Paper uses conformal prediction sets to make criminal justice risk assessments fairer.
problem Fairness issues in criminal justice risk assessment algorithms.
method Adopting conformal prediction sets to remove unfairness from algorithms and covariates.
result Constructs confusion tables and measures fairness effectively free of racial differences.
Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual precision. Rhetoric too often substitutes for careful analysis. In this paper, we seek to clarify the tradeoffs between different kinds of fairness and between fairness and accuracy. Methods: We draw on the existing liter…
New method reduces unfairness in binary classification.
problem Achieving similar false positive and negative rates across two populations.
method Penalizes unfairness to achieve balanced false positive and negative rates.
result Empirically validated approach improves fairness and accuracy.
Bayesian model assesses criminal threat escalation.
problem Evaluate threats posed by potential violent criminals.
method Customised three-level Bayesian hierarchical model.
result Model calibrates to expert judgments and updates in real time.
Study examines how people perceive fairness in criminal risk prediction algorithms.
problem Concerns about fairness in algorithmic decision making, especially in criminal risk prediction.
method Survey of 576 people to understand perceptions of fairness in algorithmic decision making.
result People's fairness judgments are influenced by eight latent properties of features in algorithms.
Network analysis helps prevent money laundering by identifying risky clients and suspicious clusters.
problem Preventing money laundering using social network analysis.
method Real-world data analysis, network metrics, predictive models, visual analysis.
result Risk profiles can be predicted using social network metrics.
Recidivism prediction instruments provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy concerns …
Improves pre-trial risk assessments by making them safer without changing existing rules.
problem Improving pre-trial risk assessments while maintaining deterministic rules.
method Developed a maximin robust optimization approach to find a safer policy.
result Can safely improve certain components of the risk assessment instrument.
Recidivism prediction instruments (RPI's) provide decision makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time. While such instruments are gaining increasing popularity across the country, their use is attracting tremendous controversy. Much of the controversy c…
New metrics improve fairness in risk assessments.
problem Risk assessments reflect historical policies, not future decisions.
method Counterfactual analogues of metrics, doubly robust estimation.
result Fairness metrics under counterfactuals can differ from standard metrics.
Paper introduces xAUC metric to assess fairness of risk scores in bipartite ranking tasks.
problem Disparate impact of risk scores in non-binary, downstream uses.
method Investigates fairness in bipartite ranking tasks, introduces xAUC metric.
result xAUC metric reveals disparities not seen in binary classification performance.
New fair-by-design model reduces bias in recidivism prediction.
problem Discriminatory bias in recidivism prediction models.
method Prototype-based, locally learned, data distribution extraction.
result Reduces bias and provides interpretable rules.
The paper reconciles two conflicting fairness criteria in algorithmic risk scores.
problem How to reconcile calibration and equal error rates in algorithmic risk scores.
method Derive necessary and sufficient conditions for existence of calibrated scores achieving equal error rates, then present an algorithm to find the most accurate score subject to both criteria.
result The method can eliminate error disparities while maintaining calibration and improve profit in credit lending.
Paper presents mdfa to identify victims of discrimination in black box classifiers.
problem Identifying victims of discrimination in black box classifiers.
method Reduces discrimination measurement to matching distributions and sensitive attribute coincidence prediction.
result Identifies African-American individuals at high risk of violent recidivism.
DeFi doesn't fully remove trust, showing run risk and personal character's importance.
problem The need for trust in DeFi despite its code-based approach.
method Natural experiment revealing identities of DeFi participants, including a criminal.
result DeFi doesn't fully remove trust, showing run risk and personal character's relevance.
Machine learning predicts criminal networks' missing partnerships and future behavior.
problem Predicting and understanding criminal networks' properties and future behavior.
method Combining graph representation learning and machine learning methods.
result Outstanding accuracy in predicting missing criminal partnerships and future behavior.
New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.
problem Current fairness approaches assume perfect human compliance, but real-world compliance is often poor.
method Defines compliance-robustly fair algorithms and proposes an optimization strategy to improve fairness.
result Algorithmic recommendations can improve fairness even if humans do not fully comply with fair algorithms.
Interpretable ML models predict recidivism as well as non-interpretable methods and are more fair.
problem Improving recidivism prediction models for fairness and interpretability.
method Trained interpretable ML models on two recidivism datasets, compared to existing methods, and analyzed fairness.
result Interpretable ML models can predict recidivism as well as non-interpretable methods and are more fair.
New model detects hidden group structures in criminal networks.
problem Challenges in identifying group structures in criminal networks with noisy data.
method Developed an extended stochastic block model (ESBM) to infer group structures.
result Unveiled complex block structures in an Italian mafia network.
Study analyzes homicide rates in Goias, Brazil, using clustering methods.
problem Understanding spatial distribution and risk factors of homicide in Goias.
method Cluster analysis using K-means, Density-based, and Hierarchical algorithms.
result Homicide rates higher in cities near large urban centers despite better socioeconomic indicators.
Local discovery method uncovers direct unfairness in complex systems.
problem Identifying causal pathways of unfairness in complex domains.
method Local discovery for direct discrimination (LD3) method.
result LD3 returns a valid adjustment set (VAS) for assessing unfairness.
Study disrupts Sicilian Mafia networks using data analysis.
problem Challenges in disrupting resilient criminal networks.
method Social Network Analysis methods applied to real-world datasets.
result Neutralizing only 5% of affiliates can disrupt network connectivity by 70%.
