560 research outputs found
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification
Despite the growing body of work on explainable machine learning in time
series classification (TSC), it remains unclear how to evaluate different
explainability methods. Resorting to qualitative assessment and user studies to
evaluate explainers for TSC is difficult since humans have difficulties
understanding the underlying information contained in time series data.
Therefore, a systematic review and quantitative comparison of explanation
methods to confirm their correctness becomes crucial. While steps to
standardized evaluations were taken for tabular, image, and textual data,
benchmarking explainability methods on time series is challenging due to a)
traditional metrics not being directly applicable, b) implementation and
adaption of traditional metrics for time series in the literature vary, and c)
varying baseline implementations. This paper proposes XTSC-Bench, a
benchmarking tool providing standardized datasets, models, and metrics for
evaluating explanation methods on TSC. We analyze 3 perturbation-, 6 gradient-
and 2 example-based explanation methods to TSC showing that improvements in the
explainers' robustness and reliability are necessary, especially for
multivariate data.Comment: Accepted at ICMLA 202
It is not "accuracy vs. explainability" -- we need both for trustworthy AI systems
We are witnessing the emergence of an AI economy and society where AI
technologies are increasingly impacting health care, business, transportation
and many aspects of everyday life. Many successes have been reported where AI
systems even surpassed the accuracy of human experts. However, AI systems may
produce errors, can exhibit bias, may be sensitive to noise in the data, and
often lack technical and judicial transparency resulting in reduction in trust
and challenges in their adoption. These recent shortcomings and concerns have
been documented in scientific but also in general press such as accidents with
self driving cars, biases in healthcare, hiring and face recognition systems
for people of color, seemingly correct medical decisions later found to be made
due to wrong reasons etc. This resulted in emergence of many government and
regulatory initiatives requiring trustworthy and ethical AI to provide accuracy
and robustness, some form of explainability, human control and oversight,
elimination of bias, judicial transparency and safety. The challenges in
delivery of trustworthy AI systems motivated intense research on explainable AI
systems (XAI). Aim of XAI is to provide human understandable information of how
AI systems make their decisions. In this paper we first briefly summarize
current XAI work and then challenge the recent arguments of accuracy vs.
explainability for being mutually exclusive and being focused only on deep
learning. We then present our recommendations for the use of XAI in full
lifecycle of high stakes trustworthy AI systems delivery, e.g. development,
validation and certification, and trustworthy production and maintenance
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability,
popularity, and sophistication. However, one of the major barriers to
widespread acceptance of machine learning (ML) is trustworthiness: most ML
models operate as black boxes, their inner workings opaque and mysterious, and
it can be difficult to trust their conclusions without understanding how those
conclusions are reached. Explainability is therefore a key aspect of improving
trustworthiness: the ability to better understand, interpret, and anticipate
the behaviour of ML models. To this end, we propose SMILE, a new method that
builds on previous approaches by making use of statistical distance measures to
improve explainability while remaining applicable to a wide range of input data
domains
OpenXAI: Towards a Transparent Evaluation of Model Explanations
While several types of post hoc explanation methods (e.g., feature
attribution methods) have been proposed in recent literature, there is little
to no work on systematically benchmarking these methods in an efficient and
transparent manner. Here, we introduce OpenXAI, a comprehensive and extensible
open source framework for evaluating and benchmarking post hoc explanation
methods. OpenXAI comprises of the following key components: (i) a flexible
synthetic data generator and a collection of diverse real-world datasets,
pre-trained models, and state-of-the-art feature attribution methods, (ii)
open-source implementations of twenty-two quantitative metrics for evaluating
faithfulness, stability (robustness), and fairness of explanation methods, and
(iii) the first ever public XAI leaderboards to benchmark explanations. OpenXAI
is easily extensible, as users can readily evaluate custom explanation methods
and incorporate them into our leaderboards. Overall, OpenXAI provides an
automated end-to-end pipeline that not only simplifies and standardizes the
evaluation of post hoc explanation methods, but also promotes transparency and
reproducibility in benchmarking these methods. OpenXAI datasets and data
loaders, implementations of state-of-the-art explanation methods and evaluation
metrics, as well as leaderboards are publicly available at
https://open-xai.github.io/.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022) Track on Datasets and Benchmark
CLEAR: Generative Counterfactual Explanations on Graphs
Counterfactual explanations promote explainability in machine learning models
by answering the question "how should an input instance be perturbed to obtain
a desired predicted label?". The comparison of this instance before and after
perturbation can enhance human interpretation. Most existing studies on
counterfactual explanations are limited in tabular data or image data. In this
work, we study the problem of counterfactual explanation generation on graphs.
A few studies have explored counterfactual explanations on graphs, but many
challenges of this problem are still not well-addressed: 1) optimizing in the
discrete and disorganized space of graphs; 2) generalizing on unseen graphs;
and 3) maintaining the causality in the generated counterfactuals without prior
knowledge of the causal model. To tackle these challenges, we propose a novel
framework CLEAR which aims to generate counterfactual explanations on graphs
for graph-level prediction models. Specifically, CLEAR leverages a graph
variational autoencoder based mechanism to facilitate its optimization and
generalization, and promotes causality by leveraging an auxiliary variable to
better identify the underlying causal model. Extensive experiments on both
synthetic and real-world graphs validate the superiority of CLEAR over the
state-of-the-art methods in different aspects.Comment: 18 pages, 9 figure
bLIMEy:Surrogate Prediction Explanations Beyond LIME
Surrogate explainers of black-box machine learning predictions are of
paramount importance in the field of eXplainable Artificial Intelligence since
they can be applied to any type of data (images, text and tabular), are
model-agnostic and are post-hoc (i.e., can be retrofitted). The Local
Interpretable Model-agnostic Explanations (LIME) algorithm is often mistakenly
unified with a more general framework of surrogate explainers, which may lead
to a belief that it is the solution to surrogate explainability. In this paper
we empower the community to "build LIME yourself" (bLIMEy) by proposing a
principled algorithmic framework for building custom local surrogate explainers
of black-box model predictions, including LIME itself. To this end, we
demonstrate how to decompose the surrogate explainers family into
algorithmically independent and interoperable modules and discuss the influence
of these component choices on the functional capabilities of the resulting
explainer, using the example of LIME.Comment: 2019 Workshop on Human-Centric Machine Learning (HCML 2019); 33rd
Conference on Neural Information Processing Systems (NeurIPS 2019),
Vancouver, Canad
- …