Oct 27th - Nov 17th
Responsible AI with External Data and AI Quality Management
How to use Third Party Data and AI Quality Management tools to expand opportunities and build responsible AI.
How can you use 3rd party data to your advantage?
External data can be a key part of driving new and effective Machine Learning (ML) use cases. However, the use of third-party data is often held back by concerns about data quality and risk.
TruEra and Demyst collaborated on a project for the Veritas responsible AI initiative to demonstrate how these concerns can be easily overcome. That project won the AI challenge at the world’s largest fintech event in 2021. This whitepaper showcases that project’s key learnings.
- What are the challenges and concerns about using 3rd party data, and how can you overcome them?
- How does AI Quality Management help you manage model performance and data quality?
- How should you evaluate a 3rd party data provider?
- Checklist: 10 key points for managing 3rd party data for machine learning
- Case study: Learn how 3rd party data and AI Quality Management led to fairer, more accurate models for credit decisioning. This project won the responsible AI challenge at the world’s largest fintech event in 2021.
Read the whitepaper now
TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.