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Found 7 papers, 4 papers with code
UFO: A unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations for CNNs
In this work, we propose UFO, a unified method for controlling Understandability and Faithfulness Objectives in concept-based explanations.
Overlooked factors in concept-based explanations: Dataset choice, concept learnability, and human capability
Second, we find that concepts in the probe dataset are often less salient and harder to learn than the classes they claim to explain, calling into question the correctness of the explanations.
Gender Artifacts in Visual Datasets
Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models.
ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features
Specifically, we develop a novel explanation framework ELUDE (Explanation via Labelled and Unlabelled DEcomposition) that decomposes a model's prediction into two parts: one that is explainable through a linear combination of the semantic attributes, and another that is dependent on the set of uninterpretable features.
Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation
In this work, we grapple with questions that arise along three stages of the machine learning pipeline when incorporating intersectionality as multiple demographic attributes: (1) which demographic attributes to include as dataset labels, (2) how to handle the progressively smaller size of subgroups during model training, and (3) how to move beyond existing evaluation metrics when benchmarking model fairness for more subgroups.
HIVE: Evaluating the Human Interpretability of Visual Explanations
As AI technology is increasingly applied to high-impact, high-risk domains, there have been a number of new methods aimed at making AI models more human interpretable.
Fair Attribute Classification through Latent Space De-biasing
Fairness in visual recognition is becoming a prominent and critical topic of discussion as recognition systems are deployed at scale in the real world.
