Resources

This paper presents an approach for describing and characterizing al-gorithms that are discussed as though they embody artificial intelligence. After identifying key assumptions related to algorithms and summarizing work sys-tem theory (WST), this paper uses a hypothetical example to introduces aspects of WST and two additional ideas, facets of work and dimensions of smartness in devices and systems. Next, it applies those ideas to aspects of five AI-related examples presented by entrepreneurs and researchers at an MIT AI conference in July 2020.

The rise of data-driven technologies in recent years has sparked renewed attention to questions of technological sensemaking and in particular the explainability or interpretability of such systems (a growing technical subfield commonly called “Explainable AI” or “XAI”). These approaches tend to focus on the technology itself (often at the level of model or predictive output) and fail to consider the broader context of use and the situated nature of technological sensemaking.

Course Description: Humanities senior seminar. Combine current theory with practice to prepare students as socially conscious technology developers. Analyze complex social and technical situations to develop socially appropriate responses through tasks that involve problem analysis, ethical considerations, and technology issues regarding influences on the distribution of jobs and nature of work.

The Workshop on Work in the Age of Intelligent Machines was held at the 6th AAAI Conference on Human Computation and Crowdsourcing (HCOMP) on July 5, 2018 in Zurich, Switzerland. The workshop explored ways in which human work and occupations will be changed as artificial intelligence becomes more increasingly prevalent in the workplace. The workshop was a part of a series of workshops funded by a National Science Foundation (NSF) convergence grant on the future of work.