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Evidence-Based Policy Making is Particularly Important in Managing Radio Frequency Spectrum



Screenshot from one of the Silicon Flatirons panels

October 23, 2020 – Evidence-based policy making needs to be framed by the correct questions, agreed panelists at the Silicon Flatirons event on October 13 and 15.

In the first panel, “Evidence-based policy making in perspective,” Adam Scott, director general of spectrum policy at Innovation, Science and Economic Development in Canada, contrasted the questions, “Should we make broadband a human right?” with, “What are the social and economic benefits of connecting a community that hasn’t been connected yet?”

He asserted that the first question is more philosophical and doesn’t directly ask for data, while the second question can be answered very succinctly with data.

Marrying data and decision-making is the best way to think about evidence-based policy making, said Renee Gregory, senior regulatory affairs advisor at Google and moderator of a session on spectrum sharing. She was speaking about work by Thyaga Nandagopal of the National Science Foundation, who had discussed innovate the current spectrum allocation model.

Additionally, evidence-based policy making does not rely on data gathered to answer funded questions, said Blair Levin, nonresident senior fellow of the Metropolitan Policy Program at the Brookings Institution.

Levin did allow that to make room for innovation it was sometimes difficult to make policies based on evidence. He cited the theoretical work the FCC did when they made the spectrum policy auctions, pointing out that work wasn’t evidence-based because nothing like that had been done before.

How legislators view evidence-based data

Kate O’Connor, member of the chief telecom counsel’s office for the U.S. House Energy and Commerce Committee, said that in a world of information overload, nearly every person could find information to support their position. Therefore, data needed to be considered holistically.

O’Connor said the communications space was unique because it was so new. The spectrum crunch is a lot different than in the past, and the private sector has more resources than the government in some cases.

There’s bipartisan consensus that the FCC hasn’t done a good job of collecting data, said Levin. He suggested having real experts used to looking at data examine the types of data needed for effective spectrum policy.

Scott Wallsten, president and senior fellow at Technology Policy Institute, said that a lot of the FCC’s data collection methods are really antiquated. He said we should be supplementing our data with surveys like the ones in the Bureau of Labor Statistics and added that it would be nice see the two agencies work together better. He also advocated for transparency in data submission, saying transparency allowed for contextual data interpretation.

Giulia McHenry, chief of the office of economics and analytics at the Federal Communications Commission, agreed that transparency helps to remove biases when examining evidence.

Others stress the need for enforcement in spectrum management

Dale Hatfield, spectrum policy initiative co-director and distinguished advisor at Silicon Flatirons, said in a later event that evidence-based policy making could prove futile without proper enforcement, and said the FCC should delegate some of their statutory power to private industry.

The better the hypothesis, the lower the cost and burden on a company like Hawkeye to help, said Chris Tourigny, electronics engineer at the Federal Aviation Administration, at the “Spectrum Sharing Policy among Active and Passive Service” panel on Thursday.

Panelists Jennifer Manner, senior vice president of regulatory affairs at EchoStar Corporation, and Ashley Zauderer, program director in the division of astronomical sciences at NSF, emphasized the need for being open to amending data along the way.

The importance of continued communication in policy making was also discussed. Stefanie Tompkins, vice president for research and technology transfer at the Colorado School of Mines, shared that years ago they worked with a communications company to get a software package for multipath technology.

They found many of their signals bouncing and going longer than thought they should, which “led to many middle of the night panic attacks.” The communications company had rounded the speed of light—a cultural mismatch that led to a lot of mistakes. Tompkins said this experience applies to how we interpret facts.

David Redl, of Salt Point Strategies, moderated the first policy making panel.

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Artificial Intelligence

Companies Must Be Transparent About Their Use of Artificial Intelligence

Making the use of AI known is key to addressing any pitfalls, researchers said.



WASHINGTON, September 20, 2023 – Researchers at an artificial intelligence workshop Tuesday said companies should be transparent about their use of algorithmic AI in things like hiring processes and content writing. 

Andrew Bell, a fellow at the New York University Center for Responsible AI, said that making the use of AI known is key to addressing any pitfalls AI might have. 

Algorithmic AI is behind systems like chatbots which can generate texts and answers to questions. It is used in hiring processes to quickly screen resumes or in journalism to write articles. 

According to Bell, ‘algorithmic transparency’ is the idea that “information about decisions made by algorithms should be visible to those who use, regulate, and are affected by the systems that employ those algorithms.”

The need for this kind of transparency comes after events like Amazons’ old AI recruiting tool showed bias toward women in the hiring process, or when OpenAI, the company that created ChatGPT, was probed by the FTC for generating misinformation. 

Incidents like these have brought the topic of regulating AI and making sure it is transparent to the forefront of Senate conversations.

Senate committee hears need for AI regulation

The Senate’s subcommittee on consumer protection on September 12 heard about proposals to make AI use more transparent, including disclaiming when AI is being used and developing tools to predict and understand risk associated with different AI models.

Similar transparency methods were mentioned by Bell and his supervisor Julia Stoyanovich, the Director of the Center for Responsible AI at New York University, a research center that explores how AI can be made safe and accessible as the technology evolves. 

According to Bell, a transparency label on algorithmic AI would “[provide] insight into ingredients of an algorithm.” Similar to a nutrition label, a transparency label would identify all the factors that go into algorithmic decision making.  

Data visualization was another option suggested by Bell, which would require a company to put up a public-facing document that explains the way their AI works, and how it generates the decisions it spits out. 

Adding in those disclaimers creates a better ecosystem between AI and AI users, increasing levels of trust between all stakeholders involved, explained Bell.

