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Found 25 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.
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.
A Case Study on Designing Evaluations of ML Explanations with Simulated User Studies
We hope that this work motivates further study of when and how SimEvals should be used to aid in the design of real-world evaluations.
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.
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.
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.
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.
Aequitas: A Bias and Fairness Audit Toolkit
Recent work has raised concerns on the risk of unintended bias in AI systems being used nowadays that can affect individuals unfairly based on race, gender or religion, among other possible characteristics.
Entity Retrieval and Text Mining for Online Reputation Monitoring
Besides E-R retrieval we also believe ORM would benefit of text-based entity-centric prediction capabilities, such as predicting entity popularity on social media based on news events or the outcome of political surveys.
Learning Word Embeddings from the Portuguese Twitter Stream: A Study of some Practical Aspects
Using a single GPU, we were able to scale up vocabulary size from 2048 words embedded and 500K training examples to 32768 words over 10M training examples while keeping a stable validation loss and approximately linear trend on training time per epoch.
FEUP at SemEval-2017 Task 5: Predicting Sentiment Polarity and Intensity with Financial Word Embeddings
This paper presents the approach developed at the Faculty of Engineering of University of Porto, to participate in SemEval 2017, Task 5: Fine-grained Sentiment Analysis on Financial Microblogs and News.
Mining Social Media for Open Innovation in Transportation Systems
This work proposes a novel framework for the development of new products and services in transportation through an open innovation approach based on automatic content analysis of social media data.
SentiBubbles: Topic Modeling and Sentiment Visualization of Entity-centric Tweets
Social Media users tend to mention entities when reacting to news events.
