Michael Hind
@michaelhind.bsky.social
IBM Distinguished RSM, working on AI transparency, governance, explainability, and fairness. Proud husband & dad, Soccer lover. Posts are my own.
"... We'd love your feedback! Try the code, explore the Hugging Face space, and join us in building a stronger governance framework for AI."
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February 28, 2025 at 8:34 PM
"... We'd love your feedback! Try the code, explore the Hugging Face space, and join us in building a stronger governance framework for AI."
www.linkedin.com/posts/elizab...
www.linkedin.com/posts/elizab...
What are the desirable properties of AI metrics for such tests? What about summarizing these metrics for non-technical stakeholders?
December 7, 2024 at 2:34 AM
What are the desirable properties of AI metrics for such tests? What about summarizing these metrics for non-technical stakeholders?
... or when a physician tries to diagnose the health of a new patient by performing various diagnostic medical tests (blood tests, x-rays, etc).
What happens when one applies these ideas to AI models? How can it be helpful? How can it be misleading? What role could this play in regulations?
What happens when one applies these ideas to AI models? How can it be helpful? How can it be misleading? What role could this play in regulations?
December 7, 2024 at 2:31 AM
... or when a physician tries to diagnose the health of a new patient by performing various diagnostic medical tests (blood tests, x-rays, etc).
What happens when one applies these ideas to AI models? How can it be helpful? How can it be misleading? What role could this play in regulations?
What happens when one applies these ideas to AI models? How can it be helpful? How can it be misleading? What role could this play in regulations?
The work explores the challenges of testing for AI risks without have any information of how the model was developed, such as when one purchases a model from a 3rd party or open source. Similar to how a home inspector is asked to inspect a home without knowing its construction history.
December 7, 2024 at 2:29 AM
The work explores the challenges of testing for AI risks without have any information of how the model was developed, such as when one purchases a model from a 3rd party or open source. Similar to how a home inspector is asked to inspect a home without knowing its construction history.