Machine learning based decision making systems are increasingly affecting humans. An individual can suffer an undesirable outcome under such decision making systems (e.g. denied credit) irrespective of whether the decision is fair or accurate. Individual recourse pertains to the problem of providing an actionable set o…
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48 results for “decision systems”
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.
We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., …
This paper optimizes slate decision systems for large action spaces.
problem Optimizing large-scale decision systems with arbitrary reward functions.
method A policy optimization framework with a novel relaxation of decision functions.
result Demonstrates the effectiveness of the proposed method on large action spaces.
Proposes efficient algorithm for system-level I&M decisions under uncertainty.
problem Optimal management strategies for deteriorating civil engineering systems.
method Factored partially observable Markov decision process with Bayesian networks and DDMAC reinforcement learning.
result DDMAC policies offer substantial benefits over heuristic approaches in system-level cost optimization.
Multiverse analysis helps prevent fairness hacking and evaluate model design decisions.
problem Downstream effects of ADM systems depend on implicit design and evaluation decisions.
method Turn implicit decisions into explicit ones, create a grid of decision combinations, compute fairness and performance metrics.
result Decisions regarding evaluation can lead to vastly different fairness metrics for the same model.
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.
Platform combines RL and language models to study narrative influence on AI decisions.
problem Understanding how narrative elements shape AI decision-making.
method Dual-system architecture with reinforcement learning and language model integration.
result Initial experiments show narrative frameworks can influence AI decision-making.
Sparse oblique decision tree improves security rules for renewable power systems.
problem Identifying secure operating conditions in power systems with high renewable energy.
method Sparse weighted oblique decision tree to learn and embed linear security rules.
result The method significantly increases secure states and reduces solution time.
Complete criterion for VoI in multi-decision influence diagrams established.
problem Analyzing safety and fairness properties of AI systems using influence diagrams.
method Introduced ID homomorphisms and Tree of Systems to prove properties of multi-decision influence diagrams.
result First complete graphical criterion for VoI in influence diagrams with multiple decisions.
Solves selecting the best optimizing system problems.
problem Selecting the best system among contenders with unknown performance.
method Adaptive algorithms integrating stochastic gradient descent and sequential elimination.
result Exponential rates of convergence to zero for false selection probability.
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.
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.
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.
Contextual Decision Treesstat.ML
A new method for decision tree selection in recommendation systems.
problem Feature-based selection of a single tree from an ensemble for dynamic interpretation.
method A multi-armed contextual bandit recommendation framework that trains a system on top of Random Forests to identify the most relevant tree.
result The dynamic method outperforms an independent CART tree and is comparable to Random Forest in predictive performance.
Specialized hierarchies improve decision-making by partitioning tasks.
problem Division of labor and specialization in hierarchical systems.
method Information-theoretic on-line learning rule for partitioning tasks.
result Enforces specialization and optimal combination of experts.
Bayesian principles improve agentic AI decision-making.
problem Decision-making under uncertainty in agentic AI systems.
method Bayesian decision theory applied to the orchestration layer of agentic AI.
result Bayesian principles enhance agentic AI's ability to make decisions under uncertainty.
In this paper, we present a novel sequential paradigm for classification in crowdsourcing systems. Considering that workers are unreliable and they perform the tests with errors, we study the construction of decision trees so as to minimize the probability of mis-classification. By exploiting the connection between pro…
Fair ML systems can be safe ML systems by considering uncertainty.
problem Safety of data-driven decision systems is often neglected.
method Viewing ML systems as socio-technical, uncertainty-aware modeling.
result Fair models should be uncertainty-aware, e.g. through distributional regression.
Improving decision-making via risk-based active learning: Probabilistic discriminative classifierscs.LG
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.
The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level …
In many machine learning applications, there are multiple decision-makers involved, both automated and human. The interaction between these agents often goes unaddressed in algorithmic development. In this work, we explore a simple version of this interaction with a two-stage framework containing an automated model and…
This thesis analyzes MACL systems with low-regret learning algorithms for sequential decision making.
problem Designing efficient learning algorithms for multi-agent cooperative systems to minimize regret.
method Analyzes and develops algorithms for cooperative multi-agent multi-armed bandit problems and online convex optimization in distributed settings.
result Presented regret lower bounds and efficient algorithms for achieving these bounds, providing guidance on communication protocols.
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
The paper extends conformal prediction to MDP trajectories for autonomous systems.
problem Ensuring reliability of autonomous systems by providing probabilistic guarantees.
method Applying conformal corrections to quantile regression prediction intervals.
result Conformal prediction intervals ensure the observed trajectory lies inside with high probability.
