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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., Comment: IUI 2020 Workshop on Human-AI Co-Creation with Generative Models

AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.

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., Comment: 20 pages

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2006., Includes bibliographical references (leaves 105-109)., Approaches to shape computation and algorithmic art-making within the fields of shape grammars and computer graphics still do not consider the immediacy of the artist's mark in drawing and painting. This research examines the canvas, or 2D picture plane, as the expressive and dynamic problem space of the artist who naturally reframes both the problem and the solution with each successive mark. The fluidity of the artist's mark is the most important element in transforming the blank canvas into an image. In this research, I use my own traditionally drawn and painted artwork as the baseline corpus for analysis. From my analysis, I define a nonsymbolic, formal grammar for the synthesis of images in the style of the baseline corpus and develop software prototype "sketches" to support the geometric representation of freehand sketching consistent with mark-making. Curve generation is critical to the expressive marks of the artist. The result of this research is a curvilinear shape grammar that supports both explicit and implicit shape recognition while affording the artist the ability to draw (shape union) and erase (shape difference) computationally., (cont.) I compare the results of the synthesis phase with my traditional sketches showing that it is possible to compute imagery consistent with the evolving style of the artist's own hand. Additionally, the analysis phase of the research supports the supposition that formal algorithmic understanding of one's artistic process has directive and positive influences on the evolution and refinement of the style., by Jacquelyn A. Martino., Ph.D.