For instance, one such notion is'unawareness', which necessitates Additionally, preference-based fairness argues that an algorithm's design should not be solely determined by its creators or regulators but should also incorporate the preferences of those directly A myriad of techniques exist to construct fair models using counterfactual inference. Theorem 2. Assume that R has been generated using Algorithm 2. We have, Pr(R We consider a causal graph shown in Figure 6. The counterfactual data ˇ X were computed by substituting A in the structural function with ˇ A . We implemented our method and the baseline methods as described in Section 5 (since there is no difference between observed data and factual data in this scenario, we have no ICA baseline here). For the CR method, we set the weight of the fairness regularization term as 0.05.

Algorithmic indistinguishability yields a natural test for assessing whether experts incorporate this kind of "side

Data-driven decision making relies on estimating the effect of interventions, i.e. causal effect estimation . For example, a doctor must decide which medicine she will give her patient, ideally the one with the greatest effect on positive outcomes.

Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing.

Off-policy estimation: Oftentimes, one needs to estimate the linear functional based on observational data collected from a behavior policy.

Recently, an insightful phenomenon known as neural collapse (NC) [Papyan et al., 2020] has been

The principle of multi-task learning, which encompasses concepts such as "learning to learn" and "knowledge transfer," offers an efficient paradigm that mirrors human intelligence.

Current approaches to mitigate these representational harms balance the number of retrieved items across population groups defined by a small number of (often binary) attributes. However, most existing methods overlook intersectional groups determined by combinations of group attributes, such as gender, race, and ethnicity.

Evaluating machine learning models requires evaluation data.