Fairness and Security
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With the trending discussions on the potential dangers that AI may bring to our society, I carry out research on AI for social good. For example, naive automatic decision makings (e.g., for jurying a bail decision, or for making a school admission choice) will possibly reinforce the bias in the training data, which will lead to discriminations. I study fairness in AI systems, and propose algorithms that can guarantee sensible fairness definitions. |
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Another topic that I found to be extremely important is to protect human subjects' data from being leaked and breached. With technology interacting with our daily life more and more frequently, an increasing amount of our personal data is at the risk of being leaked, e.g., mobile apps gather GPS and use data to track our locations. Potential data (e.g., sensitive information such as credit card information) breach can lead to billions of social losses. In fact, data breach in 2016 leads to a loss of 12 billion US dollars. I developed the first prototype system that leverages on machine learning and data analysis to forecast future data breaches. |
Ongoing works
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Bayesian fairness, with Goran Radanovic, Christos Dimitrakakis and David Parkes. Fairness machine, with David Parkes et al. Preferred Classification , with Berk Ustun. |
Relevant works
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Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal and David Parkes |
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Yang Liu |
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Yang Liu, Armin Sarabi, Jing Zhang, Parinaz Ardabili, Manish Karir, Michael Bailey and Mingyan Liu |
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Armin Sarabi, Parinaz Naghizadeh, Yang Liu and Mingyan Liu |
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Yang Liu and Mingyan Liu |