The Infosys Responsible AI toolkit incorporates various features including safety, security, explainability, fairness, bias and hallucination detection to ensure AI solutions are trustworthy and transparent.
C-Rank Grade: B (Solid) - 58/100
External PR Merge Rate: 75%
Response Time: 1d
First Timer Success: 100%
Infosys/Infosys-Responsible-AI-Toolkit has a recorded first-timer success rate of 100.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in Infosys/Infosys-Responsible-AI-Toolkit respond to incoming external pull requests in approximately 24.0 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 57.9 places Infosys/Infosys-Responsible-AI-Toolkit in the B tier.
The external contributor pull request merge rate for Infosys/Infosys-Responsible-AI-Toolkit is 75.0%, based on public PR activity from non-core contributors.
Infosys/Infosys-Responsible-AI-Toolkit currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Growing Python project in Infosys/Infosys-Responsible-AI-Toolkit welcoming community pull requests and bug fixes.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/Infosys/Infosys-Responsible-AI-Toolkit?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 13:00 UTC (13:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 11:00 to 15:00 UTC (11:00 - 15:00 UTC local).
Limited recent maintainer review activity recorded for this repository. Prediction based on heuristic baseline.
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~16 hours (94% approval). Large PRs (>500 lines) take ~115 hours (34% approval). Keep your diff compact for rapid review.
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Module Name: responsible-ai-moderationlayer summary: Enable risk detection ( toxicity/PII/Prompt-injection, etc) for romanized Indian Languages (e.g., Hinglish) in chatbot and app integrations. why: Current checks miss non-standard romanized/code-mixed inputs. Scope: Add transliteration/normalization --> run existing checks, handle common code-mix patterns. Constraints: Sparse labeled data, high spelling variability.
Module name: responsible-ai-moderationlayer Summary: Add deepfake detection to existing image guardrails to mitigate synthetic/ manipulated media risks. Why: Current image guardrails don't cover deepfakes, rising enterprise risk. Scope: Integrate an open-source deepfake detector, return confidence score, apply policy (block/warn/log). Constraints: Limited infra for fine-tuning, need a combined, diverse dataset (for multiple generators).
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When evaluating whether to contribute to Infosys/Infosys-Responsible-AI-Toolkit, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.
Currently, maintainers of Infosys/Infosys-Responsible-AI-Toolkit acknowledge new external contributions in approximately 1 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 75.0% were successfully merged into the primary branch.
Infosys/Infosys-Responsible-AI-Toolkit has a recorded first-timer success rate of 100.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
The C-Rankâ„¢ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 57.9 places Infosys/Infosys-Responsible-AI-Toolkit in the Solid tier.
The external pull request merge rate is 75.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, Infosys/Infosys-Responsible-AI-Toolkit currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for Infosys/Infosys-Responsible-AI-Toolkit are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.