AI Explainability 360 (v0.3.0)

✨NEW: IBM Research has a new In-Context Explainability 360 (ICX360) toolkit that extends explainability to LLMs, specifically in terms of the input given to the LLM. Please check it out! 🚀✨

The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 toolkit supports tabular, text, images, and time series data.

The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

There is no single approach to explainability that works best. There are many ways to explain: data vs. model, directly interpretable vs. post hoc explanation, local vs. global, etc. It may therefore be confusing to figure out which algorithms are most appropriate for a given use case. To help, we have created some guidance material and a taxonomy tree that can be consulted.

We have developed the package with extensibility in mind. This library is still in development. We encourage you to contribute your explainability algorithms, metrics, and use cases. To get started as a contributor, please join the AI Explainability 360 Community on Slack by requesting an invitation here. Please review the instructions to contribute code and python notebooks here.

Supported explainability algorithms

Data explanations

• ProtoDash (Gurumoorthy et al., 2019)
• Disentangled Inferred Prior VAE (Kumar et al., 2018)

Local post-hoc explanations

• ProtoDash (Gurumoorthy et al., 2019)
• Contrastive Explanations Method (Dhurandhar et al., 2018)
• Contrastive Explanations Method with Monotonic Attribute Functions (Luss et al., 2019)
• Exemplar based Contrastive Explanations Method
• Grouped Conditional Expectation (Adaptation of Individual Conditional Expectation Plots by Goldstein et al. to higher dimension )
• LIME (Ribeiro et al. 2016, Github)
• SHAP (Lundberg, et al. 2017, Github)

Time-Series local post-hoc explanations

• Time Series Saliency Maps using Integrated Gradients (Inspired by Sundararajan et al. )
• Time Series LIME (Time series adaptation of the classic paper by Ribeiro et al. 2016 )
• Time Series Individual Conditional Expectation (Time series adaptation of Individual Conditional Expectation Plots Goldstein et al. )

Local direct explanations

• Teaching AI to Explain its Decisions (Hind et al., 2019)
• Order Constraints in Optimal Transport (Lim et al.,2022, Github)

Certifying local explanations

• Trust Regions for Explanations via Black-Box Probabilistic Certification (Ecertify) (Dhurandhar et al., 2024)

Global direct explanations

• Interpretable Model Differencing (IMD) (Haldar et al., 2023)
• CoFrNets (Continued Fraction Nets) (Puri et al., 2021)
• Boolean Decision Rules via Column Generation (Light Edition) (Dash et al., 2018)
• Generalized Linear Rule Models (Wei et al., 2019)
• Fast Effective Rule Induction (Ripper) (William W Cohen, 1995)

Global post-hoc explanations

• ProfWeight (Dhurandhar et al., 2018)

Supported explainability metrics

• Faithfulness (Alvarez-Melis and Jaakkola, 2018)
• Monotonicity (Luss et al., 2019)

Setup

Supported Configurations:

Installation keyword	Explainer(s)	OS	Python version
cofrnet	cofrnet	macOS, Ubuntu, Windows	3.10
contrastive	cem, cem_maf	macOS, Ubuntu, Windows	3.7
dipvae	dipvae	macOS, Ubuntu, Windows	3.10
gce	gce	macOS, Ubuntu, Windows	3.10
ecertify	ecertify	macOS, Ubuntu, Windows	3.10
imd	imd	macOS, Ubuntu	3.10
lime	lime	macOS, Ubuntu, Windows	3.10
matching	matching	macOS, Ubuntu, Windows	3.10
nncontrastive	nncontrastive	macOS, Ubuntu, Windows	3.10
profwt	profwt	macOS, Ubuntu, Windows	3.6
protodash	protodash	macOS, Ubuntu, Windows	3.10
rbm	brcg, glrm	macOS, Ubuntu, Windows	3.10
rule_induction	ripper	macOS, Ubuntu, Windows	3.10
shap	shap	macOS, Ubuntu, Windows	3.6
ted	ted	macOS, Ubuntu, Windows	3.10
tsice	tsice	macOS, Ubuntu, Windows	3.10
tslime	tslime	macOS, Ubuntu, Windows	3.10
tssaliency	tssaliency	macOS, Ubuntu, Windows	3.10

(Optional) Create a virtual environment

AI Explainability 360 requires specific versions of many Python packages which may conflict with other projects on your system. A virtual environment manager is strongly recommended to ensure dependencies may be installed safely. If you have trouble installing the toolkit, try this first.

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