias have gained deserved attention. However, there are still relatively few standard processes to assess and address algorithmic biases in industry practice. Practical tools that integrate into engineers' workflows are needed. As a case study, we present two tooling efforts to create tools for teams in practice to address algorithmic bias. Both intend to increase understanding of data, models, and outcome measurement decisions. We describe the development of 1) a prototype checklist based on existing literature frameworks; and 2) dashboarding for quantitatively assessing outcomes at scale. We share both technical and organizational lessons learned on checklist perceptions, data challenges and interpretation pitfalls.
- Ricardo Baeza-Yates. 2016. Data and algorithmic bias in the web. In Proceedings of ACM Web Science. ACM, 1--1. Google Scholar
Digital Library
- Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. 77--91.Google Scholar
- Kate Crawford. 2017. The trouble with bias. In Conference on Neural Information Processing Systems, invited speaker.Google Scholar
- Michael D Ekstrand, Mucun Tian, Mohammed R Imran Kazi, Hoda Mehrpouyan, and Daniel Kluver. 2018. Exploring author gender in book rating and recommendation. In Proceedings of RecSys'18. ACM, 242--250. Google ScholarDigital Library
- Avriel C Epps and Travis L Dixon. 2017. A Comparative Content Analysis of Anti-and Prosocial Rap Lyrical Themes Found on Traditional and New Media Outlets. Journal of Broadcasting & Electronic Media 61, 2 (2017), 467--498.Google ScholarCross Ref
- Batya Friedman and Helen Nissenbaum. 1996. Bias in computer systems. ACM TOIS 14, 3 (1996), 330--347. Google ScholarDigital Library
- Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Emre Kiciman. 2016. Social data: Biases, methodological pitfalls, and ethical boundaries. (2016).Google Scholar
- D Sculley, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young. 2014. Machine learning: The high-interest credit card of technical debt. (2014).Google Scholar
- Aaron Springer and Henriette Cramer. 2018. Play PRBLMS: Identifying and Correcting Less Accessible Content in Voice Interfaces. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 296. Google ScholarDigital Library
- Elizabeth H Stokoe. 2004. Gender and discourse, gender and categorization: Current developments in language and gender research. Qualitative Research in Psychology 1, 2 (2004), 107--129.Google ScholarCross Ref
- Allison Woodruff, Sarah E Fox, Steven Rousso-Schindler, and Jeffrey Warshaw. 2018. A Qualitative Exploration of Perceptions of Algorithmic Fairness. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 656. Google ScholarDigital Library
Index Terms
- Translation, Tracks & Data: an Algorithmic Bias Effort in Practice
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