Dataset Information

Minimax Pareto Fairness: A Multi Objective Perspective.

ABSTRACT: In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches.

SUBMITTER: Martinez N

PROVIDER: S-EPMC7912461 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Minimax Pareto Fairness: A Multi Objective Perspective.

Martinez Natalia N Bertran Martin M Sapiro Guillermo G

Proceedings of machine learning research 20200701

In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method ...[more]

PMID: 33644764

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Project description:In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A1AR and A2AAR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment. Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent. During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.

Project description:Introduction: The aim of the Objective Structured Clinical Examination (OSCE) is a standardized and fair assessment of clinical skills. Observing second clinical year medical students during a summative OSCE assessing a General Practice clerkship, we noticed that information exchange with peers led to a progressively faster and overly focused management of simulations. Therefore, we established a Multiple Scenario-OSCE (MS-OSCE) where all students had to manage the same chief complaint at a station but it's underlying scenarios being randomly changed during students' rotation through their parcours. We wanted to ensure they fully explore differential diagnosis instead of managing their task influenced by shared information. We wanted to assess if a MS-OSCE violates the assumption of objectivity and fairness given that students are not tested with the same scenarios. Methods: We developed and piloted five OSCE stations (chest pain, abdominal pain, back pain, fatigue and acute cough) with two or three different underlying scenarios each. At each station these scenarios randomly changed from student to student. Performance was assessed with a checklist and global rating. The effect of scenarios and raters on students' grades was assessed calculating the intraclass correlation coefficient with a fixed effect two level linear model. Results: A total of 169 students and 23 raters participated in the MS-OSCE. The internal consistency over all stations was 0.65 by Cronbach's alpha. The difference of the mean grades between the scenarios of a given chief complaint ranged from 0.03 to 0.4 on a 1 to 5 grading scale. The effect of scenarios on the variance of the final grades at each station ranged from 4% to 9% and of raters from 20% to 50% when adjusted for students' skills. Conclusions: The effect of different scenarios on the grades was relevant but small compared to the effect of raters on grades. Improving rater training is more important to ensure objectivity and fairness of MS-OSCE than providing the same scenario to all students.

Dataset Information

Minimax Pareto Fairness: A Multi Objective Perspective.

Publications

Minimax Pareto Fairness: A Multi Objective Perspective.

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