Scholars including HAI faculty affiliates @MichelleM_Mello and @drnigam underscore the importance of an ethical review process of AI in medicine in a commentary on @JAMA_current
. @StanfordHP @StanfordMed
POLICY
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Ethical Review Process for AI in Medicine Emphasized
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Inclusive AI Regulation: Beyond White Male Definitions
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Btw any regulation not including – explicitly – women, POCs, the indigenous and marginalized is not “safe” by any definition. “Beneficence” as defined by 37 white guys creating the tech they’re non-regulating is a HARM.
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Financial Surveillance and Compliance in Digital Transactions
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“Why genius?” When you think of the legs of this trade that settle in the surveilled financial ecosystem, they look like “Someone who works in Seattle for Amazon sent a large but explicable amount of money to a tax-paying business matching an invoice for a very legal product.”
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AI Wellbeing Frameworks: Prioritizing Human and Environmental Rights
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If you get the chance, this article featuring wellbeing frameworks shows a sort of “model card for societal issues” logic is like to explore with you all, re AI: https://
whatworkswellbeing.org/blog/oecd-and-
wellbeing-frameworks-from-inspiration-to-facilitation-and-collaboration/
…. AI must prioritize human and environmental rights / flourishing. Thanks! -
Tech Companies’ AI Ethics Gap: Policy vs Practice Analysis
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New policy brief: Tech companies often “talk the talk” of AI ethics without fully “walking the walk.” Our empirical investigation into AI ethics on the ground highlights the stark gap between company policy and practice in this field. @sannasideup https://
hai.stanford.edu/policy-brief-w
alking-walk-ai-ethics-technology-companies
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New Dataset Measures Discrimination Across 70 Language Model Applications
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We’re releasing a new dataset for measuring discrimination across 70 different potential applications of language models, including loan applications, visa approvals, and security clearances.
We’ve used this dataset to measure discrimination in LMs and develop new mitigations. -
Language Models Risk Assessment High-Risk Automated Decision Making
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While we don’t permit or endorse the use of LMs for high-risk automated decision making, it’s crucial to anticipate the potential societal impacts and risks of these models as early as possible.
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AI Executive Order: Workforce Gaps Challenge Implementation Progress
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Workforce gaps remain one of the biggest challenges in implementing the @POTUS AI executive order. Among the requirements, workforce policy generated the most to-do items, according to a new tracker by @stanfordhai
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Standards and Benchmarks for AI Safety and Innovation
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it connects the Safety & Responsibility crowd with the Model/Data Innovation crowd, and tries to establish standards and benchmarks that these two sets of crowds can agree are good.
Think of it as establishing a standard that you can subscribe to if it benefits your cause — -
Commercializing Superior LLM Models: Safety Certification Strategy
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You've created a superior llama/mistral-derivative model (like @teknium often does).
How can you convince the world to use it (and pay you)? Step 1: You need a 3rd party to approve that this model is safe and responsible.
the Purple Llama project starts to bridge this gap!