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  2. Blind Justice: Fairness with Encrypted Sensitive Attributes

    https://mlg.eng.cam.ac.uk/adrian/ICML18-BlindJustice.pdf
    19 Jun 2024: 2.2. Fairness Criteria. In large part, works that formalize fairness in machine learn-ing do so by balancing a certain condition between groupsof people with different sensitive attributes, z versus ... Figure 3. The fraction of people with z = 0
  3. One-network Adversarial Fairness

    https://mlg.eng.cam.ac.uk/adrian/AAAI2019_OneNetworkAdversarialFairness.pdf
    19 Jun 2024: Here, following earlierwork, unfairness means discriminating against a particulargroup of people due to sensitive group characteristics suchas gender or race (Grgic-Hlaca et al. ... For the Adult data, a random guessing classi-fier would result in 75.9%
  4. A Unified Approach to Quantifying Algorithmic Unfairness: Measuring…

    https://mlg.eng.cam.ac.uk/adrian/KDD2018_inequality_indices.pdf
    19 Jun 2024: We furtherillustrate this point with two examples: First, by this definition amodel that assigns the same outcome to everyone is considered fair,regardless of people’s merit for different outcomes ... This offersa framework to interpolate between group
  5. Working Draft 1 Accountability of AI Under the Law: ...

    https://mlg.eng.cam.ac.uk/adrian/SSRN-id3064761-Dec19.pdf
    19 Jun 2024: for most people.8 Moreover, decisions about how to define objective functions and what training data to use can introduce human error into AI decision making.9 Thus, there exist legitimate
  6. Methods for Inference in Graphical Models

    https://mlg.eng.cam.ac.uk/adrian/phd_FINAL.pdf
    19 Jun 2024: I’ve had the privilege to collaborate with wonderful people. Many thanks to Tony, Dan Ellis,.

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