Papers

Gender bias is one of the most common and well-studied demographic biases in information retrieval, and in general in AI systems. After discovering and reporting that gender bias for certain professions could change searchers' worldviews, mainstreaming image search engines, such as Google, quickly took actions to correct and fix such a bias. However, given the nature of these systems, viz., being opaque, it is unclear if they addressed unequal gender representation and gender stereotypes in image search results systematically and in a sustainable way. In this paper, we propose adversarial attack queries composing of professions and countries (e.g., `CEO United States') to investigate whether gender bias is thoroughly mitigated by image search engines. Our experiments on Google, Baidu, Naver, and Yandex Image Search show that the proposed attack can trigger high levels of gender bias in image search results very effectively. To defend against such attacks and mitigate gender bias, we design and implement three novel re-ranking algorithms -- epsilon-greedy algorithm, relevance-aware swapping algorithm, and fairness-greedy algorithm, to re-rank returned images for given image queries. Experiments on both simulated (three typical gender distributions) and real-world (18,904 images) datasets demonstrate the proposed algorithms can mitigate gender bias effectively.

poster session 3 @ red 6, poster session 10 @ red 6, oral session 3 @ red 6, poster session 3, poster session 10, oral session 3

Impossibility results show that important fairness measures (independence, separation, sufficiency) cannot be satisfied at the same time under reasonable assumptions. This paper explores whether we can satisfy and/or improve these fairness measures simultaneously to a certain degree. We introduce information-theoretic formulations of the fairness measures and define degrees of fairness based on these formulations. The information-theoretic formulations suggest unexplored theoretical relations between the three fairness measures. In the experimental part, we use the information-theoretic expressions as regularizers to obtain fairness-regularized predictors for three standard datasets. Our experiments show that a) fairness regularization directly increases fairness measures, in line with existing work, and b) some fairness regularizations indirectly increase other fairness measures, as suggested by our theoretical findings. This establishes that it is possible to increase the degree to which some fairness measures are satisfied at the same time -- some fairness measures are gradually compatible.

poster session 3 @ red 6, poster session 10 @ red 6, oral session 3 @ red 6, poster session 3, poster session 10, oral session 3

Along with the rapid growth and rise to prominence of food delivery platforms, concerns have also risen about the terms of employment of the ``gig workers'' underpinning this growth. Our analysis on data derived from a real-world food delivery platform across three large cities from India show that there is significant inequality in the money delivery agents earn. In this paper, we formulate the multi-objective problem of fair income distribution among agents while also ensuring timely food delivery. We establish that the problem is not only NP-hard but also inapproximable in polynomial time. We overcome this computational bottleneck through a novel matching algorithm called FairFoody. Extensive experiments over real-world food delivery datasets show FairFoody imparts up to 10 times improvement in equitable income distribution when compared to baseline strategies, while also ensuring minimal impact on customer experience.

poster session 3 @ red 6, poster session 10 @ red 6, oral session 3 @ red 6, poster session 3, poster session 10, oral session 3