# microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Repository: https://github.com/microsoft/responsible-ai-toolbox
Canonical: https://ross.abutalabs.com/products/responsible-ai-toolbox
Homepage: https://responsibleaitoolbox.ai/
Language: TypeScript
License: MIT
License Family: permissive
Topics: ui, responsible-ai, data-science, fairness, fairness-ml, fairness-ai, explainable-ai, explainable-ml, explainability, machinelearning, machine-learning, ml, visualization, widgets, widget, jupyter, error-analysis, data-analysis, data-visualization, interpretability
Last push: 2026-08-19T19:32:14+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 8, longevity 100
- inputs: {"age_days": 2249, "days_push": 14, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1824, forks 492 (observed 2026-08-28T04:05:41.135874+00:00)

## What it is
A Microsoft suite of Python libraries and Jupyter widgets providing dashboards for model assessment, error analysis, interpretability, fairness evaluation, and causal decision-making. It helps developers and stakeholders understand, debug, and responsibly monitor AI systems.

## Use cases
- analyze why my machine learning model makes errors on certain cohorts
- visualize feature importance and model explanations in a Jupyter notebook
- assess fairness of my model across demographic groups
- debug underperforming ML model subpopulations
- generate a responsible AI dashboard for model assessment
- explore counterfactuals and causal insights for model decisions
- evaluate model error rates by data cohort

## When to choose
- you train ML models in Python and need interpretability, error analysis, or fairness assessment
- you want interactive dashboards inside Jupyter notebooks
- you need a holistic responsible-AI evaluation suite for tabular models

## When to avoid
- you need LLM/GenAI evaluation rather than classical ML model assessment
- you want a production monitoring service rather than notebook-based analysis
- your models are not accessible from Python

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, machine-learning, ui-components
- domain: machine-learning, data-science, data-visualization, developer-tools
- platform: python
- tags: responsible-ai, fairness, explainability, error-analysis, interpretability, jupyter-widgets, model-assessment, jupyter, web

## Member repositories
- microsoft/responsible-ai-toolbox (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:41.135874+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:19:44.105321+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/responsible-ai-toolbox (fetched 2026-08-28T04:05:41.135874+00:00, sha 1b6f7170b783)
- Data as of 2026-08-30T08:39:29.467469+00:00.