避免机器学习的压抑未来:解放式助手的设计理论

MIS Q. Pub Date : 2021-03-01 DOI:10.25300/MISQ/2021/1578
Gerald C. Kane, A. Young, A. Majchrzak, S. Ransbotham
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引用次数: 34

摘要

机器学习(ML)系统的广泛使用可能会导致无处不在的监控和行为控制的压迫性未来,出于对话的目的,我们称之为“信息狂热”。这种反乌托邦的未来源于机器学习系统基于训练数据的固有设计,而不是用代码构建的。为了避免这种压迫性的未来,我们开发了一个解放助手(EA)的概念,这是一个与人类用户互动的机器学习系统,帮助他们在信息狂的压迫环境中理解和制定解放性的结果。利用解放式教学法作为核心理论,我们开发了两套设计原则:一套是针对近期的,另一套是针对长期的。设计师根据个人用户的解放结果优化EA,这可以通过在必要时与压迫性ML平台建立对抗关系来保护用户免受信息狂的压迫。这些原则应该鼓励IS研究人员扩大响应ML系统涌入的可能性范围。鉴于信息系统研究体现了社会和技术专长的融合,我们鼓励其他信息系统研究人员大胆地将新兴技术对社会的长期影响理论化,并有可能改变其发展轨迹。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Avoiding an Oppressive Future of Machine Learning: A Design Theory for Emancipatory Assistants
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.
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