Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms.
https://dl.acm.org/doi/10.1145/3706598.3713279
Animating objects’ movements is widely used to facilitate tracking changes and observing both the global trend and local hotspots where objects converge or diverge. Existing methods, however, often obscure critical local hotspots by only considering the start and end positions of objects' trajectories. To address this gap, we propose RouteFlow, a trajectory-aware animated transition method that effectively balances the global trend and local hotspots while minimizing occlusion. RouteFlow is inspired by a real-world bus route analogy: objects are regarded as passengers traveling together, with local hotspots representing bus stops where these passengers get on and off. Based on this analogy, animation paths are generated like bus routes, with the object layout generated similarly to seat allocation according to their destinations. Compared with state-of-the-art methods, RouteFlow better facilitates identifying the global trend and locating local hotspots while performing comparably in tracking objects' movements.
https://dl.acm.org/doi/10.1145/3706598.3714300
Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' self-confidence and its calibration. To explore these dynamics, we conducted a randomized behavioral experiment. Our results indicate that in human-AI decision-making, users' self-confidence aligns with AI confidence and such alignment can persist even after AI ceases to be involved. This alignment then affects users' self-confidence calibration. We also found the presence of real-time correctness feedback of decisions reduced the degree of alignment. These findings suggest that users' self-confidence is not independent of AI confidence, which practitioners aiming to achieve better human-AI collaboration need to be aware of. We call for research focusing on the alignment of human cognition and behavior with AI.
https://dl.acm.org/doi/10.1145/3706598.3713336
Algorithmic fairness research often disregards concerns related to systemic injustice. We study how contextualizing algorithms within systemic injustice impacts lay perceptions of algorithmic discrimination. Using the hiring domain as a case-study, we conduct a 2x3 between-participants experiment (N=716), studying how people's views of algorithmic fairness are influenced by information about (i) systemic injustice in historical hiring decisions and (ii) algorithms' propensity to perpetuate biases learned from past human decisions. We find that shedding light on systemic injustice has heterogeneous effects: participants from historically advantaged groups became more negative about discriminatory algorithms, while those from disadvantaged groups reported more positive attitudes. Explaining that algorithms learn from past human decisions had null effects on people's views, adding nuances to calls for improving public understanding of algorithms. Our findings reveal that contextualizing algorithms in systemic injustice can have unintended consequences and show how different ways of framing existing inequalities influence perceptions of injustice.
https://dl.acm.org/doi/10.1145/3706598.3713536
There are inevitably delays between user actions and system responses, which can increase task completion times. However, it remains unclear whether this is solely due to waiting times and compensation strategies, or whether users further slow down their actions because these delays become integrated into their cognitive action structures, as suggested by cognitive psychological theories. To explore this, we examined the effects of repeated exposure to delays during point-and-click tasks. Our findings demonstrate that longer system response delays significantly slow down users' actions, even before they experience the delayed feedback from the current input. This suggests that the user's cognitive system anticipates delays based on previous interactions and adjusts actions accordingly. These results emphasize the importance of minimizing systematic delays to maintain optimal user performance and highlight the potential for system properties to become embedded in users' cognitive action structures.
https://dl.acm.org/doi/10.1145/3706598.3713475
We present an exploratory study on how people perceive visualizations of spatial social networks generated by edge bundling algorithms. Although these algorithms successfully minimize clutter in node-link diagrams, they do so through various methods that can sometimes create false connections between nodes. We conducted a qualitative experiment involving participants with technical expertise but no prior knowledge of edge bundling algorithms. Participants described their perceptions of both bundled and straight-line visualizations in open-ended tasks. Analysis of their annotations and transcripts revealed a general preference for bundled visualizations. However, when it came to false connections, participants tended to follow them in tightly bundled diagrams while also vocalizing that these drawings were more ambiguous. The routing of bundles influenced the perception of clusters and participants assigned more or fewer nodes to the clusters, depending on the routing of bundles. Participants' unfamiliarity with the dataset led them to use analogies to describe the bundled drawings, potentially adding perceived semantic meaning to the data.
https://dl.acm.org/doi/10.1145/3706598.3713444