We know that technological development often opens new areas of application. The latest technological advancements in AI and ML open up for increasing the range of application areas (i.e., areas for automation) as well as the levels of automation. HCI-researchers have referred to socio-technical shifts as paradigm shifts (Harrison et al. 2007) or waves Bødker 2006; 2015; 2016; Duarte and Baranauskas 2016), emphasizing that the waves coexist like generative metaphors (Agre 1997). The previous three waves: (1) human factors and ergonomics, (2) symbolic information processing, and (3) networks and mobile computing, consumer technologies, human experience, and user participation have been accompanied by a change in the theoretical basis for understanding interaction from psychology via cognitive science to a wider range of disciplines including philosophy (e.g., phenomenology (Dourish 2001)), social sciences (e.g., situated action (Suchman 2007; 1987)) and design (Winograd 1996; Janlert and Stolterman 2017). Current discussions of the future development of HCI includes human-computer entanglement (Frauenberger 2019) and integration (Farooq and Grudin 2016; Mueller et al. 2020), the computer as a partner (Beaudouin-Lafon and Mackay 2018) and political positioning (Shneiderman et al. 2016; Bødker and Kyng 2018; Bannon et al. 2018). We are particularly interested in discussing the challenges to HCI by a set of relatively new technical possibilities: autonomous technologies.
In an already complex and cluttered environment, the combination of these three waves has further complicated our environment as it becomes populated with physical and virtual systems. Now, more than ever, digital systems and artifacts get more entangled with almost all aspects of societal life, and autonomous (and ubiquitous) technologies add to the number of things we interact with in our daily lives: the digital permeates society. Autonomous technologies appear as self-directed and/or self-sufficient (Bradshaw et al. 2013): they do their things without explicit instructions from a human. Digital artifacts or systems that are “artificially intelligent” (AI) or use “machine learning” (ML) and neural networks to make decisions often operate outside the reach of human interventions; they are imperceptible as we cannot always know what they do or why they do it. In the light of the new kinds of interactions offered by the latest technological advancements, this workshop particularly addresses human autonomy, focusing on the individual humans and their autonomy and self-determination as the overall goal for HCI. How can we, as researchers and interaction designers, understand, evaluate and describe human—technology interactions in an increasingly complex and technologically diverse environment? The aim of this workshop is to address the role that human autonomy presently receives in HCI research, and how we can ensure that this perspective gets proper attention as autonomous technology advances beyond (the average) human understanding.
Full list of references is found here.
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