AVOIDING AN OPPRESSIVE FUTURE OF MACHINE LEARNING: A DESIGN THEORY FOR EMANCIPATORY ASSISTANTS

• 成果类型: Article
• 署名作者: Kane, Gerald C.; Young, Amber G.; Majchrzak, Ann; Ransbotham, Sam
• 署名单位: Boston College; University of Arkansas System; University of Arkansas Fayetteville; University of Southern California
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2021/1578
• 发表日期: 2021
• 页码: 371-396
• 关键词: information-systems research; management; FRAMEWORK; science; media; intelligence; rationality; PERSPECTIVE; empowerment; TECHNOLOGY

摘要

Widespread use of machine learning (ML) systems could result in an oppressive future of ubiquitous monitoring and behavior control that, for dialogic purposes, we call Informania. This dystopian future results from ML systems' inherent design based on training data rather than built with code. To avoid this oppressive future, we develop the concept of an emancipatory assistant (EA), an ML system that engages with human users to help them understand and enact emancipatory outcomes amidst the oppressive environment of Informania. Using emancipatory pedagogy as a kernel theory, we develop two sets of design principles: one for the near future and the other for the far-term future. Designers optimize EA on emancipatory outcomes for an individual user, which protects the user from Informania's oppression by engaging in an adversarial relationship with its oppressive ML platforms when necessary. The principles should encourage IS researchers to enlarge the range of possibilities for responding to the influx of ML systems. Given the fusion of social and technical expertise that IS research embodies, we encourage other IS researchers to theorize boldly about the long-term consequences of emerging technologies on society and potentially change their trajectory.