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Found 31 papers, 7 papers with code
Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier.
DiConStruct: Causal Concept-based Explanations through Black-Box Distillation
Lastly, explanation methods should be efficient and not compromise the performance of the predictive task.
FiFAR: A Fraud Detection Dataset for Learning to Defer
Financial fraud detection is a high-stakes setting where algorithms and human experts often work in tandem; however, there are no publicly available datasets for L2D concerning this important application of human-AI teaming.
Adversarial training for tabular data with attack propagation
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying fraudulent activity as legitimate, whereas system maintainers try to stop them.
The GANfather: Controllable generation of malicious activity to improve defence systems
In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system.
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
We demonstrate that our graph-sprints features, combined with a machine learning classifier, achieve competitive performance (outperforming all baselines for the node classification tasks in five datasets).
Fairness-Aware Data Valuation for Supervised Learning
Data valuation is a ML field that studies the value of training instances towards a given predictive task.
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
The suite was generated by applying state-of-the-art tabular data generation techniques on an anonymized, real-world bank account opening fraud detection dataset.
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering
Reviewing these cases is a cumbersome and complex task that requires analysts to navigate a large network of financial interactions to validate suspicious movements.
FairGBM: Gradient Boosting with Fairness Constraints
Tabular data is prevalent in many high-stakes domains, such as financial services or public policy.
Lightweight Automated Feature Monitoring for Data Streams
We propose a flexible system, Feature Monitoring (FM), that detects data drifts in such data sets, with a small and constant memory footprint and a small computational cost in streaming applications.
Understanding Unfairness in Fraud Detection through Model and Data Bias Interactions
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction
The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within.
Human-AI Collaboration in Decision-Making: Beyond Learning to Defer
Human-AI collaboration (HAIC) in decision-making aims to create synergistic teaming between human decision-makers and AI systems.
On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in their design, resulting in limited conclusions of methods' real-world utility.
ConceptDistil: Model-Agnostic Distillation of Concept Explanations
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop.
Data+Shift: Supporting visual investigation of data distribution shifts by data scientists
We propose Data+Shift, a visual analytics tool to support data scientists in the task of investigating the underlying factors of shift in data features in the context of fraud detection.
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1. 7-4 trillion euros annually) such as drug dealing, human trafficking, or corruption.
Active learning for imbalanced data under cold start
Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels, which are costly or slow to obtain.
Weakly Supervised Multi-task Learning for Concept-based Explainability
In ML-aided decision-making tasks, such as fraud detection or medical diagnosis, the human-in-the-loop, usually a domain-expert without technical ML knowledge, prefers high-level concept-based explanations instead of low-level explanations based on model features.
Promoting Fairness through Hyperparameter Optimization
Considerable research effort has been guided towards algorithmic fairness but real-world adoption of bias reduction techniques is still scarce.
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Money laundering is a global phenomenon with wide-reaching social and economic consequences.
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations
We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts.
Decision Making
Explainable Artificial Intelligence (XAI)
+1
TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
Although recurrent neural networks (RNNs) are state-of-the-art in numerous sequential decision-making tasks, there has been little research on explaining their predictions.
Teaching the Machine to Explain Itself using Domain Knowledge
Moreover, we collect the domain feedback from a pool of certified experts and use it to ameliorate the model (human teaching), hence promoting seamless and better suited explanations.
A Bandit-Based Algorithm for Fairness-Aware Hyperparameter Optimization
Hence, coupled with the lack of tools for ML practitioners, real-world adoption of bias reduction methods is still scarce.
BreachRadar: Automatic Detection of Points-of-Compromise
Bank transaction fraud results in over $13B annual losses for banks, merchants, and card holders worldwide.
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset.
Interleaved Sequence RNNs for Fraud Detection
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities.
ARMS: Automated rules management system for fraud detection
We propose ARMS, an automated rules management system that evaluates the contribution of individual rules and optimizes the set of active rules using heuristic search and a user-defined loss-function.
Automatic Model Monitoring for Data Streams
We evaluated SAMM using human feedback from domain experts, by sending them 100 reports generated by the system.
