Beyond Being Accurate: Toward More Inclusive and Fairer Models using Focused Learning and Adversarial Training
One big challenge of machine learning research is devising approaches to learn more inclusive and fairer models. For example, when building a recommender system, how can we ensure that every user and item are actually modeled well? Typically, recommender systems are built, optimized, and tuned to improve a single global prediction objective. However, as we will show, recommenders often leave many items or users badly-modeled and thus under-served. As a result, we ask the following question: how can we improve models for a specified subset of items or users? In this talk, we will discuss recent research at Google toward more inclusiveness and fairer models using two techniques  : First, we offer a new technique called "focused learning" that is based on hyperparameter optimization and a modified optimization objective. We demonstrate prediction improvements on multiple datasets. For instance, on MovieLens we achieve as much as a 17% improvement for niche movies, cold-start items, and even the most badly-modeled items in the original model.
Second, using adversarial training in a multi-task network, we remove information about a sensitive subgroup or an attribute from the latent representation learned by the neural network. In particular, we study how the choice of data for the adversarial training impacts the resulting model. We find two interesting results---encouragingly, only a small amount of data is needed to train these adversarial models; and a balanced distribution of examples significantly enables more inclusive and fairer models.
 Alex Beutel, Ed H. Chi, Zhiyuan Cheng, Hubert Pham, John Anderson. Beyond Globally Optimal: Focused Learning for Improved Recommendations. WWW, 2017.
 Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi. Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations. KDD FATML Workshop, 2017.
Ed H. Chi is a Principal Scientist at Google, leading a machine learning research team focused on recommendation systems and social computing research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With 39 patents and over 110 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior. Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center's Augmented Social Cognition Group, where he led the team in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on software systems since 1993. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press, and has won awards for both teaching and research. In his spare time, Ed is an avid photographer and snowboarder, and has a blackbelt in Taekwondo.