Fairness is an increasingly important concern as machine learning models are used to support decision making in high-stakes applications such as mortgage lending, hiring, and prison sentencing. This paper introduces a new open source Python toolkit for algorithmic fairness, AI Fairness 360 (AIF360), released under an Apache v2.0 license (https://github.com/ibm/aif360). The main objectives of this toolkit are to help facilitate the transition of fairness research algorithms to use in an industrial setting and to provide a common framework for fairness researchers to share and evaluate algorithms.The package includes a comprehensive set of fairness metrics for datasets and models, explanations for these metrics, and algorithms to mitigate bias in datasets and models. It also includes an interactive Web experience (https://aif360.mybluemix.net) that provides a gentle introduction to the concepts and capabilities for line-of-business users, as well as extensive documentation, usage guidance, and industry-specific tutorials to enable data scientists and practitioners to incorporate the most appropriate tool for their problem into their work products. The architecture of the package has been engineered to conform to a standard paradigm used in data science, thereby further improving usability for practitioners. Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking. A built-in testing infrastructure maintains code quality.

Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. Inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI and provide examples for two fictitious AI services in the appendix of the paper. * A. Olteanu's work was done while at IBM Research. Author is currently affiliated with Microsoft Research.

It is much easier to look for an unfamiliar character (upsidedown A) embedded among familiar ones than to look for a familiar character (A) among unfamiliar ones. Furthermore, the nature of the background seems more important to performance than the nature of the target. The basic experiments involved showing subjects a matrix with nine characters. On target-present trials, eight of the characters were background items and one character was a target item. On target-absent trials, all nine matrix positions were filled by background characters. The types of unfamiliar characters used were rotated English letters, partial letters, and Gibson figures. The familiar characters were upright English letters or digits. Search was easier through familiar backgrounds than through unfamiliar backgrounds with all of the character types used and whether measuring speed or accuracy.

Over 100 top-traffic and government websites from the United States and United Kingdom were examined for evidence of changes on accessibility indicators over the 14-year period from 1999 to 2012, the longest period studied to date. Automated analyses of WCAG 2.0 Level A Success Criteria found high percentages of violations overall. Unlike more circumscribed studies, however, these sites exhibited improvements over the years on a number of accessibility indicators, with government sites being less likely than topsites to have accessibility violations. Examination of the causes of success and failure suggests that improving accessibility may be due, in part, to changes in website technologies and coding practices rather than a focus on accessibility per se.