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Communicating and combating algorithmic bias: effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust

Cheng Chen & S. Shyam Sundar

Human-Computer Interaction2024https://doi.org/10.1080/07370024.2024.2392494article
AJG 1ABDC A
Weight
0.62

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https://doi.org/https://doi.org/10.1080/07370024.2024.2392494

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@article{cheng2024,
  title        = {{Communicating and combating algorithmic bias: effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust}},
  author       = {Cheng Chen & S. Shyam Sundar},
  journal      = {Human-Computer Interaction},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1080/07370024.2024.2392494},
}

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Communicating and combating algorithmic bias: effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust

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Evidence weight

0.62

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.73 × 0.4 = 0.29
M · momentum0.72 × 0.15 = 0.11
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.