Explainable Disparity Compensation for Efficient Fair Ranking

Ranking functions that are used in decision systems often produce disparate results for different populations because of bias in the underlying data. Addressing, and compensating for, these disparate outcomes is a critical problem for fair decision-making. Recent compensatory measures have mostly focused on opaque transformations of the ranking functions to satisfy fairness guarantees or on the use of quotas or set-asides to guarantee a minimum number of positive outcomes to members of underrepresented groups. In this paper we propose easily explainable data-driven compensatory measures for ranking functions. Our measures rely on the generation of bonus points given to members of underrepresented groups to address disparity in the ranking function. The bonus points can be set in advance, and can be combined, allowing for considering the intersections of representations and giving better transparency to stakeholders. We propose efficient sampling-based algorithms to calculate the number of bonus points to minimize disparity. We validate our algorithms using real-world school admissions and recidivism datasets, and compare our results with that of existing fair ranking algorithms.Comment: 22 pages, 5 figure

CD4-positive T cells and M2 macrophages dominate the peritoneal infiltrate of patients with encapsulating peritoneal sclerosis

Background Encapsulating peritoneal sclerosis (EPS) is a severe complication of peritoneal dialysis (PD). Previously, it has been shown that infiltrating CD4-positive T cells and M2 macrophages are associated with several fibrotic conditions. Therefore, the characteristics of the peritoneal cell infiltrate in EPS may be of interest to understand EPS pathogenesis. In this study, we aim to elucidate the composition of the peritoneal cell infiltrate in EPS patients and relate the findings to clinical outcome. Study Design, Setting, and Participants We studied peritoneal membrane biopsies of 23 EPS patients and compared them to biopsies of 15 PD patients without EPS. The cellular infiltrate was characterized by immunohistochemistry to detect T cells(CD3-positive), CD4-positive (CD4+) and CD8-positive T cell subsets, B cells(CD20-positive), granulocytes(CD15-positive), macrophages(CD68-positive), M1(CD80-positive), and M2(CD163-positive) macrophages. Tissues were analysed using digital image analysis. Kaplan-Meier survival analysis was performed to investigate the survival in the different staining groups. Results The cellular infiltrate in EPS biopsies was dominated by mononuclear cells. For both CD3 and CD68, the median percentage of area stained was higher in biopsies of EPS as opposed to non-EPS patients (p<0.001). EPS biopsies showed a higher percentage of area stained for CD4 (1.29%(0.61-3.20)) compared to CD8 (0.71%(0.46-1.01), p = 0.04), while in the non-EPS group these cells were almost equally represented (respectively 0.28% (0.05-0.83) versus 0.22%(0.17-0.43), p = 0.97). The percentage of area stained for both CD80 and CD163 was higher in EPS than in non-EPS biopsies (p<0.001), with CD163+ cells being the most abundant phenotype. Virtually no CD20-positive and CD15-positive cells were present in biopsies of a subgroup of EPS patients. No relation was found between the composition of the mononuclear cell infiltrate and clinical outcome. Conclusions A characteristic mononuclear cell infiltrate consisting of CD4+ and CD163+ cells dominates the peritoneum of EPS patients. These findings suggest a role for both CD4+ T cells and M2 macrophages in the pathogenesis of EPS

Abstract Projecting XML Documents

XQuery is not only useful to query XML in databases, but also to applications that must process XML documents as files or streams. These applications suffer from the limitations of current main-memory XQuery processors which break for rather small documents. In this paper we propose techniques, based on a notion of projection for XML, which can be used to drastically reduce memory requirements in XQuery processors. The main contribution of the paper is a static analysis technique that can identify at compile time which parts of the input document are needed to answer an arbitrary XQuery. We present a loading algorithm that takes the resulting information to build a projected document, which is smaller than the original document, and on which the query yields the same result. We implemented projection in the Galax XQuery processor. Our experiments show that projection reduces memory requirements by a factor of 20 on average, and is effective for a wide variety of queries. In addition, projection results in some speedup during query evaluation.