This thesis evaluates the quality of binary classification models in crime forecasting tools.
problem Quality assessment of binary classification models in crime forecasting tools.
method Binary classifier approach to evaluate AUC and PPVk. result The PPVk deviates significantly from AUC in crime forecasting models. Report assesses Edinburgh tram project's risk management.
problem Projects often exceed costs and timelines, underperforming intended benefits.
method Describes and evaluates risk assessment and management for the Edinburgh tram.
result Careful consideration of cost, schedule, and benefit risks is crucial for megaprojects.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
Bayesian networks improve product risk assessment by handling uncertainty and causality.
problem Limited handling of uncertainty and inability to incorporate causal explanations in existing methods.
method Bayesian Networks (BNs) for improved systematic product risk assessment.
result BN approach provides more powerful and flexible risk assessments.
The paper tackles fairness in machine learning by modeling latent unbiased labels.
problem Ensuring fairness in machine learning systems that use biased data.
method Explicitly models a latent variable representing a hidden, unbiased label to achieve demographic parity.
result The latent variable approach successfully retrieves fair labels from biased data.
Unified framework for CVA sensitivities, hedging, and risk assessment.
problem Computing and managing Credit Value Adjustment (CVA) sensitivities and risks.
method Probabilistic machine learning and refined regression on simulated data, validated by Monte Carlo methods.
result Identification of optimal sensitivities for practical tasks like hedging and risk assessment.
New method assesses financial and cyber risks under uncertainty.
problem Uncertainty in risk assessment for financial and cyber systems.
method Combines stochastic approximation and distorted mix method to compute worst case average value at risk.
result Efficient algorithm for tail uncertainty in multivariate distributions.
The paper introduces a model comparison framework for identifying fairness differences between machine learning models.
problem Comparing fairness in machine learning models across different subgroups.
method Develops a framework to automatically identify subgroups where models differ in fairness metrics.
result Identifies subgroups where machine learning models disagree on fairness-related quantities.
Paper proposes model to assess financial risk of grid-ignited wildfires.
problem Financial risk and solvency threats from grid-ignited wildfires.
method Integrated model to evaluate damage costs and risk levels.
result Identifies high-risk areas for preemptive actions.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Paper tackles fairness in algorithms by predicting protected class from auxiliary data.
problem Protected class membership is often unobserved in data, leading to unfair algorithmic decisions.
method Use auxiliary datasets to predict protected class from proxy variables and provide characterizations of possible disparities.
result Common disparity measures are generally unidentifiable with auxiliary data, highlighting the need for robust assessments.
Network theory assesses systemic risk in the insurance sector.
problem Detecting critical insurance companies in systemic risk.
method Complex network approach with weighted effective resistance centrality.
result Identifies companies with significant influence on network robustness.
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and criminal justice, but are difficult to learn from data because they need to be calibrated, sparse, use small integer coefficients, and obey …
Optimizes risk assessment tools using mixed-integer programming.
problem Challenges in healthcare risk assessment due to label scarcity and asymmetric misclassification costs.
method Jointly optimizes scoring weights and category thresholds via mixed-integer programming (MIP).
result Prevents label-scarce category collapse and achieves more accurate risk categorization.
Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.
problem Current risk assessment ignores the stochastic nature of energy storage availability.
method Data-driven unified model with exogenous and endogenous uncertainty description for four types of generic energy storage.
result Comparative results show more severe risks for endogenous uncertainty, suggesting new strategies for system operators.
Large corporate credit models may be adapted for small business risk assessment.
problem Limited data and lack of credit analysts for small businesses.
method Adapting large corporate credit risk models for small businesses.
result Adapted models can predict small business credit risk effectively.
Study combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
Enhances Transformers for better risk assessment in finance.
problem Transformer models lack sensitivity to extreme financial losses.
method Integrates Loss-at-Risk function with Value at Risk (VaR) and Conditional Value at Risk (CVaR).
result Improves risk prediction and management in financial datasets.
Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
Paper proposes a natural hedging framework with graphical assessment for longevity risk management.
problem Lack of a unified framework for natural hedging and graphical risk assessment.
method Structured natural hedging framework integrated with a graphical risk metric.
result Demonstrates flexibility, interpretability, and practical value for longevity risk management.
Approach for assessing supply chain cyber risks using expert judgment and forecasting.
problem Supply chain managers face challenges in assessing cyber risks affecting business factors.
method Structured expert judgment and forecasting models to assess various attack techniques and impacts.
result Facilitates implementation of risk management activities and decision-making processes.
TinyXRA assesses financial risks from 10-K reports using a lightweight transformer model.
problem Comprehensive risk assessment from financial reports, distinguishing between upside and downside risk.
method Lightweight transformer model with dynamic attention, incorporating skewness, kurtosis, and Sortino ratio.
result State-of-the-art predictive accuracy and transparent risk assessments.
The paper identifies and critiques problems with risk matrices using ordinal scales.
problem Problems with risk matrices using ordinal scales.
method Overview of risk assessment process, explanation of fallacies, and suggestions for improvement.
result The paper proposes avoiding risk matrices and using fully quantitative methods instead.
Neural networks assess asset-liability risk over time.
problem Challenging valuation of portfolios with complex products.
method Neural network approach for conditional portfolio valuation.
result Effective risk assessment for banking and insurance portfolios.
A new method uses BiGANs to assess operational risks in distribution networks.
problem Operational risk assessment in distribution networks using online monitoring data.
method Unsupervised approach based on BiGANs with adversarial feature learning and operational risk assessment.
result The approach provides more accurate risk assessment and discovers latent data structures.
The paper introduces a new risk assessment framework using φ-divergence.
problem Assessing risk and decision-making in uncertain conditions.
method Introduces a novel framework called the φ-Divergence Quadrangle.
result Provides a more nuanced understanding of risk through φ-divergence.