xGEMs: Generating Examplars to Explain Black-Box Models
This work proposes xGEMs or manifold guided exemplars, a framework to understand black-box classifier behavior by exploring the landscape of the underlying data manifold as data points cross decision boundaries. To do so, we train an unsupervised implicit generative model -- treated as a proxy to the data manifold. We summarize black-box model behavior quantitatively by perturbing data samples along the manifold. We demonstrate xGEMs' ability to detect and quantify bias in model learning and also for understanding the changes in model behavior as training progresses.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning to Explain: Generating Stable Explanations Fast
The importance of explaining the outcome of a machine learning model, especially a black-box model, is widely acknowledged. Recent approaches explain an outcome by identifying the contributions of input features to this …
Forward Learning for Gradient-based Black-box Saliency Map Generation
Gradient-based saliency maps are widely used to explain deep neural network decisions. However, as models become deeper and more black-box, such as in closed-source APIs like ChatGPT, computing gradients become challengi…
Explaining and visualizing black-box models through counterfactual paths
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-c…
counterfactualExplainable Artificial Intelligence (XAI)Feature ImportanceKnowledge Graphs"Why did you do that?": Explaining black box models with Inductive Synthesis
By their nature, the composition of black box models is opaque. This makes the ability to generate explanations for the response to stimuli challenging. The importance of explaining black box models has become increasing…
Program SynthesisExplaining Black-Box Models through Counterfactuals
We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield …
counterfactualExplainable artificial intelligence