讲座主题：Treat me fairly: Implementing unbiased and interpretable algorithmic decision making;

　　主讲人：丁文萱

　　讲座地点：B247

　　讲座时间：2018年6月8 日(周 五)上午9：00-10：00

　　讲座摘要：

　　Today, driven by big data and rapid improvements in computing power, many firms and organizations are interested in deploying various machine learning algorithms to improve their decision-making. Although conventional machine learning models have shown a great performance in many areas, the European Union has recently issued a new law (the General Data Protection Regulation- GDPR) to prohibit the currently widely used conventional machine learning algorithms in applications including recommendation systems, collaboration filtering in social networks, credit and insurance risk assessments, computational advertising, intelligent agent, etc. This law posts important challenges to industries and information systems and artificial intelligence communities because conventional machine learning models suffer two flaws: the potential discrimination on data subjects and being unable to explain the logic involved in learning outcomes.

　　These two flaws are due to (1) aggregate-level learning from a data set consisting of different data subjects such that a sample selection bias may occur, resulting in biased decisions when a subject is not in the same population as those in the training sample; and (2) non-theory-driven processes focusing on correlations among feature variables rather than their causal effects.

　　This paper presents a novel theory-based individual dynamic model to overcome the discrimination issue and incorporate a causal mechanism to enable explanation simultaneously. The model only uses information from an individual subject without employing other subjects’ data. It learns the underlying data generating process and identifies the corresponding causation mechanism to achieve a fair and interpretable decision. Using a real-world credit and risk assessment as the context, we empirically test our model and show that the proposed model outperforms conventional supervised learning models and decision tree models in terms of fairness and prediction accuracy.

　　个人简介：

　　丁文萱老师现为美国西北大学医学院研究副教授。她师从诺贝尔经济学奖、图灵奖获得者赫伯特·西蒙院士 (Herbert A. Simon), 从美国卡内基·梅隆大学(Carnegie Mellon University)获得信息技术与认知科学博士学位。她的研究兴趣是可解释的人工智能，移动医疗保健，信息技术，用户动态建模和科学发现。发表学术专著两部，多篇研究论文发表在多家顶级国际学术刊物上，包括 Information Systems Research, Decision Support Systems, Defense & Security Analysis, Oxford Journal of Management Mathematics, Safety Science, Journal of Defense Modeling and Simulation, Springer Computational Intelligence Series, Proceedings of Association for Advancement of Artificial Intelligence。 她近期的一篇研究论文获得了2016年国际信息系统年会人机交互领域的最佳论文奖。她于2008-2014年担任 Journal of Defense Modeling and Simulation的副主编。丁教授曾在Indiana University, University of Illinois, Carnegie Mellon University, Cheung Kong Graduate School of Business, and National University of Singapore多家高校给本科,硕士和博士生授课。