The paper explores fairness metrics in automated decision-making and their limitations.
problem Discrimination in automated resource allocation decisions.
method Analysis of fairness metrics and distributive justice principles.
result Prominent fairness metrics fail to address egalitarian and sufficiency concerns in resource allocation.
Deep neural networks (DNNs) may outperform human brains in complex tasks, but the lack of transparency in their decision-making processes makes us question whether we could fully trust DNNs with high stakes problems. As DNNs' operations rely on a massive number of both parallel and sequential linear/nonlinear computati…
TRACER improves interpretability in high stakes analytics.
problem Interpreting complex models in high stakes applications.
method TRACER framework with TITV model for healthcare analytics.
result TRACER facilitates both accurate and interpretable analytics.
Conformal Alignment ensures trustworthy outputs from foundation models.
problem Ensuring outputs from foundation models align with human values in high-stakes tasks.
method A framework that trains an alignment predictor using reference data to select trustworthy outputs.
result Conformal Alignment accurately identifies trustworthy outputs via lightweight training over moderate reference data.
Neural Additive Models combine neural nets with interpretable models for high stakes tasks.
problem Inability to understand how neural networks make decisions.
method Combines neural nets with generalized additive models to create Neural Additive Models (NAMs).
result NAMs are more accurate than intelligible models and as accurate as state-of-the-art generalized additive models.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying t…
This paper introduces a new task to better understand Transformers in quantitative contexts.
problem Understanding Transformers in high-stakes quantitative and scientific applications.
method Introduces a novel contextual counting task and analyzes it with causal and non-causal Transformer architectures.
result Causal attention is better suited for the contextual counting task, and no positional embeddings lead to the best accuracy.
New framework assesses extreme errors in machine learning models.
problem Current validation methods fail to quantify extreme errors in high-stakes domains.
method Uses Extreme Value Theory (EVT) to estimate worst-case failures.
result Establishes EVT as a fundamental tool for assessing model reliability.
Study improves conformal prediction for EEG classification in healthcare, enhancing coverage.
problem Uncertainty quantification in clinical predictions, especially in distribution-shifted settings.
method Personalized calibration strategies to improve coverage of prediction sets.
result Coverage improved by over 20 percentage points with comparable prediction set sizes.
Graph Neural Networks (GNNs) have proven to be successful in many classification tasks, outperforming previous state-of-the-art methods in terms of accuracy. However, accuracy alone is not enough for high-stakes decision making. Decision makers want to know the likelihood that a specific GNN prediction is correct. For …
FINCH dataset enables financial Text-to-SQL tasks, improving model evaluation.
problem Lack of a large-scale financial dataset for Text-to-SQL tasks.
method Curated financial dataset (FINCH), benchmarking reasoning and language models.
result Proposed FINCH Score for more accurate financial model evaluation.
Fair active learning selects data points to balance model accuracy and fairness.
problem Ensuring fairness in machine learning models used in high-stakes applications.
method Designing algorithms for fair active learning that select data points to balance model accuracy and fairness, focusing on demographic parity.
result Demonstrated the effectiveness of the proposed fair active learning approach over benchmark datasets.
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the potential allocation harms that can result from semantic representation bias. To do so, we study the impact on occupation classification of…
Bayesian graph contrastive learning improves uncertainty quantification for graph analytics.
problem Uncertainty quantification for node representations in graph contrastive learning.
method Proposes a Bayesian framework to learn stochastic encoders representing nodes as distributions, providing uncertainty estimates.
result Significant improvement in performance on benchmark datasets compared to existing methods.
In high-stakes machine learning applications, it is crucial to not only perform well on average, but also when restricted to difficult examples. To address this, we consider the problem of training models in a risk-averse manner. We propose an adaptive sampling algorithm for stochastically optimizing the Conditional Va…
Perry uses auxiliary data to estimate RL policy values with confidence intervals.
problem Leveraging auxiliary datasets for off-policy evaluation with uncertainty quantification.
method Two methods for constructing valid confidence intervals for OPE with data augmentation.
result Methods consistently produce confidence intervals covering ground truth policy values.
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations…
Framework for assessing fairness across similar predictive models.
problem Fairness in predictive models across different groups.
method Develops a framework for characterizing fairness over the set of good models under selective labels.
result Framework can replace or audit models for better fairness properties.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
LSCI provides locally adaptive prediction sets for operator models with tighter coverage.
problem Generating robust, calibrated uncertainty quantification for operator models.
method Local Sliced Conformal Inference (LSCI) for operator models.
result LSCI yields tighter prediction sets with stronger adaptivity compared to conformal baselines.
Using machine learning in high-stakes applications often requires predictions to be accompanied by explanations comprehensible to the domain user, who has ultimate responsibility for decisions and outcomes. Recently, a new framework for providing explanations, called TED, has been proposed to provide meaningful explana…
New framework controls statistical dispersion for high-stakes applications.
problem Understanding and controlling the dispersion of loss distributions in high-stakes applications.
method Simple yet flexible framework for distribution-free control of statistical dispersion measures.
result Proposed methods control statistical dispersion measures with societal implications.
A new method for fair classification using characteristic function distance.
problem Fairness in high-stakes decision-making with sensitive groups.
method Proposes a novel approach based on characteristic function distance to ensure minimal sensitive information in learned representations.
result Consistently matches or achieves better fairness and predictive accuracy than existing methods.
Enhanced anomaly detection using PRC-RF with autoencoders.
problem Extreme class imbalance and curse of dimensionality in anomaly detection.
method Hybrid framework combining PRC-RF and autoencoders.
result Autoencoder-PRC-RF model outperforms previous methods in accuracy, scalability, and interpretability.
