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Found 5 papers, 2 papers with code
Eliciting Bias in Question Answering Models through Ambiguity
We develop two sets of questions for closed and open domain questions respectively, which use ambiguous questions to probe QA models for bias.
Do Concept Bottleneck Models Obey Locality?
Deep learning models trained under this paradigm heavily rely on the assumption that neural networks can learn to predict the presence or absence of a given concept independently of other concepts.
Human Uncertainty in Concept-Based AI Systems
We study how existing concept-based models deal with uncertain interventions from humans using two novel datasets: UMNIST, a visual dataset with controlled simulated uncertainty based on the MNIST dataset, and CUB-S, a relabeling of the popular CUB concept dataset with rich, densely-annotated soft labels from humans.
Improving Learning-to-Defer Algorithms Through Fine-Tuning
We work to improve learning-to-defer algorithms when paired with specific individuals by incorporating two fine-tuning algorithms and testing their efficacy using both synthetic and image datasets.
Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
Rideshare and ride-pooling platforms use artificial intelligence-based matching algorithms to pair riders and drivers.
