Assessing and Addressing Algorithmic Bias - But Before We Get There
Jean Garcia-Gathright, Aaron Springer, Henriette Cramer
备注：
The version submitted here for FATREC 2018 is a condensed version of our publication which originally appeared in the 2018 AAAI Spring Symposium Series under the "Design of the User Experience for Artificial Intelligence" track. The original publication can be found here: https://www.aaai.org/ocs/index.php/SSS/SSS18/paper/view/17542/15470
Algorithmic and data bias are gaining attention as a pressing issue in popular press - and rightly so. However, beyond these calls to action, standard processes and tools for practitioners do not readily exist to assess and address unfair algorithmic and data biases. The literature is relatively scattered and the needed interdisciplinary approach means that very different communities are working on the topic. We here provide a number of challenges encountered in assessing and addressing algorithmic and data bias in practice. We describe an early approach that attempts to translate the literature into processes for (production) teams wanting to assess both intended data and algorithm characteristics and unintended, unfair biases.
Evaluating Algorithmic Bias in Models for Predicting Academic Performance of Filipino Students
University of Pennsylvania（宾夕法尼亚大学）；Kyushu University（九州大学）；Sorbonne University（索邦大学）；Ateneo de Manila University（雅典耀大学）；LIP6（巴黎第六大学信息学实验室）；ait.kyushu-u.ac.jp（九州大学先进信息技术研究所）
Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness
Google Research, Mountain View, CA, USA（谷歌研究, 山景城, 加州, 美国）；Google DeepMind, Mountain View, CA, USA（谷歌深Mind, 山景城, 加州, 美国）；New York University, New York, NY, USA（纽约大学, 纽约, 纽约, 美国）；Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院, 柏林, 马萨诸塞, 美国）；The Hopsital for Sick Children, Toronto, ON, Canada（儿童医院, 温哥华, 加拿大）；Stanford University, Stanford, CA, USA（斯坦福大学, 斯坦福, 加州, 美国）
