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Generative AI is a class of machine learning technology that learns to generate new data from training data. While deep fakes and media-and art-related generative AI breakthroughs have recently caught people's attention and imagination, the overall area is in its infancy for business use. Further, little is known about generative AI's potential for malicious misuse at large scale. Using co-creation design fictions with AI engineers, we explore the plausibility and severity of business misuse cases., IUI 2020 Workshop on Human-AI Co-Creation with Generative Models

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., 20 pages

Personal Television is here via the advent of a new class of devices called personal video recorders (PVRs). These recorders change the user task from (a) selecting a specific channel to watch from the 100+ available channels to (b) finding something “good” to record from the 10,000+ shows broadcast each week. Recommender systems, such as the one described in this paper, will help track users’ preferences and aid users in choosing shows to record. In this paper we advance a multi-agent TV recommender system that encapsulates three user information streams--implicit view history, explicit preferences, and feedback information on specific shows--into adaptive agents and generates program recommendations for a TV viewer. We have tested the system in various agent combinations with real users drawn from a wide variety of living conditions. The combination of implicit and explicit agents seems to work best in our framework.