Fairness Aware Counterfactuals for Subgroups

In this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refined notions of subgroup fairness. We aim to (a) formulate different aspects of the difficulty of individuals in certain subgroups to achieve recourse, i.e. receive the desired outcome, either at the micro level, considering members of the subgroup individually, or at the macro level, considering the subgroup as a whole, and (b) introduce notions of subgroup fairness that are robust, if not totally oblivious, to the cost of achieving recourse. We accompany these notions with an efficient, model-agnostic, highly parameterizable, and explainable framework for evaluating subgroup fairness. We demonstrate the advantages, the wide applicability, and the efficiency of our approach through a thorough experimental evaluation of different benchmark datasets

Efficient Time-Series Clustering through Sparse Gaussian Modeling

In this work, we consider the problem of shape-based time-series clustering with the widely used Dynamic Time Warping (DTW) distance. We present a novel two-stage framework based on Sparse Gaussian Modeling. In the first stage, we apply Sparse Gaussian Process Regression and obtain a sparse representation of each time series in the dataset with a logarithmic (in the original length T) number of inducing data points. In the second stage, we apply k-means with DTW Barycentric Averaging (DBA) to the sparsified dataset using a generalization of DTW, which accounts for the fact that each inducing point serves as a representative of many original data points. The asymptotic running time of our Sparse Time-Series Clustering framework is Ω(T2/log2T) times faster than the running time of applying k-means to the original dataset because sparsification reduces the running time of DTW from Θ(T2) to Θ(log2T). Moreover, sparsification tends to smoothen outliers and particularly noisy parts of the original time series. We conduct an extensive experimental evaluation using datasets from the UCR Time-Series Classification Archive, showing that the quality of clustering computed by our Sparse Time-Series Clustering framework is comparable to the clustering computed by the standard k-means algorithm

Lymphocutaneous nocardiosis in a kidney transplant patient successfully treated with tigecycline

Cutaneous nocardiosis is an infrequent infection which has been increasingly reported in immunocompromised patients. Although trimethoprim-sulfamethoxazole is considered to be the agent of choice for treatment of nocardiosis, newer antimicrobials such as tigecycline have been proven to be effective in vitro, as well. We report the first case of primary cutaneous nocardiosis in a renal transplant recipient having corresponded well to treatment with tigecycline

Fairness Aware Counterfactuals for Subgroups

POSTERInternational audienceIn this work, we present Fairness Aware Counterfactuals for Subgroups (FACTS), a framework for auditing subgroup fairness through counterfactual explanations. We start with revisiting (and generalizing) existing notions and introducing new, more refined notions of subgroup fairness. We aim to (a) formulate different aspects of the difficulty of individuals in certain subgroups to achieve recourse, i.e. receive the desired outcome, either at the micro level, considering members of the subgroup individually, or at the macro level, considering the subgroup as a whole, and (b) introduce notions of subgroup fairness that are robust, if not totally oblivious, to the cost of achieving recourse. We accompany these notions with an efficient, model-agnostic, highly parameterizable, and explainable framework for evaluating subgroup fairness. We demonstrate the advantages, the wide applicability, and the efficiency of our approach through a thorough experimental evaluation of different benchmark datasets