Demonstrations
clstk: The Cross-Lingual Summarization Tool-Kit
Tuesday, February 12 | 3:30 PM–4:15 PM
Nisarg Jhaveri, Manish Gupta, Vasudeva Varma
Half-Day Tutorials
Causal Inference and Counterfactual Reasoning
Monday, February 11 | 9:00 AM–12:30 PM
Emre Kiciman, Amit Sharma
As computing systems are more frequently and more actively intervening to improve people’s work and daily lives, it is critical to correctly predict and understand the causal effects of these interventions. This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from abroad literature from statistics, social sciences and machine learning. To tackle such questions, we will introduce the key ingredient that causal analysis depends on—counterfactual reasoning—and describe the two most popular frameworks based on Bayesian graphical models and potential outcomes. Based on this, we will cover a range of methods suitable for doing causal inference with large-scale online data, including randomized experiments, observational methods like matching and stratification, and natural experiment-based methods such as instrumental variables and regression discontinuity. We will also focus on best practices for evaluation and validation of causal inference techniques, drawing from our own experiences.
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned
Monday, February 11 | 1:30 PM–5:00 PM
Sarah Bird, Krishnaram Kenthapadi, Emre Kiciman, Margaret Mitchell
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine-learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. This tutorial presents an overview of algorithmic bias/discrimination issues observed over the last few years and the lessons learned key regulations and laws, and evolution of techniques for achieving fairness in machine learning systems. We will motivate the need for adopting a “fairness by design” approach (as opposed to viewing algorithmic bias/fairness considerations as an afterthought) when developing machine learning based models and systems for different consumer and enterprise applications. Then, we will focus on the application of fairness-aware machine learning techniques in practice, by presenting non-proprietary case studies from different technology companies. Finally, based on our experiences working on fairness in machine learning at companies such as Facebook, Google, LinkedIn, and Microsoft, we will present open problems and research directions for the data mining/machine learning community.