Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
1University of California Los Angeles; 2Chinese University of Hong Kong; 3University of Cambridge; 4Technical University of Munich
Given the breathless development of AI applications and its discourse led by hegemonic actors, the space for ‘un-blackboxing’- developing a critical technical practice around AI is limited and diminished. The current state of Gen-AI tools as the default and proprietary manifestations of specific technical developments claim to curate and advance user-friendly systems but in the process, conceal decisions of data, procedures, and interface-level design. This extension of a furthermore passive Internet consumer, for whom information will be curated demands a reframing. We propose that in curating information, AI technologies are performing an act of concealment. While the popular discourse on GenAI in different generations has been about what these applications produce – we are more critically invested in what these apps conceal when they curate the information that is designed for consumption.
We are thus offering a provocation in understanding the role and function of emerging AI: Emerging AI introduces a new function of the digital author – Concealment. Concealment of information which has historically been treated as an act of censorship, extraordinary political retraction, sensitive redaction, or strategic information shaping, has now become the default in our GenAI networks. This panel brings together 4 academics, researchers, educators, who draw from 4 very different contexts in the US, UK, Germany, and Hong Kong, to think about Concealment as the new author function of our Gen-AI times. All of us draw from our original research, classroom practice, and engagement with other stakeholders in the field to think through this new authorship as a way of critically examining the information making with Gen-AI.