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19 Jun 2024: on their effects on people belonging to certain sensitivedemographic groups (e.g., gender, race). ... Now we compare user judgments of fairness of individual features, fordifferent demographic subgroups of people.

19 Jun 2024: Our Contributions. We collected and analyzed fairness judgmentsfrom a survey of 576 people. ... So our framework for how people judgefeature usage fairness consists of two-parts.

19 Jun 2024: Yet both concepts are somewhat ambiguous, and can meandifferent things to different people in different contexts. ... People might be experts or not. We list several types and goals of transparency.

19 Jun 2024: However, we note that such judgments can begathered from any other group of people, ranging fromcrowd workers to domain experts. ... people) more likelyto be falsely predicted as having a higher risk of recidivismthan another group of people (e.g., white

19 Jun 2024: Let x2 be a sensitive fea-ture, such as age, given by the shape of the point: assume youngand mature people. ... 100% points are accurate (correctly, blue maturepeople are in the blue zone, red mature people are in the red zone).

19 Jun 2024: in terms of thetotal number of people influenced, and the fraction of peopleinfluenced from both A and B communities. ... maximizing the to-tal number of influenced people, while also significantly de-creasing disparity.

19 Jun 2024: 1 Introduction. Many important data-sets involve networks: people belong to social networks, webpages are joined in a linkgraph, and power utilities are connected in a grid.

19 Jun 2024: 2017),and might help to promote greater understanding and cohe-sion among people. ... CNN You mean, like the UNFOUNDED claims of Russiancollusion? You people are typically selective in your bias pro?https://t.co/CESkVpIZOk@nytimes His actions were

19 Jun 2024: SampleSize” denotes how many people received this variant. The next two columns contain the proportionof correct answers when identifying epistemic or aleatoric uncertainty for LSAT, respectively.

19 Jun 2024: That is, max-margin training with approximateinference—which is something people do anyway to learn graphical models—reduces not only theprediction error, but also the inference approximation error.