Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment

Automated data-driven decision making systems are increasingly being used to assist, or even replace humans in many settings. These systems function by learning from historical decisions, often taken by humans. In order to maximize the utility of these systems (or, classifiers), their training involves minimizing the errors (or, misclassifications) over the given historical data. However, it is quite possible that the optimally trained classifier makes decisions for people belonging to different social groups with different misclassification rates (e.g., misclassification rates for females are higher than for males), thereby placing these groups at an unfair disadvantage. To account for and avoid such unfairness, in this paper, we introduce a new notion of unfairness, disparate mistreatment, which is defined in terms of misclassification rates. We then propose intuitive measures of disparate mistreatment for decision boundary-based classifiers, which can be easily incorporated into their formulation as convex-concave constraints. Experiments on synthetic as well as real world datasets show that our methodology is effective at avoiding disparate mistreatment, often at a small cost in terms of accuracy.Comment: To appear in Proceedings of the 26th International World Wide Web Conference (WWW), 2017. Code available at: https://github.com/mbilalzafar/fair-classificatio

Unleashing the Potential of US Foundation Endowments: Using Responsible Investment to Strengthen Endowment Oversight and Enhance Impact

A small but growing number of US foundations are investigating or pursuing sustainable and responsible investing approaches -- often employing such terms as mission-related investing or impact investing. They are embracing the notion that in addition to making grants, they can employ investment and shareowner strategies across their assets to help achieve positive societal outcomes and targeted financial returns. This report is designed for foundation staff and trustees who are interested in encouraging their institutions to align a broader portion of their assets under management with their programmatic goals or to factor environmental, social and corporate governance (ESG) issues into their investment decisions to help fulfill fiduciary duties. Practitioners in the sustainable and responsible investment industry who serve foundations, including consultants, research providers, financial advisors, and investment managers, can also benefit from the information and resources in this paper.Using extensive data from primary and secondary resources, this paper presents the current range and state of involvement by foundations in sustainable and responsible investing (SRI) and profiles a number of foundations whose approaches to SRI have resulted in meaningful environmental, social or corporate governance outcomes. It demonstrates that it is feasible for foundations to invest their endowments in alignment with their mission and ESG issues of concern, while at the same time achieving their overall financial goals. This report also details a range of resources, including many that have emerged just in the past few years, available to foundations in their efforts to explore SRI. Last, the report offers recommendations and ideas for foundation officers and trustees to enable them to guide their institutions into this space