Transparency Coalition.ai

In a significant milestone for AI safety and transparency, the California legislature yesterday gave final approval to AB 2013, the Training Data Transparency Act. The Act, authored by Assemblymember Jacqui Irwin, represents one of the first serious efforts to enact safeguards around the data used to train AI models. The Assembly approved the bill on a vote of 75-0, following the Senate’s 38-0 approval on Monday. AB 2013 has been one of the Transparency Coalition’s top priority bills in the California legislature this year. TCAI founders Jai Jaisimha and Rob Eleveld offered expert testimony at committee hearings and worked closely with the bill’s sponsors, offering suggestions to strengthen and clarify the bill’s intentions. The approved bill now awaits the signature of Gov. Gavin Newsom. #AI #AIregulation #California #data #artificialintelligence

Introducing "AI Field Notes," an occasional column by Transparency Coalition editor Bruce Barcott covering movements and trends in AI policy. It's crunch time in California, and we're following along with the latest.

In a significant milestone for AI safety and transparency, the California legislature yesterday gave final approval to AB 2013, the Training Data Transparency Act. The Act, authored by Assemblymember Jacqui Irwin, represents one of the first serious efforts to enact safeguards around the data used to train AI models. The Assembly approved the bill on a vote of 75-0, following the Senate’s 38-0 approval on Monday. AB 2013 has been one of the Transparency Coalition’s top priority bills in the California legislature this year. TCAI founders Jai Jaisimha and Rob Eleveld offered expert testimony at committee hearings and worked closely with the bill’s sponsors, offering suggestions to strengthen and clarify the bill’s intentions. The approved bill now awaits the signature of Gov. Gavin Newsom. #AI #AIregulation #California #data #artificialintelligence

In late July 2024, Nature published a paper by Ilia Shumailov and Zakhar Shumaylov that rattled the AI world. The Oxford University computer scientists showed that feeding AI-generated data (known as synthetic data) to an AI model caused subsequent generations of that model to degrade to the point of collapse. This morning, New York Times reporter Aatish Bhatia published a remarkable follow-up to that work. Aatish asked a simple question: What would that erosion look like, exactly?

UW ECE Affiliate Professor Jai Jaisimha was featured in a recent GeekWire article that covered his participation in the PNW Climate Week panel on AI. Training and using AI models consumes a significant amount of energy, and the panel discussed ways to address that issue. Jaisimha emphasized that not every problem requires a complex AI solution. “I do have some substantive concerns about trying to swat flies with laser beams,” Jaisimha said. “AI can be great, but it should be built for the task that you intend to apply it to.” To learn more about AI energy issues, click the link below! #uwece https://lnkd.in/drkjfNhA

Transparency Coalition founders Rob Eleveld and Jai Jaisimha talk California SB 1047 with Tech Policy Press. #ai #airegulation #techpolicy #california #tech https://lnkd.in/e6fTF5Y3

How much energy does AI consume? So much that major AI developers are investing in nuclear power to keep their data centers running. Last week, Transparency Coalition’s Jai Jaisimha joined a panel of experts to discuss the intersection of AI and climate change as part of PNW Climate Week. The possibilities were unexpected, surprising, hopeful, and alarming. On hand to tackle the question, “Artificial Intelligence: Friend or Foe for the Planet?” were Heather Redman, Hansi Singh, PhD, Sailesh Chutani, Vikram Modgil, and Jordan Bain. It was a terrific discussion, a preview of policy issues on the horizon. #artificialintelligence #AI #climateweek #climate

Americans depend on ingredients labels for food safety. They deserve the same from their AI. As the need for AI training data transparency gains traction in policy circles, some of the most powerful AI companies are pushing back. They argue that basic descriptive information about the data used to train AI models constitutes proprietary information. In other words, they claim it’s a trade secret. In fact, it’s not. #AI #data #transparency #AIregulation

Americans depend on ingredients labels for food safety. They deserve the same from their AI. As the need for AI training data transparency gains traction in policy circles, some of the most powerful AI companies are pushing back. They argue that basic descriptive information about the data used to train AI models constitutes proprietary information. In other words, they claim it’s a trade secret. In fact, it’s not. #AI #data #transparency #AIregulation

The Transparency Coalition.ai is proud to support the Promoting Responsible Evaluation and Procurement to Advance Readiness for Enterprise-Wide Deployment (PREPARED) for AI Act. Read more in this piece by Ben Wodecki at AI Business. https://lnkd.in/ezgnwbNp