Managing bias in an AI system is critical to establishing and maintaining trust in its operation. Despite its importance, bias in AI systems remains endemic across many application domains and can lead to harmful impacts regardless of intent. Bias is also context-dependent. To tackle this complex problem, we adopt a socio-technical approach to testing, evaluation, verification, and validation (TEVV) of AI systems in context. This approach connects the technology to societal values in order to develop recommended guidance for deploying AI/ML-based decision-making applications in a sector of the industry. This project will also look at the interplay between bias and cybersecurity. The project will leverage existing commercial and open-source technology in conjunction with the NIST Dioptra, an experimentation test platform for ML datasets and models. The initial phase of the project will focus on a proof-of-concept implementation for credit underwriting decisions in the financial services sector. This project will result in a freely available NIST AI/ML Practice Guide.
Managing bias in an AI system is critical to establishing and maintaining trust in its operation. Despite its importance, bias in AI systems remains endemic across many application domains and can lead to harmful impacts regardless of intent. Bias is also context-dependent. To tackle this complex...
See full abstract
Managing bias in an AI system is critical to establishing and maintaining trust in its operation. Despite its importance, bias in AI systems remains endemic across many application domains and can lead to harmful impacts regardless of intent. Bias is also context-dependent. To tackle this complex problem, we adopt a socio-technical approach to testing, evaluation, verification, and validation (TEVV) of AI systems in context. This approach connects the technology to societal values in order to develop recommended guidance for deploying AI/ML-based decision-making applications in a sector of the industry. This project will also look at the interplay between bias and cybersecurity. The project will leverage existing commercial and open-source technology in conjunction with the NIST Dioptra, an experimentation test platform for ML datasets and models. The initial phase of the project will focus on a proof-of-concept implementation for credit underwriting decisions in the financial services sector. This project will result in a freely available NIST AI/ML Practice Guide.
Hide full abstract
) or https:// don't prove our archive is authentic, only that you securely accessed it. Note that we are working to fix that :)