Decision-Making Frameworks for Network Resilience -- Managing and Mitigating Systemic (Cyber) Riskq-fin.RM
Framework for managing cyber risks in networks.
problem Managing systemic cyber risks in digital networks.
method Three components: acceptable configurations, risk mitigation interventions, and cost function.
result Effective decision-making for network resilience.
Inferring a person's goal from their behavior is an important problem in applications of AI (e.g. automated assistants, recommender systems). The workhorse model for this task is the rational actor model - this amounts to assuming that people have stable reward functions, discount the future exponentially, and construc…
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
Improved A/B testing by leveraging system similarities.
problem Traditional A/B testing ignores potential system similarities.
method Off-policy estimation to exploit system propensities.
result Improved A/B testing estimators achieve better accuracy.
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimizationcs.LG
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.
Paper formalizes anti-discrimination law in automated systems.
problem Algorithmic discrimination in legal contexts.
method Decision-theoretic framework grounded in UK anti-discrimination law.
result Introduced 'conditional estimation parity' metric for ML fairness.
Develops methods for AI self-assessment to improve trustworthiness.
problem Uncertainty in AI predictions and lack of trust in AI systems.
method Uncertainty estimation techniques considering practical impacts and costs.
result Guidelines for selecting and designing effective AI self-assessment methods.
Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?cs.LG
The paper uses causal machine learning to optimize rework decisions in manufacturing.
problem Optimizing rework policies in manufacturing systems to balance yield improvement and rework costs.
method Proposes a causal model using double/debiased machine learning (DML) techniques to estimate conditional treatment effects and derive rework policies.
result Achieved a yield improvement of 2-3% during the color-conversion process of white LEDs.
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…
New method evaluates AI stock prediction systems based on decision-making processes.
problem Lack of evaluation for AI systems' decision-making processes.
method Scores intermediate decision process using large language models and closed-loop reinforcement learning feedback.
result Composite behavioral score correlates with Sharpe ratio and reduces prediction error.
End-to-end portfolio system accounts for model risk.
problem Model risk in portfolio selection.
method Distributionally robust optimization with convex duality.
result Explicitly accounts for model risk in portfolio selection.
Automated decision making systems are increasingly being used in real-world applications. In these systems for the most part, the decision rules are derived by minimizing the training error on the available historical data. Therefore, if there is a bias related to a sensitive attribute such as gender, race, religion, e…
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.
Decision-calibrated prediction sets improve power system operations by reducing unnecessary costs.
problem Balancing operating costs and reliability in power systems with renewable uncertainty.
method Learn conditional prediction sets as sub-level sets of norm-based score functions, calibrate uncertainty sets based on reliability of downstream decisions.
result Decision-calibrated sets lead to more efficient operations with smaller uncertainty sets and lower costs compared to standard coverage-based calibration.
The development of machine learning systems for the diagnosis of rare diseases is challenging mainly due the lack of data to study them. Despite this challenge, this paper proposes a system for the Computer Aided Diagnosis (CAD) of low-prevalence, congenital muscular dystrophies from confocal microscopy images. The pro…
Enhanced financial trading system using multi-agent LLMs with layered memory.
problem Inefficient prioritization of tasks in LLMs due to their memory processing.
method Introducing a multi-agent framework with layered memories and inter-agent debate.
result Superior automated trading accuracy and decision robustness.
This study investigates the use of reinforcement learning to guide a general purpose cache manager decisions. Cache managers directly impact the overall performance of computer systems. They govern decisions about which objects should be cached, the duration they should be cached for, and decides on which objects to ev…
Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by querying the model in black-box settings, where no information abou…
New framework uses OR to ensure AI systems make safe decisions.
problem Ensuring generative AI systems make safe decisions as they gain autonomy.
method Developed a conceptual framework combining flow-based models and adversarial robustness.
result Increased autonomy requires new OR approaches for feasibility, robustness, and stress testing.
The paper introduces a new algorithm for fair decision-making in outcome control tasks.
problem Fair and equitable automated decision-making in outcome control tasks.
method Causal analysis and optimization to ensure fairness in decision-making.
result Developed an algorithm for maximizing while ensuring causal fairness.
In most real-world settings such as recommender systems, finance, and healthcare, collecting useful information is costly and requires an active choice on the part of the decision maker. The decision-maker needs to learn simultaneously what observations to make and what actions to take. This paper incorporates the info…
New algorithm reduces costs in wind energy systems by minimizing decision changes.
problem Costs associated with decision changes in wind energy systems.
method Episodic CBO with movement costs using Gaussian Process and mirror descent.
result Our algorithm consistently outperforms standard CBO in altitude optimization.
Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical a…