Algorithmic Fairness in Black-box Machine Learning Models

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Deep learning models are successful machine learning models that have seen incredible growth in the last few years with impressive results in many applications. Deep learning models are being used with increasing frequency for decision making in domains that affect peoples' lives such as employment, education, policing and loan approval. These uses raise concerns about biases of algorithmic discrimination and have motivated the development of fairness-aware machine learning. The initial efforts in this fast-growing field were focusing on formalizing statistical measures of fairness that could be used to train new models. While initial efforts were important first steps towards addressing fairness concerns in machine learning, there were immediate challenges when applying them to deep learning models. The main success of deep learning models comes from a combination of efficient learning algorithms and their huge parametric space with hundreds of layers and millions of parameters. Such huge and complex parametric space makes deep learning models to be known as black box models. This means that we are not able to see the inside of an algorithm and understand how it arrives at a decision. The main objective of this research program is to develop mathematical tools and algorithms for effective and efficient fairness-aware deep learning approaches. The goal is to bring fairness-aware approaches to a level of maturity and reliability that anyone can trust these automated systems without a fear of discrimination. This research program will address the followings: (a) Describe how data is biased and how such biases are affecting the deep learning models. (b) Verify discrimination in trained deep learning models and specify how a trained model is not fair. (c) Deploy fairness guarantees in learning by either imposing fairness constraints or removing bias during learning. (d) Enforce fairness jointly in the learning and decision-making. (e) Explain the final decisions to the end users. Explaining the decision is a necessary step to validate the fairness of the process which can be done through a range of measures from describing the process of decision making to the end users, to providing solutions to change the undesirable decisions. The development and analysis of algorithmic discrimination in deep learning can be a key in enabling future scientific and technological progress. This research will contribute to Canada's position as a leader in artificial intelligence and fuel downstream applications, such as fair health care, that can help Canadians in their daily lives.