1. Black box fairness testing of machine learning models
2. Shibbir Ahmed , Sayem Mohammad Imtiaz , Samantha Syeda Khairunnesa , Breno Dantas Cruz , and Hridesh Rajan . 2023 . Design by Contract for Deep Learning APIs. In ESEC/FSE’2023 : The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. Shibbir Ahmed, Sayem Mohammad Imtiaz, Samantha Syeda Khairunnesa, Breno Dantas Cruz, and Hridesh Rajan. 2023. Design by Contract for Deep Learning APIs. In ESEC/FSE’2023: The 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.
3. Julia Angwin , Jeff Larson , Surya Mattu , and Lauren Kirchner . 2016. Machine bias risk assessments in criminal sentencing. ProPublica , May, 23 ( 2016 ). Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias risk assessments in criminal sentencing. ProPublica, May, 23 (2016).
4. Rachel KE Bellamy Kuntal Dey Michael Hind Samuel C Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta and Aleksandra Mojsilovic. 2018. AI Fairness 360: An extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943. Rachel KE Bellamy Kuntal Dey Michael Hind Samuel C Hoffman Stephanie Houde Kalapriya Kannan Pranay Lohia Jacquelyn Martino Sameep Mehta and Aleksandra Mojsilovic. 2018. AI Fairness 360: An extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias. arXiv preprint arXiv:1810.01943.
5. Reuben Binns . 2018 . Fairness in machine learning: Lessons from political philosophy . In Conference on Fairness, Accountability and Transparency. 149–159 . Reuben Binns. 2018. Fairness in machine learning: Lessons from political philosophy. In Conference on Fairness, Accountability and Transparency. 149–159.