Cebulla Andreas, Szpak Zygmunt, Howell Catherine, Knight Genevieve, Hussain Sazzad
Australian Industrial Transformation Institute, Flinders University, Adelaide, Australia.
Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia.
AI Soc. 2023;38(2):919-935. doi: 10.1007/s00146-022-01460-9. Epub 2022 May 13.
Artificial Intelligence (AI) is taking centre stage in economic growth and business operations alike. Public discourse about the practical and ethical implications of AI has mainly focussed on the societal level. There is an emerging knowledge base on AI risks to human rights around data security and privacy concerns. A separate strand of work has highlighted the stresses of working in the gig economy. This prevailing focus on human rights and gig impacts has been at the expense of a closer look at how AI may be reshaping traditional workplace relations and, more specifically, workplace health and safety. To address this gap, we outline a conceptual model for developing an AI Work Health and Safety (WHS) Scorecard as a tool to assess and manage the potential risks and hazards to workers resulting from AI use in a workplace. A qualitative, practice-led research study of AI adopters was used to generate and test a novel list of potential AI risks to worker health and safety. Risks were identified after cross-referencing Australian AI Ethics Principles and Principles of Good Work Design with AI ideation, design and implementation stages captured by the AI Canvas, a framework otherwise used for assessing the commercial potential of AI to a business. The unique contribution of this research is the development of a novel matrix itemising currently known or anticipated risks to the WHS and ethical aspects at each AI adoption stage.
人工智能(AI)在经济增长和商业运营中都占据着核心地位。关于人工智能的实际和伦理影响的公众讨论主要集中在社会层面。围绕数据安全和隐私问题,已经出现了一个关于人工智能对人权风险的知识库。另一项独立的研究工作强调了零工经济中的工作压力。这种对人权和零工影响的普遍关注,是以牺牲对人工智能如何重塑传统工作场所关系,更具体地说是工作场所健康和安全的深入研究为代价的。为了填补这一空白,我们概述了一个概念模型,用于开发人工智能工作健康与安全(WHS)记分卡,作为评估和管理工作场所使用人工智能给工人带来的潜在风险和危害的工具。一项针对人工智能采用者的定性、实践导向的研究,用于生成和测试一份关于工人健康和安全的潜在人工智能风险的新清单。在将澳大利亚人工智能伦理原则和良好工作设计原则与人工智能画布(一个原本用于评估人工智能对企业商业潜力的框架)所捕捉的人工智能构思、设计和实施阶段进行交叉参考后,识别出了风险。这项研究的独特贡献在于开发了一个新颖的矩阵,列出了在每个人工智能采用阶段对工作健康与安全以及伦理方面目前已知或预期的风险。