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pro vyhledávání: '"Harrison, Lane"'
New tactile interfaces such as swell form printing or refreshable tactile displays promise to allow visually impaired people to analyze data. However, it is possible that design guidelines and familiar encodings derived from experiments on the visual
Externí odkaz:
http://arxiv.org/abs/2410.08438
Visualization literacy is an essential skill for accurately interpreting data to inform critical decisions. Consequently, it is vital to understand the evolution of this ability and devise targeted interventions to enhance it, requiring concise and r
Externí odkaz:
http://arxiv.org/abs/2308.14147
Publikováno v:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 1685-1698)
For applications where multiple stakeholders provide recommendations, a fair consensus ranking must not only ensure that the preferences of rankers are well represented, but must also mitigate disadvantages among socio-demographic groups in the final
Externí odkaz:
http://arxiv.org/abs/2308.06233
Choropleth maps have been studied and extended in many ways to counteract the many biases that can occur when using them. Two recent techniques, Surprise metrics and Value Suppressing Uncertainty Palettes (VSUPs), offer promising solutions but have y
Externí odkaz:
http://arxiv.org/abs/2307.15138
Autor:
Davis, Russell, Pu, Xiaoying, Ding, Yiren, Hall, Brian D., Bonilla, Karen, Feng, Mi, Kay, Matthew, Harrison, Lane
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics 2022
Graphical perception studies typically measure visualization encoding effectiveness using the error of an "average observer", leading to canonical rankings of encodings for numerical attributes: e.g., position > area > angle > volume. Yet different p
Externí odkaz:
http://arxiv.org/abs/2212.10533
Combining the preferences of many rankers into one single consensus ranking is critical for consequential applications from hiring and admissions to lending. While group fairness has been extensively studied for classification, group fairness in rank
Externí odkaz:
http://arxiv.org/abs/2207.10020
Publikováno v:
IEEE Transactions on Visualization and Computer Graphics 2023
Visualizations today are used across a wide range of languages and cultures. Yet the extent to which language impacts how we reason about data and visualizations remains unclear. In this paper, we explore the intersection of visualization and languag
Externí odkaz:
http://arxiv.org/abs/2207.09608
Autor:
Birchfield, Ryan, Caten, Maddison, Cheng, Errica, Kelly, Madyson, Larson, Truman, Pham, Hoan Phan, Ding, Yiren, Rakotondravony, Noëlle, Harrison, Lane
Publikováno v:
Proceedings of IEEE Visualization conference 2023
Graphical perception studies are a key element of visualization research, forming the basis of design recommendations and contributing to our understanding of how people make sense of visualizations. However, graphical perception studies typically in
Externí odkaz:
http://arxiv.org/abs/2207.09534
Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generatin
Externí odkaz:
http://arxiv.org/abs/2207.07765
Autor:
Tlachac, M.L., Reisch, Miranda, Lewis, Brittany, Flores, Ricardo, Harrison, Lane, Rundensteiner, Elke
Publikováno v:
In Healthcare Analytics November 2022 2
