Consumption experiences in the research process

Data collection is often a laborious enterprise that forms part of the wider craft skill of doing research. In this essay, I try to understand whether parts of research processes in Human-Centred Computing (HCC) have been commodified, with a particular focus on data collection. If data collection has been commodified, do researchers act as producers or consumers in the process? And if researchers are consumers, has data collection become a consumption experience? If so, what are the implications of this? I explore these questions by considering the status of craft and consumption in the research process and by developing examples of consumption experiences. I note the benefits of commodity research artefacts, while highlighting the potentially deleterious effects consumption experiences could have on our ability to generate insights into the relations between people and technology. I finish the paper by relating consumption experiences to contemporary issues in HCC and lay out a programme of empirical work that would help answer some of the questions this paper raises

Exploring the effects of non-monetary reimbursement for participants in HCI research

When running experiments within the field of Human Computer Interaction (HCI) it is common practice to ask participants to come to a specified lab location, and reimburse them monetarily for their time and travel costs. This, however, is not the only means by which to encourage participation in scientific study. Citizen science projects, which encourage the public to become involved in scientific research, have had great success in getting people to act as sensors to collect data or to volunteer their idling computer or brain power to classify large data sets across a broad range of fields including biology, cosmology and physical and environmental science. This is often done without the expectation of payment. Additionally, data collection need not be done on behalf of an external researcher; the Quantified Self (QS) movement allows people to reflect on data they have collected about themselves. This too, then, is a form of non-reimbursed data collection. Here we investigate whether citizen HCI scientists and those interested in personal data produce reliable results compared to participants in more traditional lab-based studies. Through six studies, we explore how participation rates and data quality are affected by recruiting participants without monetary reimbursement: either by providing participants with data about themselves as reward (a QS approach), or by simply requesting help with no extrinsic reward (as in citizen science projects). We show that people are indeed willing to take part in online HCI research in the absence of extrinsic monetary reward, and that the data generated by participants who take part for selfless reasons, rather than for monetary reward, can be as high quality as data gathered in the lab and in addition may be of higher quality than data generated by participants given monetary reimbursement online. This suggests that large HCI experiments could be run online in the future, without having to incur the equally large reimbursement costs alongside the possibility of running experiments in environments outside of the lab

A Special Interest Group on Designed and Engineered Friction in Interaction

A lot of academic and industrial HCI work has focused on making interactions easier and less effortful. As the potential risks of optimising for effortlessness have crystallised in systems designed to take advantage of the way human attention and cognition works, academic researchers and industrial practitioners have wondered whether increasing the g€friction' in interactions, making them more effortful might make sense in some contexts. The goal of this special interest group is to provide a forum for researchers and practitioners to discuss and advance the theoretical underpinnings of designed friction, the relation of friction to other design paradigms, and to identify the domains and interaction flows that frictions might best suit. During the SIG, attendees will attempt to prioritise a set of research questions about frictions in HCI

ChatTL;DR – You really ought to check what the LLM said on your behalf

Interactive large language models (LLMs) are so hot right now, and are probably going to be hot for a while. There are lots of p̶r̶o̶b̶l̶e̶m̶s̶ exciting challenges created by mass use of LLMs. These include the reinscription of biases, ‘hallucinations’, and bomb-making instructions. Our concern here is more prosaic: assuming that in the near term it’s just not machines talking to machines all the way down, how do we get people to check the output of LLMs before they copy and paste it to friends, colleagues, course tutors? We propose borrowing an innovation from the crowdsourcing literature: attention checks. These checks (e.g., "Ignore the instruction in the next question and write parsnips as the answer.") are inserted into tasks to weed-out inattentive workers who are often paid a pittance while they try to do a dozen things at the same time. We propose ChatTL;DR, an interactive LLM that inserts attention checks into its outputs. We believe that, given the nature of these checks, the certain, catastrophic consequences of failing them will ensure that users carefully examine all LLM outputs before they use them