HySRL improves RL sample efficiency with shifted-dynamics data.
problem Leveraging historical data with shifted dynamics to improve sample efficiency in RL.
method HySRL, a hybrid transfer RL algorithm that uses prior information on dynamics shift to achieve better sample complexity.
result HySRL achieves problem-dependent sample complexity and outperforms pure online RL.
Paper proposes SCQ and P-TAMS for structured OOD testing in high-stakes ML.
problem Difficulty of incorporating auxiliary information in traditional conformal methods.
method Structure-adaptive conformal q-value (SCQ) and pseudo-score-guided transductive automated model selection (P-TAMS).
result Unified framework controls false discovery rate and improves power across diverse settings.
Study proposes a statistical test for Vision Transformer's attention mechanisms.
problem ViT's attention mechanisms may focus on irrelevant regions, leading to unreliable evidence.
method Selective inference framework to quantify statistical significance of attentions as p-values.
result Proposed method enables reliable quantification of false positive detection probability of attentions.
SConU improves uncertainty prediction for large models by testing for outliers and reducing miscoverage.
problem Real-world deployment of large language models requires reliable guarantees of task-specific metrics.
method SConU implements significance tests to identify and exclude outliers that violate exchangeability assumptions.
result SConU reduces miscoverage rates and enhances prediction efficiency in high-stakes tasks.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
problem Learning optimal decision trees for high-stakes predictive and prescriptive tasks.
method Mixed-integer optimization framework and object-oriented design.
result Implementation of optimal decision trees for various tasks.
Amazon SageMaker Model Monitor detects drift in deployed ML models.
problem Ensuring high performance of ML models in production environments.
method Automatically detects data, concept, bias, and feature attribution drift in real-time.
result Maintains high quality models by providing alerts and corrective actions.
Bayesian autoencoders quantify anomaly uncertainty for safer machine learning.
problem Lack of uncertainty quantification in autoencoders for anomaly detection.
method Formulated Bayesian autoencoders to quantify epistemic and aleatoric anomalies.
result Demonstrated effectiveness of BAEs on benchmark and real datasets.
Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as r…
Systematic review of conformal inference for treatment effect estimation.
problem Uncertainty quantification in treatment effect estimation.
method Conformal prediction methods for treatment effect estimation.
result Current state-of-the-art conformal prediction methods identified and described.
LLMs can collude in market divisions, maximizing profits.
problem Strategic collusion of LLM agents in multi-commodity markets.
method Examined LLMs in Cournot competition frameworks, analyzing pricing and resource allocation strategies.
result LLMs can monopolize specific commodities without direct human input or explicit collusion commands.
Improves generative models for cost-sensitive decisions.
problem Generative models lack awareness of decision costs.
method Integrates a decision loss into the training objective.
result Improves cost-sensitive forecast accuracy.
Extends Shifts dataset for MS lesion segmentation and marine vessel power estimation.
problem Distributional shift in training and deployment data for ML models.
method Develops new datasets for high-risk industrial applications.
result Demonstrates robustness and uncertainty estimation in new industrial tasks.
New GP-based method improves uncertainty quantification for causal functions.
problem Challenges in quantifying uncertainty for causal effects, especially for entire functions.
method GP-based approach using inner-product of observational functions in RKHS, with tractable posterior moments and calibration.
result Improves uncertainty quantification while maintaining causal effect estimation performance.
Bayesian data selection framework ensures fairness in machine learning models.
problem High computational costs and limited scalability of fairness-aware methods.
method Bayesian data selection framework using generalized discrepancy measures.
result Consistently outperforms existing methods in fairness and accuracy.
AI systems are being deployed to support human decision making in high-stakes domains. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI's inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including …
This paper tackles data-efficient CEE with scarce labelled data, proposing a method to progressively reduce generalization risk.
problem Data scarcity in CEE tasks, especially in high-stake domains like medical treatment effect prediction.
method Develops a principled label acquisition pipeline (MACAL) for CEE tasks, focusing on reducing generalization risk progressively.
result Proposes Model Agnostic Causal Active Learning (MACAL) algorithm for batch-wise label acquisition.
Shapley values explain financial language models, aligning with domain knowledge.
problem Lack of explainability in financial applications of large language models.
method Shapley value analysis for financial textual data.
result Shapley values provide consistent explanations with financial reasoning.
Where machine-learned predictive risk scores inform high-stakes decisions, such as bail and sentencing in criminal justice, fairness has been a serious concern. Recent work has characterized the disparate impact that such risk scores can have when used for a binary classification task. This may not account, however, fo…
COMMOD debiases models with minimal and interpretable changes.
problem Inconsistent and costly model updates in fair machine learning.
method Introduced COMMOD, a novel algorithm for algorithmic fairness that minimizes changes and makes them interpretable.
result COMMOD achieves comparable performance to state-of-the-art debiasing methods while making minimal and interpretable changes.
This work uses conformal prediction to quantify uncertainty in large language models for multiple-choice questions.
problem Ensuring robustness and reliability of large language models in high-stakes applications.
method Conformal prediction applied to multi-choice question answering.
result Uncertainty estimates from conformal prediction are closely related to prediction accuracy.
A new method calibrates scientific models by adding randomness to their predictions.
problem Current scientific foundation models lack calibrated uncertainty.
method Stochastic Attention, which randomizes attention weights using multinomial samples.
result Stochastic Attention achieves the strongest native calibration and sharpest prediction intervals.
Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportation, healthcare, and industrial automation. We address the opportunity to maximize the utility of an overall computing system by employing re…
A statistical test controls false positives in anomaly localization using diffusion models.
problem Uncertainty and bias in generative models for anomaly localization.
method Selective inference to quantify significance and control false positives.
result The method effectively controls false positive detection rates.