Unfair Utilities and First Steps Towards Improving Them

Many fairness criteria constrain the policy or choice of predictors. In this work, we propose a different framework for thinking about fairness: Instead of constraining the policy or choice of predictors, we consider which utility a policy is optimizing for. We define value of information fairness and propose to not use utilities that do not satisfy this criterion. We describe how to modify a utility to satisfy this fairness criterion and discuss the consequences this might have on the corresponding optimal policies.Comment: 20 page

Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

Uncertainty quantification in image retrieval is crucial for downstream decisions, yet it remains a challenging and largely unexplored problem. Current methods for estimating uncertainties are poorly calibrated, computationally expensive, or based on heuristics. We present a new method that views image embeddings as stochastic features rather than deterministic features. Our two main contributions are (1) a likelihood that matches the triplet constraint and that evaluates the probability of an anchor being closer to a positive than a negative; and (2) a prior over the feature space that justifies the conventional l2 normalization. To ensure computational efficiency, we derive a variational approximation of the posterior, called the Bayesian triplet loss, that produces state-of-the-art uncertainty estimates and matches the predictive performance of current state-of-the-art methods

Monitoring Public Behavior During a Pandemic Using Surveys: Proof-of-Concept Via Epidemic Modelling

Implementing a lockdown for disease mitigation is a balancing act: Non-pharmaceutical interventions can reduce disease transmission significantly, but interventions also have considerable societal costs. Therefore, decision-makers need near real-time information to calibrate the level of restrictions. We fielded daily surveys in Denmark during the second wave of the COVID-19 pandemic to monitor public response to the announced lockdown. A key question asked respondents to state their number of close contacts within the past 24 hours. Here, we establish a link between survey data, mobility data, and, hospitalizations via epidemic modeling. Using Bayesian analysis, we then evaluate the usefulness of survey responses as a tool to monitor the effects of lockdown and then compare the predictive performance to that of mobility data. We find that, unlike mobility, self-reported contacts track the immediate behavioral response after the lockdown's announcement, weeks before the lockdown's national implementation. The survey data agree with the inferred effective reproduction number and their addition to the model results in greater improvement of predictive performance than mobility data. A detailed analysis of contact types indicates that disease transmission is driven by friends and strangers, whereas contacts to colleagues and family members (outside the household) only played a minor role despite Christmas holidays. Our work shows that an announcement of non-pharmaceutical interventions can lead to immediate behavioral responses, weeks before the actual implementation. Specifically, we find that self-reported contacts capture this early signal and thus qualify as a reliable, non-privacy invasive monitoring tool to track the implementation of non-pharmaceutical interventions