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A Prayer App Turns to TikTok to Find Its Flock
A Prayer App Turns to TikTok to Find Its Flock
(Soul was a classic historical causal or origin story. Redemption was newer. Ideologies have their value ranks. May also be a booster to get out the hoosegow. Not to be confused with Halo, except for the legendary Cortana.)
#Apps#Theology#Theism
·nytimes.com·
A Prayer App Turns to TikTok to Find Its Flock
Rosen, D., Oh, Y., Chesebrough, C., Zhang, F. Z., & Kounios, J. (2024). Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians. Neuropsychologia, 108824.
Rosen, D., Oh, Y., Chesebrough, C., Zhang, F. Z., & Kounios, J. (2024). Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians. Neuropsychologia, 108824.
#Creativity#Neuroscience#Brain Science#BCI#Paper#PDF#Behavior
·pdf.sciencedirectassets.com·
Rosen, D., Oh, Y., Chesebrough, C., Zhang, F. Z., & Kounios, J. (2024). Creative flow as optimized processing: Evidence from brain oscillations during jazz improvisations by expert and non-expert musicians. Neuropsychologia, 108824.
Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.
Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.

(Systems like eJury claim to highlight issues for prep. The states that host them may have some controversial legislation that invites reactions. Separately, within organizations, shadow AI is considered to be extraneous information, possibly pernicious although formerly heroic for surviving the Winter.)

#Decision-Making#Research#Legal
·philpapers.org·
Ross, L. Mock Juries, Real Trials: How to Solve (some) Problems with Jury Science.
Pan, C. A., Yakhmi, S., Iyer, T. P., Strasnick, E., Zhang, A. X., & Bernstein, M. S. (2022). Comparing the perceived legitimacy of content moderation processes: Contractors, algorithms, expert panels, and digital juries. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1-31.
Pan, C. A., Yakhmi, S., Iyer, T. P., Strasnick, E., Zhang, A. X., & Bernstein, M. S. (2022). Comparing the perceived legitimacy of content moderation processes: Contractors, algorithms, expert panels, and digital juries. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1-31.
Digital juries.
#Social Media#Moderation#Legitimacy#Paper#PDF#Democracy
·dl.acm.org·
Pan, C. A., Yakhmi, S., Iyer, T. P., Strasnick, E., Zhang, A. X., & Bernstein, M. S. (2022). Comparing the perceived legitimacy of content moderation processes: Contractors, algorithms, expert panels, and digital juries. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1-31.
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).
(How is a juror instructed to eliminate implicit bias? What would be the specifics of a course that changed their minds? This is fairly easy to trigger in practice, e.g. as subtext to invoke irony.)
#Machine Learning#Legal#Bias#Paper#PDF
·dl.acm.org·
Gordon, M. L., Lam, M. S., Park, J. S., Patel, K., Hancock, J., Hashimoto, T., & Bernstein, M. S. (2022, April). Jury learning: Integrating dissenting voices into machine learning models. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (pp. 1-19).