Fairer and More Accurate Tabular Models Through NAS

Making models algorithmically fairer in tabular data has been long studied, with techniques typically oriented towards fixes which usually take a neural model with an undesirable outcome and make changes to how the data are ingested, what the model weights are, or how outputs are processed. We employ an emergent and different strategy where we consider updating the model's architecture and training hyperparameters to find an entirely new model with better outcomes from the beginning of the debiasing procedure. In this work, we propose using multi-objective Neural Architecture Search (NAS) and Hyperparameter Optimization (HPO) in the first application to the very challenging domain of tabular data. We conduct extensive exploration of architectural and hyperparameter spaces (MLP, ResNet, and FT-Transformer) across diverse datasets, demonstrating the dependence of accuracy and fairness metrics of model predictions on hyperparameter combinations. We show that models optimized solely for accuracy with NAS often fail to inherently address fairness concerns. We propose a novel approach that jointly optimizes architectural and training hyperparameters in a multi-objective constraint of both accuracy and fairness. We produce architectures that consistently Pareto dominate state-of-the-art bias mitigation methods either in fairness, accuracy or both, all of this while being Pareto-optimal over hyperparameters achieved through single-objective (accuracy) optimization runs. This research underscores the promise of automating fairness and accuracy optimization in deep learning models

Performance Bounds for LASSO under Multiplicative Noise: Applications to Pooled RT-PCR Testing

Group testing is a technique which avoids individually testing $n$ samples for a rare disease and instead tests $n < p$ pools, where a pool consists of a mixture of small, equal portions of a subset of the $p$ samples. Group testing saves testing time and resources in many applications, including RT-PCR, with guarantees for the recovery of the status of the $p$ samples from results on $n$ pools. The noise in quantitative RT- PCR is inherently known to follow a multiplicative data-dependent model. In recent literature, the corresponding linear systems for inferring the health status of $p$ samples from results on $n$ pools have been solved using the Lasso estimator and its variants, which have been typically used in additive Gaussian noise settings. There is no existing literature which establishes performance bounds for Lasso for the multiplicative noise model associated with RT-PCR. After noting that a recent general technique, Hunt et al., works for Poisson inverse problems, we adapt it to handle sparse signal reconstruction from compressive measurements with multiplicative noise: we present high probability performance bounds and data-dependent weights for the Lasso and its weighted version. We also show numerical results on simulated pooled RT-PCR data to empirically validate our bounds.Comment: Signal Processing Journal under revie

For What It's Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm

Knowledge and perception regarding clinical trials among doctors of government medical colleges: A questionnaire-based study

Aims: By virtue of being a specialized field by itself, the science of clinical trials (CTs) may not be well understood by doctors who are not specifically trained in it. A lack of knowledge may translate to a negative perception toward CT. With the idea of getting a situational snapshot, we estimated the knowledge and perception of CTs among doctors from government medical colleges of West Bengal who are not trained on CT in their postgraduate curriculum. Several determinants of knowledge and perception regarding CT were also evaluated. Methods: We have quantified the knowledge and perception of CTs by a structured validated questionnaire. Development and validation of the questionnaire was performed prior to the study. Results: Among 133 participants, 7.5% received focused training on CT and 16.5% participated in CTs as investigators. Majority of the doctors were unfamiliar with the basic terminologies such as, “adverse event” and “good clinical practice.” Encouragingly, 93.3% doctors advised that a detailed discussion of CT methodology should be incorporated in the under graduate medical science curriculum. They had an overall positive attitude toward CTs conducted in India, with a mean score that is 72.6% of the maximum positive score. However, a large number of the doctors were skeptical about the primary motivation and operations of pharmaceutical industry sponsored CTs, with 45% of them believing that patients are exploited in these sponsored CTs. Conclusion: Participant doctors had a basic knowledge of CT methodology. The study has revealed specific areas of deficient knowledge, which might be emphasized while designing focused training on CT methodology

For What It's Worth: Humans Overwrite Their Economic Self-interest to Avoid Bargaining With AI Systems

As algorithms are increasingly augmenting and substituting human decision-making, understanding how the introduction of computational agents changes the fundamentals of human behavior becomes vital. This pertains to not only users, but also those parties who face the consequences of an algorithmic decision. In a controlled experiment with 480 participants, we exploit an extended version of two-player ultimatum bargaining where responders choose to bargain with either another human, another human with an AI decision aid or an autonomous AI-system acting on behalf of a passive human proposer. Our results show strong responder preferences against the algorithm, as most responders opt for a human opponent and demand higher compensation to reach a contract with autonomous agents. To map these preferences to economic expectations, we elicit incentivized subject beliefs about their opponent's behavior. The majority of responders maximize their expected value when this is line with approaching the human proposer. In contrast, responders predicting income maximization for the autonomous AI-system overwhelmingly override economic self-interest to avoid the algorithm.Web Information System