Fairness, bias, and social/economical impact of AI/ML algorithms

“With great power comes great responsibility”, as AI/ML especially deep learning continues to advance in research and expand to commercial applications, AI/ML algorithms are making big social economical impact to people’s lives, from deciding what health insurance policy a person can get, to whether a bank decides to issue a loan to a borrower, or what content a person can see on a web site. With even a slight bias, the algorithms can amplify unfairness or even injustice. So how do we unleash the power of AI/ML to improve people’s lives with fairness and justice, while not tying the hands of the algorithm developers?  In this talk, I will talk about how bias creeps into your ML models, both consciously and unconsciously, both from data and from the code, how to address them with novel debias techniques and blackbox model interpretation components, and how to design fairness principles into the architecture of your ML platform, all by using real world examples, cutting edge research results, and practical techniques in algorithm and architecture design. At the end of the talk, you should have a higher level of awareness of bias in AI/ML algorithms, recognize the value of fairness instead of viewing it as an inconvenience, have a mindset of how to address them in your design of ML platform and solutions.

演讲提纲

1. Overview of fairness and bias in AI/ML
   • Unconscious bias in data 
   • Unconscious bias in algorithms
   • Well-known trust busters
   • Current state of the art: research and industry
2. Case study: B2B AI/ML solutions for digital experience optimization
   • Identification of protected groups
   • Generic measurement of fairness
   • Innovation to correct bias while minimizing accuracy loss
   • Innovation to interpret black-box model results
3. Opportunities and challenges in generalizing fairness practices in AI/ML platforms

参考译文：

俗话说“权力越大责任越大”。随着 AI / ML（尤其是深度学习）在研究中不断发展并扩展到商业应用，AI / ML 算法对人们的生活日渐产生巨大的社会经济影响，例如保险公司卖给消费者什么样的健康保险，银行是否决定向借款人发放贷款，甚至在网站决定访问者看到什么内容都是由算法决定。即使有轻微的偏差，这些算法都会加剧社会的不公平甚至不公正。那么，如何在不束缚算法开发人员创新的前提下，发挥 AI / ML 的力量，以公平和正义的方式来改善人们的生活呢？

在本次演讲中，我将讨论数据和算法如何有意识或无意识地将偏差渗入到ML模型中，如何使用新颖的 Debias 技术和黑盒式模型解释组件来解决这个问题，以及如何将公平原则融入到ML平台的架构设计中。所有这些都通过真实案例，前沿研究结果以及算法和架构设计中的实用技术来讲述，以帮助大家对 AI / ML 算法中的偏差有更高的认识，认识到公平的价值，而不是将其视为负担，同时也了解如何在 ML 平台设计中处理这些问题。

演讲提纲

1. AI / ML 中的公平性和偏差概述
   • 数据中的无意识偏差
   • 算法中的无意识偏差
   • 著名的信任破坏案例
   • 最新的研究和工业技术案例
2. 案例研究：用于数字体验优化的 B2B AI / ML 解决方案
   • 确认受保护群体
   • 公平性的衡量方法
   • 最小精度损失的去偏差创新方法
   • 黑箱式模型解释创新方法
3. 在 AI / ML 平台中推广公平实践的机会和挑战