The law and the loop

Ambrose, Meg Leta

doi:10.1109/ethics.2014.6893374

Cited by 2 publications

(1 citation statement)

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“…Zeng (2015) highlights that current legislation refers mostly to low-tech technologies, leaving advanced AI systems unregulated. Consequently, the legal and policymaking approaches to AI ethics are reactionary (i.e., triggered sporadically by accidents that occur) rather than holistic (i.e., generally preventative; Ambrose, 2014). Further research that examines how legal and policy decisions are debated, agreed and implemented is needed to understand how different societies are responding to the challenges and opportunities intelligent machines present for knowledge and service work.…”

Section: Research Priorities For the Three Themesmentioning

confidence: 99%

The Changing Nature of Knowledge and Service Work in the Age of Intelligent Machines

Coombs

Hislop

Taneva

et al. 2020

The Oxford Handbook of Digital Technology and Society

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One of the most significant recent technological developments concerns the application of intelligent machines to jobs that up to now have been considered safe from automation. These changes have generated considerable debate regarding the impacts that the widespread adoption of intelligent machines could have on the nature of work. This chapter provides a thematic review, across multiple academic disciplines, of the current state of academic knowledge regarding the impact of intelligent machines on knowledge and service work. Adopting a work-practice perspective, the chapter reviews the extant literature concerning changing relations between workers and intelligent machines, the adoption and acceptance of intelligent machines, and ethical issues associated with greater machine human collaboration. A key finding is that much of the research discusses intelligent machines complementing and extending human capabilities rather than removing humans from work processes. The concept of augmentation of humans and human work, rather than wholesale replacement from automation, flows through the literature across a range of domains. The chapter concludes with a discussion of the main gaps in existing knowledge and ways in which future research may provide a deeper understanding of how people (currently and in the near future) experience intelligent machines in their day-to-day work practice. These include the need for multi-disciplinary research, the role of contexts, the need for more and better empirical research, the changing relationships between humans and intelligent machines, the adoption and acceptance of the technology, and ethical issues.

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Section: Research Priorities For the Three Themesmentioning

confidence: 99%