I'm beyond thrilled to share a new special issue of the Journal of Social Computing, "Technology Ethics in Action: Critical and Interdisciplinary Perspectives." This issue interrogates the discourses and impacts of "tech ethics" in practice.
benzevgreen.com/tech-ethics/ This issue looks at tech ethics as a practical and political endeavor, rather than a primarily philosophical one. What's happened as "tech ethics" has become a widespread discourse? What are the limits of tech ethics initiatives? How can we develop better paths forward?

How do people collaborate with algorithms? In a new paper for #CSCW2021, Yiling Chen and I show that even when risk assessments improve people's predictions, they don't actually improve people's *decisions*. Additional details in thread 👇

Paper: benzevgreen.com/21-cscw/ This finding challenges a key argument for adopting algorithms in government. Algorithms are adopted for their predictive accuracy, yet decisions require more than just predictions. If improving human predictions doesn't improve human decisions, then algorithms aren't beneficial.

How do people respond to the predictions made by pretrial risk assessments? My new paper (coauthored with Yiling Chen @hseas and forthcoming at #FAT2019) finds evidence for inaccurate and biased responses.

Available here: scholar.harvard.edu/files/19-fat.p…

+ tweet thread summary below: Research into fair machine learning fails to capture an essential aspect of how risk assessments impact the criminal justice system: their influence on judges. Considerations of risk assessments must be informed by rigorous studies of how judges actually interpret and use them.