'It's Reducing a Human Being to a Percentage'; Perceptions of Justice in Algorithmic Decisions

Data-driven decision-making consequential to individuals raises important questions of accountability and justice. Indeed, European law provides individuals limited rights to 'meaningful information about the logic' behind significant, autonomous decisions such as loan approvals, insurance quotes, and CV filtering. We undertake three experimental studies examining people's perceptions of justice in algorithmic decision-making under different scenarios and explanation styles. Dimensions of justice previously observed in response to human decision-making appear similarly engaged in response to algorithmic decisions. Qualitative analysis identified several concerns and heuristics involved in justice perceptions including arbitrariness, generalisation, and (in)dignity. Quantitative analysis indicates that explanation styles primarily matter to justice perceptions only when subjects are exposed to multiple different styles---under repeated exposure of one style, scenario effects obscure any explanation effects. Our results suggests there may be no 'best' approach to explaining algorithmic decisions, and that reflection on their automated nature both implicates and mitigates justice dimensions.Comment: 14 pages, 3 figures, ACM Conference on Human Factors in Computing Systems (CHI'18), April 21--26, Montreal, Canad

Machine Learning Education for Artists, Musicians, and Other Creative Practitioners

This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses—an online course as well as undergraduate and masters-level courses taught at a university—to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research