New fairness notion helps identify fair auditors for evaluating decision-support systems.
problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.
AI system narrows human decision options for better outcomes.
problem Improving human decision-making in sequential tasks.
method Developed a decision support system using a pre-trained AI agent to limit human action choices.
result Participants outperformed AI and solo play in a wildfire mitigation game.
New decision support systems use prediction sets to help experts update their predictions, improving performance.
problem Challenges in explaining and updating predictions from decision support systems.
method Developed a methodology leveraging nested structure of prediction sets and counterfactual monotonicity to improve performance.
result Limiting experts' agency leads to better performance in decision support systems based on prediction sets.
Discriminative classifiers improve decision-making in SHM systems.
problem Lack of descriptive labels for SHM data.
method Risk-based active learning with discriminative classifiers.
result Discriminative classifiers offer improved robustness and reduced inspection costs.
System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.
problem Helping experts make accurate decisions in multiclass classification tasks.
method Develops an automated decision support system using conformal prediction to provide precise prediction sets and an efficient search method.
result System improves expert predictions by providing precise prediction sets and forcing experts to predict from these sets.
Integrating causal machine learning with inherently interpretable models for decision support.
problem Providing causal insights and decision support through machine learning models.
method Proposing an approach that integrates causal machine learning with inherently interpretable models.
result The proposed approach achieves competitive performance in prediction and what-if analysis while offering transparency on the system structure, causal relationships among variables, and functional forms connecting them.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
PolicySynth improves synthetic data alignment with real data for better campaign decisions.
problem Synthetic data used in decision support systems often leads to incorrect decisions.
method PolicySynth framework that conditions synthetic data on churn scorer to align with real data decisions.
result PolicySynth achieves high strategy simulation fidelity (0.923-0.960) on churn and acquisition datasets.
Bayesian system helps identify and thwart terrorist plans.
problem Identifying and thwarting terrorist plans before they occur.
method Develops a Bayesian decision support system to integrate member and group activities.
result Estimates the combined threat posed by a terrorist group.
T-SVM improves learning in spiking neurons by maximizing dynamical margin.
problem Finding robust solutions in spiking neuronal networks with temporal correlations.
method Introduces Temporal Support Vector Machine (T-SVM) to maximize dynamical margin.
result T-SVM enables learning of tasks requiring nonlinear spatial integration.
Automated data-driven decision-making systems are ubiquitous across a wide spread of online as well as offline services. These systems, depend on sophisticated learning algorithms and available data, to optimize the service function for decision support assistance. However, there is a growing concern about the accounta…
Two new approaches improve decision-making in asset monitoring systems.
problem Sampling bias in risk-based active learning leads to poor performance later.
method Semi-supervised learning and discriminative classification models.
result Discriminative classifiers are more robust to sampling bias.
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement learning (DRL) models in this paper. DRL models have demonstrated human-le…
Deep learning, an area of machine learning, is set to revolutionize patient care. But it is not yet part of standard of care, especially when it comes to individual patient care. In fact, it is unclear to what extent data-driven techniques are being used to support clinical decision making (CDS). Heretofore, there has …
DECE visualizes machine learning decisions with counterfactual explanations.
problem Making machine learning models transparent and explainable.
method Interactive visualization system supporting counterfactual explanations at instance- and subgroup-levels.
result DECE enables users to explore and understand machine learning model decisions.
AI predicts medical specialty diagnostic choices from EHR records.
problem Predicting timely medical specialty diagnostic workups for patients.
method Ensemble of feed-forward neural networks trained on EHR data.
result Significantly higher accuracy compared to traditional checklists.
Proposes TPIS for early and low-cost TB vs. pneumonia diagnosis.
problem Challenges in differentiating TB from pneumonia.
method Two-step decision support system with stacked ensemble classifiers.
result TPIS outperforms other methods in early and final diagnosis.
Paper detects bias in AI medical models using CART.
problem Ensuring fairness in AI medical decision support systems.
method Uses Classification and Regression Trees (CART) algorithm to identify bias.
result Validated the CART approach in both synthetic and real-world data.
The paper uses GIS data to predict urban sprawl.
problem Overgrowth and expansion of low-density areas with car dependency and segregation.
method Data mining algorithms (Apriori, J4.8) adapted for geospatial analysis using ArcGIS.
result Prototype spatial decision support system (SDSS) predicts urban sprawl and estimates impact variables.
Clinical decision support systems (CDSS) will play an in-creasing role in improving the quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have com-plete information on state of supporting physiologic monitor-ing devices, which can …
This work shifts focus from prediction to intervention in social systems.
problem The limitations of focusing solely on prediction in automated decision systems.
method Shift from prediction-focused paradigm to intervention-oriented approach.
result A new perspective unifies statistical frameworks and tools for ADS design, implementation, and evaluation.
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.
A two-stage decision support system optimizes long-short portfolios under ESG considerations.
problem Optimizing long-short portfolios under environmental, social, and governance (ESG) considerations.
method First stage: Multi-criteria evaluation using TODIMSort and MEREC. Second stage: Non-convex portfolio optimization with Omega ratio.
result ESG-enhanced long-short portfolios outperform non-ESG and market-value-weighted benchmarks.
Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that simple approximations, such as linear models or decision-trees, are inherently human-interpretable, which has not been empirically tested. Addi…
Paper improves privacy-preserving measurement of advertising incrementality.
problem Privacy degradation in randomized lift tests for advertising measurement.
method Formulates a robust causal decision problem under signal losses, projecting clean worlds onto incrementality.
result Sharp decision frontier shows valid certification or rejection outside the frontier.
