Coforge Quasar Responsible AI Assessment

Introducing Quasar Responsible AI, an Azure hosted platform that plays a pivotal role in identifying, explaining and mitigating biases within datasets and models, ensuring model explainability, identifying compliance challenges, all the while providing clear options to govern, mitigate, and remediate AI risks where necessary. In a world where anti-discrimination and privacy laws are becoming increasingly stringent, this platform provides a robust framework for trustworthy and ethical AI.

Our expert team, leveraging the robust capabilities of Azure hosted Quasar Responsible AI, will conduct a comprehensive assessment of your chosen AI model. We'll delve into inexplicable decision-making processes, and security vulnerabilities providing a clear roadmap to mitigation of risks and enablement of responsible AI deployment. This targeted engagement ensures peace of mind, maximizes trust of AI in your operations, and positions you for success in the ever-evolving AI landscape.

Major Takeaways of the Assessment Offering:

This Assessment helps organizations address three key aspects of responsible AI:

1.AI Compliance: Navigate complex regulations such as SR 11-7, FTC, and Equal Credit Opportunity Act, Proposed EU AI Act, UK AI Regulation Policy, US AI Bill of Rights, and your own internal AI risk management policies & controls, ensuring compliant and ethical AI design, development, and use.

2.Fairness: Employ industry-trusted tools and techniques to identify and mitigate AI bias in datasets and models, promoting fair and equitable treatment.

3.Explainability:Demystify AI models' decision-making process in detail thus fostering trust and enabling informed decisions.

The Model Assessment methodology contains the following key steps: Orientation and Information Gathering, Leadership introduction to Responsible AI followed by data, model, and business strategy understanding.

1.Model Evaluation and Validation: Perform comprehensive AI bias assessment, proxy bias reviews, Exploratory data analysis, model performance analysis, core explainabiltiy analysis, comprehensive “what if” analysis, data and concept drift detection, AI control risk assessment, and compliance assessment based on applicable AI regulations. .

2.Recommendations and Reporting: Deliver a detailed AI Risk assessment report including the executive summary, scope, evaluation methodology, techniques and tools used, results, interpretation of results, AI compliance posture, our conclusions and recommendations. Furthermore, remediation plans to enable fairness and compliance are provided.

Key Deliverables:

•Phase 1: Review of materials, on-site meeting, discussion of scope, deliverables, and assessment timelines.
•Phase 2: Comprehensive AI Model assessment, validation, identification of risks and remediation recommendations. •Phase 3: Detailed assessment report, on-site report presentation, letter of validation, walk through of remediation plans across all Responsible AI pillars including Bias, explainability, ML Operations, AI compliance and AI Robustness.

Overall, this assessment offers a comprehensive approach to responsible AI development and deployment, enabling assurance on AI compliance, AI fairness, and AI explainability leveraging Azure hosted Quasar Responsible AI platform.