Study evaluates gender bias in relation extraction systems.
problem Gender bias in relation extraction systems.
method Created WikiGenderBias dataset, evaluated systems for bias, analyzed bias mitigation techniques.
result NRE systems exhibit gender bias in predictions.
Financial LLMs need explicit bias consideration to avoid invalid results.
problem Finance-specific biases inflate performance and contaminate backtests.
method Identified five recurring biases and proposed a Structural Validity Framework.
result Explicit bias consideration is necessary for valid deployment claims.
Study finds gender bias in human evaluators and shows how machine learning can mitigate it.
problem Gender bias in human decision-making on micro-lending platforms.
method Structural econometric model and machine learning algorithms trained on real-world data.
result Machine learning algorithms can mitigate both preference-based and belief-based biases.
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…
Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical…
Study improves unbiased recommender learning by addressing missing-reward bias.
problem Data bias caused by missing-reward observations in recommender systems.
method Proposes a novel estimator using propensity scores to mitigate both position and reward bias.
result The proposed estimator outperforms other methods, even with increased reward observation bias.
The paper proposes a method to evaluate ML models for subjective inference, focusing on sentence toxicity.
problem Bias in ML models for subjective inference, especially in real-life applications.
method Proposes a list of specifications to evaluate ML models for subjective inference, illustrated with a sentence toxicity example.
result Demonstrates the importance of considering subjectivity and bias in evaluating ML models.
The paper tackles sampling bias in credit scoring models and proposes methods to improve their training and evaluation.
problem Sampling bias in credit scoring models leads to an incomplete representation of the borrower population.
method Bias-aware self-learning framework and Bayesian evaluation method to correct for bias.
result Bayesian evaluation outperforms standard accuracy measures in predicting future performance.
Corrects bias in LLM-as-a-judge evaluations using adaptive calibration.
problem Bias in LLM evaluations due to imperfect sensitivity and specificity.
method Plug-in framework with confidence intervals accounting for test and calibration dataset uncertainties.
result LML-based evaluation yields more reliable estimates than human-only evaluation.
Paper reduces variance in infinite horizon off-policy evaluation with bias reduction.
problem High variance in infinite horizon off-policy evaluation.
method Doubly robust augmentation of Liu et al. (2018a) method using learned value function.
result Significant reduction in bias with higher accuracy when either density ratio or value function is accurate.
We model and correct bias in sequential evaluation, improving ranking accuracy.
problem Sequential evaluation bias in online, irrevocable scoring.
method Modeling the rating process, posing as statistical inference, proposing an online algorithm.
result Near-linear time, online algorithm with guarantees in ranking metrics, information theoretically optimal.
This paper evaluates debiasing methods on word embeddings to reduce religious bias.
problem Social biases persist in word embeddings, potentially amplifying them in AI applications.
method Investigates and evaluates three multiclass debiasing techniques on three word embeddings.
result ConceptorDebiasing is the most effective method, reducing religious bias by 82-96%.
Look-Ahead-Bench evaluates financial LLMs for lookahead bias, revealing significant differences in model performance.
problem Measuring and mitigating lookahead bias in financial LLMs.
method Standardized benchmark evaluating model behavior in practical financial scenarios, analyzing performance decay across market regimes.
result Standard LLMs exhibit significant lookahead bias, while Pitinf models show improved generalization and reasoning abilities.
The study addresses biases in evaluating molecular optimization methods and proposes methods to reduce these biases.
problem Biases in in silico evaluation of molecular optimization methods.
method Discussion and empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
result Empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
Study evaluates bias mitigation methods in deep learning, finds they often exploit hidden biases.
problem Deep learning systems learn biases, affecting performance on minority groups.
method Improved evaluation protocol, new dataset, robustness across different tuning distributions.
result Bias mitigation methods often exploit hidden biases, are not robust to multiple forms of bias, and are sensitive to tuning set choice.
MultiwayPAM clusters LLM-as-a-Judge scores to reveal evaluator bias.
problem Revealing bias in LLM evaluations of text quality.
method Tensor clustering with MultiwayPAM method.
result Medoids reveal cluster membership of questions, answerers, and evaluators.
Study examines whether biased training data lead to biased algorithms.
problem Tackles the issue of bias in algorithmic decision-making.
method Examines a setting where biased human decision-makers affect training data and labels.
result Clarifies conditions for bias reversal in algorithmic decision rules.
Paper analyzes ECE bias and provides bounds for its estimation.
problem Understanding the estimation bias in ECE for machine learning models.
method Information-theoretic approach to analyze bias in uniform mass and uniform width binning strategies.
result Established upper bounds on ECE estimation bias and optimal number of bins.
