HomeMicrosoft unveils tool for developers to create AI behavior tests from text descriptions
AI researchers and labs have made significant progress in evaluating AI models for safety, compliance, sycophancy, and alignment. However, companies and developers now face a specific challenge: ensuring their AI systems behave exactly as intended for their particular product or service.
To simplify that testing process, Microsoft unveiled ASSERT on Tuesday—an acronym for Adaptive Spec-driven Scoring for Evaluation and Regression Testing.
According to Microsoft, the open-source framework makes it easy to evaluate application-specific AI behavior. It uses AI to convert high-level, natural-language descriptions of goals, policies, or intended behaviors into comprehensive, scored tests that can be examined.
ASSERT accepts plain-language descriptions of an AI model's expected behavior and policies, then transforms them into a structured set of acceptable and unacceptable behaviors. It generates problem scenarios and test cases, runs them against the target system, and scores the results. The framework also records the AI system's paths, including intermediate actions and tool calls, allowing developers to inspect where failures occur.
Developers can also provide system context, tools, and constraints to further customize what the evaluations cover.
For instance, a developer could specify that a document research AI agent should not email people outside the company, must restrict confidential information to C-level executives, and should provide concise summaries while considering prior context. ASSERT then uses those rules to generate test cases that continuously check whether the system follows them.
Image Credits:Microsoft
According to Microsoft, the framework fills a gap that broader, more general evaluations cannot address when AI models are intended to behave in ways shaped by an application or product's context, policies, and tools.
“One of the things we’ve learned is that evaluations are absolutely critical to making good decisions,” said Sarah Bird, chief product officer of Responsible AI at Microsoft. “Because if you don’t understand the behavior of the AI system, it’s really hard to know if it’s meeting your organization’s bar […] What we found is that if you really want a trustworthy system, you should evaluate many more dimensions that are application-specific.”
Bird said ASSERT can be used to evaluate systems during development, after deployment, and for continuous monitoring.
The release comes amid a gradual yet broader shift in the AI industry. As models become more capable, researchers are focusing on repeatable testing and regression checks. Stanford's HELM, MLCommons' AILuminate, and evaluation groups like METR are rolling out benchmarks to measure how models behave under different conditions.
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AI researchers and labs have made significant progress in evaluating AI models for safety, compliance, sycophancy, and alignment. However, companies and developers now face a specific challenge: ensuring their AI systems behave exactly as intended for their particular product or service.
To simplify that testing process, Microsoft unveiled ASSERT on Tuesday—an acronym for Adaptive Spec-driven Scoring for Evaluation and Regression Testing.
According to Microsoft, the open-source framework makes it easy to evaluate application-specific AI behavior. It uses AI to convert high-level, natural-language descriptions of goals, policies, or intended behaviors into comprehensive, scored tests that can be examined.
ASSERT accepts plain-language descriptions of an AI model's expected behavior and policies, then transforms them into a structured set of acceptable and unacceptable behaviors. It generates problem scenarios and test cases, runs them against the target system, and scores the results. The framework also records the AI system's paths, including intermediate actions and tool calls, allowing developers to inspect where failures occur.
Developers can also provide system context, tools, and constraints to further customize what the evaluations cover.
For instance, a developer could specify that a document research AI agent should not email people outside the company, must restrict confidential information to C-level executives, and should provide concise summaries while considering prior context. ASSERT then uses those rules to generate test cases that continuously check whether the system follows them.
Image Credits:Microsoft
According to Microsoft, the framework fills a gap that broader, more general evaluations cannot address when AI models are intended to behave in ways shaped by an application or product's context, policies, and tools.
“One of the things we’ve learned is that evaluations are absolutely critical to making good decisions,” said Sarah Bird, chief product officer of Responsible AI at Microsoft. “Because if you don’t understand the behavior of the AI system, it’s really hard to know if it’s meeting your organization’s bar […] What we found is that if you really want a trustworthy system, you should evaluate many more dimensions that are application-specific.”
Bird said ASSERT can be used to evaluate systems during development, after deployment, and for continuous monitoring.
The release comes amid a gradual yet broader shift in the AI industry. As models become more capable, researchers are focusing on repeatable testing and regression checks. Stanford's HELM, MLCommons' AILuminate, and evaluation groups like METR are rolling out benchmarks to measure how models behave under different conditions.
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