On Discrimination Discovery and Removal in Ranked Data using Causal Graph

Predictive models learned from historical data are widely used to help companies and organizations make decisions. However, they may digitally unfairly treat unwanted groups, raising concerns about fairness and discrimination. In this paper, we study the fairness-aware ranking problem which aims to discover discrimination in ranked datasets and reconstruct the fair ranking. Existing methods in fairness-aware ranking are mainly based on statistical parity that cannot measure the true discriminatory effect since discrimination is causal. On the other hand, existing methods in causal-based anti-discrimination learning focus on classification problems and cannot be directly applied to handle the ranked data. To address these limitations, we propose to map the rank position to a continuous score variable that represents the qualification of the candidates. Then, we build a causal graph that consists of both the discrete profile attributes and the continuous score. The path-specific effect technique is extended to the mixed-variable causal graph to identify both direct and indirect discrimination. The relationship between the path-specific effects for the ranked data and those for the binary decision is theoretically analyzed. Finally, algorithms for discovering and removing discrimination from a ranked dataset are developed. Experiments using the real dataset show the effectiveness of our approaches.Comment: 9 page

Understanding Opportunities in Social Entrepreneurship: A Critical Realist Abstraction

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper extends social entrepreneurship (SE) research by drawing upon a critical realist perspective to analyse dynamic structure/agency relations in SE opportunity emergence, illustrated by empirical evidence. Our findings demonstrate an agential aspect (opportunity actualisation following a path-dependent seeding-growing-shaping process) and a structural aspect (institutional, cognitive and embedded structures necessary for SE opportunity emergence) related to SE opportunities. These structures provide three boundary conditions for SE agency: institutional discrimination, an SE belief system and social feasibility. Within this paper, we develop a novel theoretical framework to analyse SE opportunities plus, an applicable tool to advance related empirical research