Modeling bias in evaluation processes using optimization.
problem Bias in evaluation processes based on socially-salient attributes.
method Optimization-based model with two parameters: resource-information trade-off and risk-averseness.
result Characterization of distributions and effect of parameters on observed distributions.
Adaptive TD learning reduces bias in policy evaluation by switching between TD and MC methods.
problem Achieving accurate policy evaluation with Temporal Difference (TD) learning in the presence of state-specific uncertainty.
method Adaptive switching between TD and Monte Carlo (MC) methods, using learned confidence intervals to detect and mitigate bias.
result The proposed adaptive algorithm outperforms existing methods in policy evaluation tasks.
Develops a method to quantify racial bias in law enforcement systems.
problem Quantify racial bias in law enforcement systems considering criminality and multi-stage interactions.
method Multi-stage causal framework incorporating criminality.
result Identifies three canonical scenarios of racial bias in law enforcement.
Recommendation systems have been integrated into the majority of large online systems. They tailor those systems to individual users by filtering and ranking information according to user profiles. This adaptation process influences the way users interact with the system and, as a consequence, increases the difficulty …
The paper evaluates biased methods for alpha-divergence minimization.
problem The impact of bias on solutions found for alpha-divergence minimization.
method Empirical evaluation of biased methods for alpha-divergence minimization, focusing on bias effects and dimensionality.
result Solutions are biased towards KL-divergence minimizers and require impractical computation in high dimensions to minimize alpha-divergence.
New estimator reduces bias-variance tradeoff in Markovian interference experiments.
problem Estimating impact of interventions in systems with limited resources.
method Differences-In-Q (DQ) estimator for on-policy policy evaluation.
result DQ estimator has exponentially smaller variance than off-policy methods.
New methods correct bias in LLM-as-a-Judge evaluations, but reliability depends on judge quality and model calibration.
problem Systematic bias in LLM-as-a-Judge evaluations using naive estimators.
method Analytical results, simulations, and real-data case study to diagnose reliability of corrected estimates.
result Corrected estimates, especially shared-calibration comparisons, can be unreliable under certain conditions.
New framework for interpreting disaggregated fairness evaluations using causal models.
problem Misinterpretation of disaggregated fairness evaluations due to data representativeness and selection bias.
method Causal graphical models to characterize fairness properties and metric stability under different data generating processes.
result Disaggregated evaluations are unreliable without explicit assumptions regarding bias mechanisms.
Corrects bias in learned generative models using likelihood-free importance weighting.
problem Bias in learned generative models relative to true data distribution.
method Estimate likelihood ratio using a classifier, apply importance weighting.
result Consistently improves goodness-of-fit metrics for deep generative models.
New method reduces bias in estimating causal effects from discretized variables.
problem Bias in estimating causal effects from discretized continuous variables.
method Proposes a bias-reduced functional that evaluates outcome regression at within-bin conditional means.
result Demonstrates substantial bias reduction and near-nominal confidence interval coverage.
Over the last decade there has been increasing concern about the biases embodied in traditional evaluation methods for Natural Language Processing/Learning, particularly methods borrowed from Information Retrieval. Without knowledge of the Bias and Prevalence of the contingency being tested, or equivalently the expecta…
New method to evaluate visual explanations from neural networks.
problem Lack of consensus on measuring effectiveness of visual explanations.
method Proposed a new procedure for evaluating explanations using a range of sources.
result Demonstrated the benefit of combining different sources and the impact of bias parameters.
Study evaluates if LLMs have company-specific biases in financial sentiment analysis.
problem Evaluating if large language models exhibit company-specific biases in financial sentiment analysis.
method Comparing sentiment scores with and without company names, constructing economic models, and empirical analysis.
result LLMs show company-specific biases in sentiment analysis, impacting investor behavior and stock prices.
New causal models perform poorly when evaluated on biased training sets.
problem Sample selection bias affects the evaluation of causal models' prediction performance.
method Re-evaluated prediction performance of causal models on a genetic perturbation data set, proposing a less-biased evaluation set.
result Causal models have similar or worse performance when evaluated on a less-biased set compared to standard association-based estimators.
FinReflectKG - EvalBench benchmarks financial KG extraction from SEC 10-K filings.
problem Lack of universal benchmark and evaluation framework for financial KG construction.
method Agentic and holistic evaluation principles, deterministic commit-then-justify judging protocol, binary and ordinal evaluations.
result Reflection-based extraction outperforms single-pass extraction in comprehensiveness, precision, and relevance.