New method provides calibrated feature importance explanations for regression models.
problem Lack of uncertainty quantification in existing local explanation methods.
method Extension of Calibrated Explanations method to support regression and probabilistic regression.
result Calibrated Explanations for regression provides quantified uncertainty and robust explanations.
Improved forecast accuracy for energy systems through decision-focused fine-tuning.
problem Challenges in integrating forecast values into time series models for diverse and specific instances.
method Decision-focused fine-tuning within time series foundation models for dispatchable feeder optimization.
result Improvement of 9.45% in average total daily costs.
TabNet improves fraud detection in bank transactions.
problem Detecting fraudulent bank transactions using deep learning.
method TabNet, SMOTE, EDA, 5 deep learning architectures, 3-fold cross-validation.
result TabNet achieved 97.39% accuracy and 0.9739 ROC-AUC.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
Paper examines how income support affects retirement decisions for low-income individuals.
problem Retirement decisions of low-income individuals affected by income disaster.
method Modeling consumption/savings, investment, and retirement choices with income support.
result Optimal retirement decision depends on the level of income support provided.
Lattice investment projects support process model with corruption is formulated and analyzed. The model is based on the Ising lattice model of ferromagnetic but takes deal with the social phenomenon. Set of corruption agents is considered. It is supposed that agents are placed in sites of the lattice. Agents take decis…
The paper develops a method for inferring second opinions from experts using counterfactual inference.
problem Designing efficient decision support systems for second opinions.
method Set invariant Gumbel-Max structural causal model for multiclass classification.
result The proposed model can infer second opinions more accurately than non-causal models.
DAT-CGAN improves time series generation for better decision support.
problem Generating accurate time series data for decision support.
method DAT-CGAN uses multi-Wasserstein loss and overlapped block-sampling for improved sample efficiency.
result DAT-CGAN outperforms GAN-based baselines in generating data relevant to decision processes.
Bayesian network (BN) modelling is extensively used in systems epidemiology. Usually it consists in selecting and reporting the best-fitting structure conditional to the data. A major practical concern is avoiding overfitting, on account of its extreme flexibility and its modelling richness. Many approaches have been p…
Algorithmic decision making systems are ubiquitous across a wide variety of online as well as offline services. These systems rely on complex learning methods and vast amounts of data to optimize the service functionality, satisfaction of the end user and profitability. However, there is a growing concern that these au…
The paper develops a decision support system for hierarchical text classification of conference proceedings.
problem Classifying documents with a fixed hierarchical structure of topics.
method Developed a weighted hierarchical similarity function to calculate topic relevance, using entropy of words to estimate weights.
result The weighted hierarchical similarity function improves ranking accuracy compared to other methods.
An assistant learns to mediate decisions between humans and experts, balancing risk and learning.
problem Learning to mediate decisions between imperfect humans and expert knowledge.
method Formalizes online decision mediation, proposes a policy to balance immediate loss and future generalization.
result Consistent gains over benchmarks in decision-making performance.
Paper introduces probabilistic digital twins for optimal decision making under uncertainty.
problem Optimal sequential decision making under incomplete and uncertain information.
method Formal definition of epistemic uncertainty using measure theory, and solution via deep reinforcement learning.
result Proposes a generic approximate solution for optimal sequential decision making.
Benchmark evaluates AI-generated financial QA hallucinations, highlighting system vulnerabilities.
problem Ensuring factual accuracy of AI-generated financial QA outputs.
method Developed a benchmark dataset and evaluated six detection methods under clean and noisy conditions.
result LLM-based judges and embedding methods perform best, but degrade under noisy conditions.
DeXposure-Claw supervises decentralized finance risks by grounding LLM decisions in evidence.
problem Weak evidence leads to over-interventions by general-purpose LLM agents in decentralized finance.
method DeXposure-Claw uses a graph time-series foundation model to forecast exposure networks, turning forecasts into alerts and constraining escalation with data-health gates.
result DeXposure-Claw reduces false alarms and improves regulator alignment in decentralized finance risk supervision.
Machine learning improves prediction of complex geology ahead of drilling.
problem Predicting complex geology during drilling in real-time.
method Generative Adversarial Network (GAN) and Forward Deep Neural Network (FDNN) for real-time geological uncertainty reduction.
result Real-time estimates of complex geological uncertainty achieved.
Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal r…
Risk-based active learning improves SHM decision-making.
problem Lack of prior labels for structural health monitoring.
method Risk-based active learning approach to guide data labeling.
result Improves decision-maker's performance in SHM.
Deep learning complements OR/MS for decision-making under uncertainty.
problem Sequential decision-making in uncertain environments.
method Integration of deep learning and OR/MS frameworks.
result Deep learning enhances adaptability and scalability in decision systems.
A system for supervising decentralized finance risks using LLMs and structured evidence.
problem Supervising decentralized finance risks
method Forecast-grounded agentic supervision system
result Developed a system that scores tickets against a regulator-aligned ground truth and false-intervention rate.
Research develops a DSS for stock selection and asset allocation using fundamental data.
problem Complex financial markets and limited use of fundamental data analysis.
method Data gathering, cleaning, and modeling of fundamental data; integration with macroeconomic conditions.
result Enhanced predictive model for mid- to long-term stock returns.
Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various methodologies have been proposed to analyze the disease factors aiming to decrease the phys…
Decision support systems (e.g., for ecological conservation) and autonomous systems (e.g., adaptive controllers in smart cities) start to be deployed in real applications. Although their operations often impact many users or stakeholders, no fairness consideration is generally taken into account in their design, which …