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Nov 12, 2024 | 
itif.org | Hodan Omaar
As President-elect Trump begins shaping his AI policy, his focus appears to be on unwinding President Biden’s approach to AI governance and enlisting figures like Elon Musk, who aims to address distant threats from superintelligent systems.
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Nov 4, 2024 | 
datainnovation.org | Hodan Omaar
California often sets the tone for national policy, but the recently passed AI Transparency Act (SB 942) is an example of what not to do. Despite its well-intentioned aim to address harmful AI-generated content, the law misunderstands both the mechanics of AI and how people respond to deceptive media. Instead of offering practical solutions, this law imposes impractical burdens on users and providers, stifles innovation, and falls far short of its promises.
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Oct 11, 2024 | 
itif.org | Hodan Omaar
On behalf of the Center for Data Innovation, we are pleased to submit this response to the Bureau of Industry and Security's (BIS) proposed rule to establish reporting requirements for the development of advanced artificial intelligence (AI) models and computing clusters.[1]The Center for Data Innovation studies the intersection of data, technology, and public policy.
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Oct 10, 2024 | 
itif.org | Hodan Omaar
|October 10, 2024Responding to an editorial in the Financial Times about Governor Gavin Newsom’s veto of California’s artificial intelligence safety bill (SB 1047), Hodan Omaar writes that the FT board is correct that getting AI regulation right is worth the effort.
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Oct 4, 2024 | 
itif.org | Hodan Omaar
On behalf of the Center for Data Innovation, we are pleased to submit this response to theDepartment of Industry, Science and Resources’ proposal paper, “Introducing mandatory guardrails for AI in high-risk settings.”[1]The Center for Data Innovation studies the intersection of data, technology, and public policy. Its mission is to formulate and promote pragmatic public policies designed to maximize the benefits of data-driven innovation in the public and private sectors.
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Oct 2, 2024 | 
itif.org | Hodan Omaar
Governor Gavin Newsom’s veto of California’s AI safety bill (SB 1047) was a sensible move, but a deeper issue remains unresolved. A fundamental flaw of the bill lies in its approach to AI safety: it attempts to identify risky systems based solely on assessments made before deployment. Even with the most rigorous pre-deployment evaluations, fully anticipating the harm these AI models might cause is impossible because their impact greatly depends on how they’re applied in real-world settings.
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Sep 24, 2024 | 
itif.org | Hodan Omaar
|September 24, 2024As the United States commands the field with English-language large language models (LLMs) and China climbs the ranks to secure the lead in models for Chinese-language tasks, Europe is barely in the game when it comes to mastering models for its own languages. Of the top open-source multilingual models designed to handle non-English European languages, hardly any are from European companies.
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Sep 23, 2024 | 
datainnovation.org | Hodan Omaar
As the United States commands the field with English-language large language models (LLMs) and China climbs the ranks to secure the lead in models for Chinese-language tasks, Europe is barely in the game when it comes to mastering models for its own languages. Of the top open-source multilingual models designed to handle non-English European languages, hardly any are from European companies.
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Sep 6, 2024 | 
datainnovation.org | Hodan Omaar
The Center for Data Innovation submitted comments to the U.S. Artificial Intelligence Safety Institute (AISI) on its draft guidelines, Managing Misuse Risk for Dual-Use Foundation Models (NIST AI 800-1). We commend AISI for its proactive best practices to prevent misuse of foundation models but recommend four improvements:Differentiate between predictable risks (e.g., drug development) and context-dependent risks (e.g., AI chatbots).
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Sep 6, 2024 | 
itif.org | Hodan Omaar
|September 6, 2024The Center for Data Innovation submitted comments to the U.S. Artificial Intelligence Safety Institute (AISI) on its draft guidelines, Managing Misuse Risk for Dual-Use Foundation Models (NIST AI 800-1). We commend AISI for its proactive best practices to prevent misuse of foundation models but recommend four improvements:Differentiate between predictable risks (e.g., drug development) and context-dependent risks (e.g., AI chatbots).
