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Artificial Intelligence

AI Likely to Bring Changes to Warfare, Including Potential De-escalation of Military Conflict, Say Panelists

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WASHINGTON, May 30, 2019 – The development of artificial intelligence will bring extreme changes to the future of warfare, a panel of scientists said Thursday, calling the impact of current advances analogous to the development of agriculture or the domestication of the horse.

The panel was hosted by the Hudson Institute, a conservative think tank founded by military and industrial strategist Herman Kahn. Speakers on the panel discussed the ways in which the Department of Defense can implement new technologies, as well as the problems that could arise as a result.

One common concern with AI in military decisions was the potentially faster escalation in the use of force. For example, during the Cuban Missile Crisis, AI might have recommended acting sooner, possibly leading to catastrophic results.

But Navy AI Lead Colonel Jeff Kojac argued that the opposite could also be true: A young platoon commander in a high-pressure situation could utilize the help of an unmanned aerial system in determining to not open fire on a non-combative group.

Additionally, Lindsey R. Sheppard, associate fellow at the Center for Strategic and International Studies, refuted this fear by explaining that a significant amount of cognitive psychology research demonstrates that more information does not necessarily lead to a faster decision.

Hudson Senior Fellow William Schneider Jr. also thought that the potential benefits outweighed the risks, pointing out that AI gives the military the opportunity to head off a crisis before it occurs.

In regard to 5G networks, Schneider claimed that they present a “substantial” risk because of what can be integrated into the technology. He cited a recent Human Rights Watch report describing a mass surveillance app that collects an “intrusive, massive collection of personal information.” Having a large inventory of data-based services presents a wide range of potential breaches.

The panelists also discussed how to mitigate the consequences of AI’s current limitations and vulnerabilities. Sheppard emphasized the importance of placing computing data as far out on the network’s edge as possible.

For example, Apple’s facial recognition technology used to send the captured image to a central server, compare it to a stored image, and send it back; this entire process is now done on the device itself, freeing important server space. This model could be applied to the structure of cloud architecture in military settings as well.

Dr. Alexander Kott, chief scientist for the Army Research Laboratory, described the need for a complex mix of decentralized clouds at the edge, making them more resilient to attack. Col. Kojac pointed out that an additional component of resilience is agility, recommending an incremental approach to developing these technologies over the more traditional “waterfall” approach.

Not only will the technology require agility, the people operating it will need to be flexible in order to make the rise of AI feasible. That barrier was highlighted by several audience members, too. Kojac called an AI literate force a “categorical imperative,” and Sheppard supported this idea by suggesting that all forces involved in the deployment of these technologies should be required to know how to program.

This should be made easier because the workforce currently entering the military is fundamentally different from what it was a decade ago. Troops now serve for longer periods of time and have higher education requirements. Additionally, many have a more technologically rich background, such that Schneider called them “digital natives.” He said that AI ultimately provides a “basis for optimism” for having the potential to save lives on the front lines.

On a civilian level, Sheppard also highlighted the need for a top to bottom recognition of the importance of analytics within company cultures.

(Photo of panelists at the Hudson Institute event by Drew Clark.)

 

Reporter Em McPhie studied communication design and writing at Washington University in St. Louis, where she was a managing editor for the student newspaper. In addition to agency and freelance marketing experience, she has reported extensively on Section 230, big tech, and rural broadband access. She is a founding board member of Code Open Sesame, an organization that teaches computer programming skills to underprivileged children.

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.

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https://engineering.nyu.edu/news/we-are-ai-series-nyu-tandon-center-responsible-ai-queens-public-library

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.

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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.

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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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