An external stability audit framework to test the validity of personality prediction in AI hiring.

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers' resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity.

Teaching Responsible Data Science: Charting New Pedagogical Territory.

Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness.

Diversity in Big Data: A Review.

Big data technology offers unprecedented opportunities to society as a whole and also to its individual members. At the same time, this technology poses significant risks to those it overlooks. In this article, we give an overview of recent technical work on diversity, particularly in selection tasks, discuss connections between diversity and fairness, and identify promising directions for future work that will position diversity as an important component of a data-responsible society.

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments.

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments.

MutaGeneSys: estimating individual disease susceptibility based on genome-wide SNP array data.

Unlabelled: We present MutaGeneSys: a system that uses genome-wide genotype data to estimate disease susceptibility. Our system integrates three data sources: the International HapMap project, whole-genome marker correlation data and the Online Mendelian Inheritance in Man (OMIM) database. It accepts SNP data of individuals as query input and delivers disease susceptibility hypotheses even if the original set of typed SNPs is incomplete.