Understanding and Designing Automation with Peoples' Wellbeing in Mind

Nowadays, automation not only dominates industry but becomes more and more a part of our private, everyday lives. Following the notion of increased convenience and more time for the "important things in life", automation relieves us from many daily household chores - robots vacuum floors and automated coffeemakers produce supposedly barista-quality coffee on the press of a button. In many cases these offers are embraced by people without further questioning. Of course, automation frees us from many unloved activities, but we may also lose something by delegating more and more everyday activities to automation. In a series of four studies, we explored the experiential costs of everyday automation and strategies of how to design technology to reconcile experience with the advantages of ever more powerful automation.Comment: 7 pages, 4 figure

Theoretical, Measured and Subjective Responsibility in Aided Decision Making

When humans interact with intelligent systems, their causal responsibility for outcomes becomes equivocal. We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems. In two laboratory experiments, participants performed a classification task. They were aided by classification systems with different capabilities. We compared the predicted theoretical responsibility values to the actual measured responsibility participants took on and to their subjective rankings of responsibility. The model predictions were strongly correlated with both measured and subjective responsibility. A bias existed only when participants with poor classification capabilities relied less-than-optimally on a system that had superior classification capabilities and assumed higher-than-optimal responsibility. The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the descriptive value of the ResQu model to predict behavior and perceptions of responsibility by considering the characteristics of the human, the intelligent system, the environment and some systematic behavioral biases. The ResQu model is a new quantitative method that can be used in system design and can guide policy and legal decisions regarding human responsibility in events involving intelligent systems

Ordinary user experiences at work: a study of greenhouse growers

We investigate professional greenhouse growers’ user experience (UX) when using climate-management systems in their daily work. We build on the literature on UX, in particular UX at work, and extend it to ordinary UX at work. In a ten-day diary study, we collected data with a general UX instrument (AttrakDiff), a domain-specific instrument, and interviews. We find that AttrakDiff is valid at work; its three-factor structure of pragmatic quality, hedonic identification quality, and hedonic stimulation quality is recognizable in the growers’ responses. In this paper, UX at work is understood as interactions among technology, tasks, structure, and actors. Our data support the recent proposal for the ordinariness of UX at work. We find that during continued use UX at work is middle-of-the-scale, remains largely constant over time, and varies little across use situations. For example, the largest slope of the four AttrakDiff constructs when regressed over the ten days was as small as 0.04. The findings contrast existing assumptions and findings in UX research, which is mainly about extraordinary and positive experiences. In this way, the present study contributes to UX research by calling attention to the mundane, unremarkable, and ordinary user experiences at work