AI Explainability 360

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Overview

HEALTH SCORE

Concerning*

Partial score (2/3 categories)

23out of 65

Maintainer coverage is the lead risk. Development cadence is present but not enough to offset the structural risk. Security & Supply Chain is unavailable, so weights were redistributed.

LIFECYCLE

Active

Consistent commits, responsive maintainers, regular releases, and healthy issue triage.

Display your project Health score on your GitHub page.

Health breakdown

Concerning*(23/65)

Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.

Maintainer responsiveness

No PRs or issues were opened in the review window, so response time could not be measured.

Bus factor

Only 2 active maintainers currently have merge rights.

Organization Diversity

Contributors span 13 organizations.

Project Achievements

Leaderboards

About

interpretability and explainability of data and machine learning models. AI Explainability 360 is an open source toolkit that can help users better understand the ways that machine learning models predict labels using a wide variety of techniques throughout the AI application lifecycle.

First commit

July 2019

Software value

$2.4M

Maturity

Incubating