We consider the off-policy evaluation problem in Markov decision processes with function approximation. We propose a generalization of the recently introduced \emph{emphatic temporal differences} (ETD) algorithm \citep{SuttonMW15}, which encompasses the original ETD(λ), as well as several other off-policy evaluation …
Mitigates bias in evaluations by considering known outcome information.
problem Bias in evaluations due to external factors like grades or acceptance.
method Formulates bias as a partial ordering, solves regularized optimization problem.
result The debiasing method improves evaluation accuracy by adapting regularization.
This paper examines fairness and arbitrariness in bias mitigation methods.
problem Understanding how different bias mitigation strategies affect individual predictions and whether they introduce arbitrariness.
method FRAME framework to evaluate bias mitigation through five dimensions: Impact Size, Change Direction, Decision Rates, Affected Subpopulations, and Neglected Subpopulations.
result Significant differences in the behaviors of debiasing methods were exhibited, highlighting the limitations of current fairness criteria and the inherent arbitrariness in the debiasing process.
PAC-MCTS addresses biased search in LLM-guided planning by dynamically pruning.
problem Systematic biases in LLMs lead to inefficient and unsafe search in deep planning tasks.
method Formulates node expansion as BAI under bounded bias, derives sample complexity bounds, and proposes PAC-MCTS for dynamic confidence bounds.
result PAC-MCTS improves robustness and efficiency by up to 78% fewer API evaluations and 3x higher sample efficiency.
New method for evaluating LLMs reduces bias in open-ended evaluations.
problem Bias in Elo-based ratings of LLMs due to data redundancies.
method Proposes evaluation as a 3-player game and introduces novel solution concepts.
result Novel method leads to more robust and intuitive ratings.
New Hessian estimators for Riemannian manifolds with reduced bias.
problem Estimating Hessians on Riemannian manifolds with reduced bias and computational efficiency.
method Introducing new stochastic zeroth-order Hessian estimators using O(1) function evaluations. result Achieved a bias bound of order O(γδ2) for analytic real-valued functions. Active testing reduces label costs for efficient model evaluation.
problem Real-world applications require expensive test labels, disconnecting from existing model evaluation methods.
method Derives acquisition strategies to select test points efficiently, addressing label bias and variance.
result Active testing improves model evaluation efficiency without sacrificing accuracy.
Recency Bias selects recent uncertain samples for faster, more accurate deep learning.
problem Improving the accuracy of deep neural networks through better mini-batch selection.
method Uses historical label predictions to evaluate predictive uncertainty and selects samples proportionally.
result Reduces test error by up to 20.97% compared to existing methods in the same training time.
Study off-policy evaluation in partially observable environments, reducing bias and errors.
problem Bias and large errors in off-policy evaluation for partially observable environments.
method Defined and solved off-policy evaluation for POMDPs, introduced Decoupled POMDP model.
result Demonstrated and compared off-policy evaluation methods, showing benefits of new approach.
The paper assesses fairness in AI for financial services, using statistical methods.
problem Unintentional bias and insufficient model validation in AI applications.
method Statistical methods for imbalanced data treatment and bias mitigation.
result Fairness evaluation metrics applied to a credit card default payment example.
Paper identifies bias and strategic behavior in crowdsourced performance assessments.
problem Crowdsourced performance assessment risks personal interest introducing distortions.
method Developed a method to identify bias and strategic behavior using a rich dataset.
result Discovers a pattern of 'discriminatory generosity' in peer evaluation.
The paper detects and identifies bias in data using a counterfactual approach.
problem Detecting and identifying bias in data, especially in medical image classification.
method A global explanation framework using the counterfactual approach to identify bias causing artifacts.
result Black frames significantly influence Convolutional Neural Network's prediction, changing benign to malignant.
DOLCE improves off-policy evaluation and learning by decomposing effects.
problem Bias in off-policy evaluation and learning due to policy mismatch.
method Uses lagged contexts and a moment-based training procedure to decompose and cancel bias.
result DOLCE achieves substantial improvements in off-policy evaluation and learning.
New method reduces bias and variance in OPE for large action spaces.
problem High bias and variance in OPE for large, combinatorial action spaces.
method Factored action spaces and decomposed importance sampling.
result Decomposed IS estimators have less variance than non-decomposed versions.
Proposes BSF to evaluate structure learning algorithms without bias.
problem Evaluation bias in structure learning algorithms.
method Balanced Scoring Function (BSF) to adjust reward based on edge difficulty.
result Eliminates bias in favour of underfitted graphs.