In his article “Robo-Apocalypse Cancelled? Reframing the Automation and Future of Work Debate,” Willcocks provides a critical reflection on the common assumption that artificial intelligence (AI) technologies have a massive impact on jobs. The article offers a much-needed and very helpful discussion entry that I certainly recommend to include in our teaching. While I fully agree that it is time to cancel the RoboApocalypse, I will argue that we first need to get rid of the more general and persistent naïve anxiety and admiration of the power that we assign to AI technologies, what Willcocks describes as the hype and fear narrative. It seems that we tend to ignore the most urgent question: what is it exactly about these technologies that legitimates all this fuss about AI changing our work? I will argue that it is time for information systems (IS) researchers trained in the sociotechnical tradition to step on board and fight the hype and fear narrative by offering empirically validated insights on why, when and how AI changes our work. Let me start with a disclaimer. Current research on AI technologies and work does not only address job losses. In fact, over the past 1 or 2 years, scholars from different disciplines have been studying changes in the quality of work instead of quantity of jobs. While strikingly silent during the Robo-Apocalypse debate—fearing among other things a second AI winter—computer scientists have recently joined the discussion on AI and work, stressing the need to keep the human in the loop (e.g. Dellermann et al., 2019). By developing hybrid AI, tools will become our new assistants, coaches and colleagues and thus will augment rather than automate work. While computer scientists’ aim is to build systems that increase the quality of work, a recently growing group of critical researchers aim instead to create societal awareness about the rise of low quality of work due to AI. They point, for example, to jobs under constant surveillance (Zuboff, 2019), jobs characterized as “ghost work” (Gray and Suri, 2019) and jobs imposed by inscrutable data-driven decisions (e.g. Faraj et al., 2018). In addition, organizational behavior scholars mainly use survey-based research to analyze, for example, the individual perceptions on the quality of work with AI (e.g. Brougham and Haar, 2017). Even those who started the hype–fear narrative, principally labor economic scholars, conduct research that departs from the focus on changes in number of jobs. For example, Felten et al.’s 2018 study how AI technologies are associated with changes in skills and wages and found that these new technologies might trigger a different type of polarization than one related to job losses and gains. While these latest academic contributions address the changes in quality of work instead of quantity of jobs, they share the implicit technological deterministic assumption that AI has the power in itself to change work. This is rather problematic; while technology has its own agency, it is also always socially constructed, which makes assumptions about its decisive impact unattainable and will only sustain the myths surrounding AI. Based mainly on the tradition of the field of Sociology of Technology Studies and Actor Network Theory, sociotechnical IS researchers know how to go beyond this technological determinism (Cecez-Kecmanovic et al., 2014). By using an historical and multi-actor perspective, Information systems research on artificial intelligence and work: A commentary on “RoboApocalypse cancelled? Reframing the automation and future of work debate”

中文翻译：

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人工智能与工作的信息系统研究：《机器人启示录取消？重新定义工作的自动化和未来辩论”

在他的文章“机器人启示录被取消？重新构建自动化和工作的未来辩论，”Willcocks 对人工智能 (AI) 技术对工作产生巨大影响的普遍假设进行了批判性反思。这篇文章提供了一个急需且非常有用的讨论条目，我当然建议将其包含在我们的教学中。虽然我完全同意现在是取消 RoboApocalypse 的时候了，但我认为我们首先需要摆脱对我们赋予人工智能技术的力量的更普遍和持久的天真的焦虑和钦佩，Willcocks 将其描述为炒作和恐惧叙述。似乎我们倾向于忽略最紧迫的问题：这些技术究竟是什么让关于人工智能改变我们工作的所有这些大惊小怪合法化？我认为，现在是接受社会技术传统培训的信息系统 (IS) 研究人员加入并打击炒作和恐惧叙事的时候了，通过提供有关 AI 为何、何时以及如何改变我们工作的经验验证的见解。让我从免责声明开始。当前对人工智能技术和工作的研究不仅解决了失业问题。事实上，过去一两年，不同学科的学者一直在研究工作质量而不是工作数量的变化。虽然在 Robo-Apocalypse 辩论中惊人地沉默——除其他外，他们担心第二个人工智能冬天——计算机科学家最近加入了关于人工智能和工作的讨论，强调需要让人类参与其中（例如 Dellermann 等人，2019 年）。通过开发混合人工智能，工具将成为我们的新助手、教练和同事，从而增强而不是自动化工作。虽然计算机科学家的目标是建立提高工作质量的系统，但最近越来越多的批判性研究人员旨在提高社会对人工智能导致的低质量工作的认识。例如，他们指出受到持续监控的工作（Zuboff，2019 年）、被称为“幽灵工作”的工作（Gray 和 Suri，2019 年）以及由难以理解的数据驱动决策强加的工作（例如 Faraj 等人，2018 年）。此外，组织行为学者主要使用基于调查的研究来分析，例如个人对人工智能工作质量的看法（例如 Brougham 和 Haar，2017）。即使是那些开始炒作恐惧叙述的人，主要是劳动经济学学者，进行的研究偏离了对工作数量变化的关注。例如，Felten 等人在 2018 年研究了人工智能技术如何与技能和工资的变化相关联，并发现这些新技术可能引发一种与失业和收益相关的两极分化类型不同的两极分化。虽然这些最新的学术贡献解决了工作质量而不是工作数量的变化，但它们都有一个隐含的技术确定性假设，即人工智能本身具有改变工作的能力。这是相当有问题的；虽然技术有自己的能动性，但它也始终是社会建构的，这使得对其决定性影响的假设无法实现，只会支持围绕人工智能的神话。主要基于技术研究社会学和行动者网络理论领域的传统，社会技术信息系统研究人员知道如何超越这种技术决定论（Cecez-Kecmanovic 等，2014）。从历史和多参与者的角度来看，人工智能和工作的信息系统研究：对“机器人启示录取消？重新定义工作的自动化和未来辩论”