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Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REvealing VIsual biaSEs (REVISE) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://github.com/princetonvisualai/revise-tool. [ABSTRACT FROM AUTHOR]

We coin the term geoMobile data to emphasize datasets that exhibit geo-spatial features reflective of human behaviors. We propose and develop an EPIC framework to mine latent patterns from geoMobile data and provide meaningful interpretations: we first 'E'xtract latent features from high dimensional geoMobile datasets via Laplacian Eigenmaps and perform clustering in this latent feature space; we then use a state-of-the-art visualization technique to 'P'roject these latent features into 2D space; and finally we obtain meaningful 'I'nterpretations by 'C'ulling cluster-specific significant feature-set. We illustrate that the local space contraction property of our approach is most superior than other major dimension reduction techniques. Using diverse real-world geoMobile datasets, we show the efficacy of our framework via three case studies. [ABSTRACT FROM AUTHOR]

We are entering an era of ubiquitous genetic information for research, clinical care and personal curiosity. Sharing these data sets is vital for progress in biomedical research. However, a growing concern is the ability to protect the genetic privacy of the data originators. Here, we present an overview of genetic privacy breaching strategies. We outline the principles of each technique, indicate the underlying assumptions, and assess their technological complexity and maturation. We then review potential mitigation methods for privacy-preserving dissemination of sensitive data and highlight different cases that are relevant to genetic applications. [ABSTRACT FROM AUTHOR]