Bell and his supervisor built their workshop around an Algorithm Transparency Playbook, a document they published that has straightforward guidelines on why transparency is important and ways companies can go about it. 

Tech lobbying groups like the Computer and Communications Industry Association, which represent Big Tech companies, however, have spoken out in the past against the Senate regulating AI, claiming that it could stifle innovation. 

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Artificial Intelligence

Congress Should Mandate AI Guidelines for Transparency and Labeling, Say Witnesses

Transparency around data collection and risk assessments should be mandated by law, especially in high-risk applications of AI.



Screenshot of the Business Software Alliance's Victoria Espinel at the Commerce subcommittee hearing

WASHINGTON, September 12, 2023 – The United States should enact legislation mandating transparency from companies making and using artificial intelligence models, experts told the Senate Commerce Subcommittee on Consumer Protection, Product Safety, and Data Security on Tuesday.

It was one of two AI policy hearings on the hill Tuesday, with a Senate Judiciary Committee hearing, as well as an executive branch meeting created under the National AI Advisory Committee.

The Senate Commerce subcommittee asked witnesses how AI-specific regulations should be implemented and what lawmakers should keep in mind when drafting potential legislation. 

“The unwillingness of leading vendors to disclose the attributes and provenance of the data they’ve used to train models needs to be urgently addressed,” said Ramayya Krishnan, dean of Carnegie Mellon University’s college of information systems and public policy.

Addressing problems with transparency of AI systems

Addressing the lack of transparency might look like standardized documentation outlining data sources and bias assessments, Krishnan said. That documentation could be verified by auditors and function “like a nutrition label” for users.

Witnesses from both private industry and human rights advocacy agreed legally binding guidelines – both for transparency and risk management – will be necessary. 

Victoria Espinel, CEO of the Business Software Alliance, a trade group representing software companies, said the AI risk management framework developed in March by the National Institute of Standards and Technology was important, “but we do not think it is sufficient.”

“We think it would be best if legislation required companies in high-risk situations to be doing impact assessments and have internal risk management programs,” she said.

Those mandates – along with other transparency requirements discussed by the panel – should look different for companies that develop AI models and those that use them, and should only apply in the most high-risk applications, panelists said.

That last suggestion is in line with legislation being discussed in the European Union, which would apply differently depending on the assessed risk of a model’s use.

“High-risk” uses of AI, according to the witnesses, are situations in which an AI model is making consequential decisions, like in healthcare, hiring processes, and driving. Less consequential machine-learning models like those powering voice assistants and autocorrect would be subject to less government scrutiny under this framework.

Labeling AI-generated content

The panel also discussed the need to label AI-generated content.

“It is unreasonable to expect consumers to spot deceptive yet realistic imagery and voices,” said Sam Gregory, director of human right advocacy group WITNESS. “Guidance to look for a six fingered hand or spot virtual errors in a puffer jacket do not help in the long run.”

With elections in the U.S. approaching, panelists agreed mandating labels on AI-generated images and videos will be essential. They said those labels will have to be more comprehensive than visual watermarks, which can be easily removed, and might take the form of cryptographically bound metadata.

Labeling content as being AI-generated will also be important for developers, Krishnan noted, as generative AI models become much less effective when trained on writing or images made by other AIs.

Privacy around these content labels was a concern for panelists. Some protocols for verifying the origins of a piece of content with metadata require the personal information of human creators.

“This is absolutely critical,” said Gregory. “We have to start from the principle that these approaches do not oblige personal information or identity to be a part of them.”

Separately, the executive branch committee that met Tuesday was established under the National AI Initiative Act of 2020, is tasked with advising the president on AI-related matters. The NAIAC gathers representatives from the Departments of State, Defense, Energy and Commerce, together with the Attorney General, Director of National Intelligence, and Director of Science and Technology Policy.

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Artificial Intelligence

Tech Policy Group CCIA Speaks Out Against AI Regulation

The trade group represents major tech companies like Amazon and Google.



WASHINGTON, September 12, 2023 – A policy director at the Computer and Communications Industry Association spoke out on Tuesday against impending artificial intelligence regulations in the European Union and United States.

The CCIA represents some of the biggest tech companies in the world, with members including Amazon, Google, Meta, and Apple.

“The E.U. approach will focus very much on the technology itself, rather than the use of it, which is highly problematic,” said Boniface de Champris, CCIA’s Europe policy manager, at a panel hosted by the Cato Institute. “The requirements would basically inhibit the development and use of cutting edge technology in the E.U.”

This echoes de Champris’s American counterparts, who have argued in front of Congress that AI-specific laws would stifle innovation.

The European Parliament is aiming to reach an agreement by the end of the year on the AI Act, which would put regulations on all AI systems based on their assessed risk level. 

The E.U. also adopted in August the Digital Services Act, legislation that tightens privacy rules and expands transparency requirements. Under the law, users can opt to turn off artificial intelligence-enabled content recommendation.

U.S. President Joe Biden announced in July that seven major AI and tech companies – including CCIA members Amazon, Meta, and Google – made voluntary commitments to various AI safeguards, including information sharing and security testing.

Multiple U.S. agencies are exploring more binding AI regulation. Both the Senate Judiciary committee and Senate consumer protection subcommittee held hearings on potential AI policy later on Tuesday. The judiciary hearing will include testimony from Microsoft president Brad Smith and AI and graphics company NVIDIA’s chief scientist William Daly.

The House Energy and Commerce Committee passed in July the Artificial Intelligence Accountability Act, which gives the National Telecommunications and Information Administration a mandate to study accountability measures for artificial intelligence systems used by telecom companies.

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