Tag Archives: public interest

AI as Sensemaking for Public Comments

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/06/ai-as-sensemaking-for-public-comments.html

It’s become fashionable to think of artificial intelligence as an inherently dehumanizing technology, a ruthless force of automation that has unleashed legions of virtual skilled laborers in faceless form. But what if AI turns out to be the one tool able to identify what makes your ideas special, recognizing your unique perspective and potential on the issues where it matters most?

You’d be forgiven if you’re distraught about society’s ability to grapple with this new technology. So far, there’s no lack of prognostications about the democratic doom that AI may wreak on the US system of government. There are legitimate reasons to be concerned that AI could spread misinformation, break public comment processes on regulations, inundate legislators with artificial constituent outreach, help to automate corporate lobbying, or even generate laws in a way tailored to benefit narrow interests.

But there are reasons to feel more sanguine as well. Many groups have started demonstrating the potential beneficial uses of AI for governance. A key constructive-use case for AI in democratic processes is to serve as discussion moderator and consensus builder.

To help democracy scale better in the face of growing, increasingly interconnected populations—as well as the wide availability of AI language tools that can generate reams of text at the click of a button—the US will need to leverage AI’s capability to rapidly digest, interpret and summarize this content.

There are two different ways to approach the use of generative AI to improve civic participation and governance. Each is likely to lead to drastically different experience for public policy advocates and other people trying to have their voice heard in a future system where AI chatbots are both the dominant readers and writers of public comment.

For example, consider individual letters to a representative, or comments as part of a regulatory rulemaking process. In both cases, we the people are telling the government what we think and want.

For more than half a century, agencies have been using human power to read through all the comments received, and to generate summaries and responses of their major themes. To be sure, digital technology has helped.

In 2021, the Council of Federal Chief Data Officers recommended modernizing the comment review process by implementing natural language processing tools for removing duplicates and clustering similar comments in processes governmentwide. These tools are simplistic by the standards of 2023 AI. They work by assessing the semantic similarity of comments based on metrics like word frequency (How often did you say “personhood”?) and clustering similar comments and giving reviewers a sense of what topic they relate to.

Think of this approach as collapsing public opinion. They take a big, hairy mass of comments from thousands of people and condense them into a tidy set of essential reading that generally suffices to represent the broad themes of community feedback. This is far easier for a small agency staff or legislative office to handle than it would be for staffers to actually read through that many individual perspectives.

But what’s lost in this collapsing is individuality, personality, and relationships. The reviewer of the condensed comments may miss the personal circumstances that led so many commenters to write in with a common point of view, and may overlook the arguments and anecdotes that might be the most persuasive content of the testimony.

Most importantly, the reviewers may miss out on the opportunity to recognize committed and knowledgeable advocates, whether interest groups or individuals, who could have long-term, productive relationships with the agency.

These drawbacks have real ramifications for the potential efficacy of those thousands of individual messages, undermining what all those people were doing it for. Still, practicality tips the balance toward of some kind of summarization approach. A passionate letter of advocacy doesn’t hold any value if regulators or legislators simply don’t have time to read it.

There is another approach. In addition to collapsing testimony through summarization, government staff can use modern AI techniques to explode it. They can automatically recover and recognize a distinctive argument from one piece of testimony that does not exist in the thousands of other testimonies received. They can discover the kinds of constituent stories and experiences that legislators love to repeat at hearings, town halls and campaign events. This approach can sustain the potential impact of individual public comment to shape legislation even as the volumes of testimony may rise exponentially.

In computing, there is a rich history of that type of automation task in what is called outlier detection. Traditional methods generally involve finding a simple model that explains most of the data in question, like a set of topics that well describe the vast majority of submitted comments. But then they go a step further by isolating those data points that fall outside the mold—comments that don’t use arguments that fit into the neat little clusters.

State-of-the-art AI language models aren’t necessary for identifying outliers in text document data sets, but using them could bring a greater degree of sophistication and flexibility to this procedure. AI language models can be tasked to identify novel perspectives within a large body of text through prompting alone. You simply need to tell the AI to find them.

In the absence of that ability to extract distinctive comments, lawmakers and regulators have no choice but to prioritize on other factors. If there is nothing better, “who donated the most to our campaign” or “which company employs the most of my former staffers” become reasonable metrics for prioritizing public comments. AI can help elected representatives do much better.

If Americans want AI to help revitalize the country’s ailing democracy, they need to think about how to align the incentives of elected leaders with those of individuals. Right now, as much as 90% of constituent communications are mass emails organized by advocacy groups, and they go largely ignored by staffers. People are channeling their passions into a vast digital warehouses where algorithms box up their expressions so they don’t have to be read. As a result, the incentive for citizens and advocacy groups is to fill that box up to the brim, so someone will notice it’s overflowing.

A talented, knowledgeable, engaged citizen should be able to articulate their ideas and share their personal experiences and distinctive points of view in a way that they can be both included with everyone else’s comments where they contribute to summarization and recognized individually among the other comments. An effective comment summarization process would extricate those unique points of view from the pile and put them into lawmakers’ hands.

This essay was written with Nathan Sanders, and previously appeared in the Conversation.

AI to Aid Democracy

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/ai-to-aid-democracy.html

There’s good reason to fear that AI systems like ChatGPT and GPT4 will harm democracy. Public debate may be overwhelmed by industrial quantities of autogenerated argument. People might fall down political rabbit holes, taken in by superficially convincing bullshit, or obsessed by folies à deux relationships with machine personalities that don’t really exist.

These risks may be the fallout of a world where businesses deploy poorly tested AI systems in a battle for market share, each hoping to establish a monopoly.

But dystopia isn’t the only possible future. AI could advance the public good, not private profit, and bolster democracy instead of undermining it. That would require an AI not under the control of a large tech monopoly, but rather developed by government and available to all citizens. This public option is within reach if we want it.

An AI built for public benefit could be tailor-made for those use cases where technology can best help democracy. It could plausibly educate citizens, help them deliberate together, summarize what they think, and find possible common ground. Politicians might use large language models, or LLMs, like GPT4 to better understand what their citizens want.

Today, state-of-the-art AI systems are controlled by multibillion-dollar tech companies: Google, Meta, and OpenAI in connection with Microsoft. These companies get to decide how we engage with their AIs and what sort of access we have. They can steer and shape those AIs to conform to their corporate interests. That isn’t the world we want. Instead, we want AI options that are both public goods and directed toward public good.

We know that existing LLMs are trained on material gathered from the internet, which can reflect racist bias and hate. Companies attempt to filter these data sets, fine-tune LLMs, and tweak their outputs to remove bias and toxicity. But leaked emails and conversations suggest that they are rushing half-baked products to market in a race to establish their own monopoly.

These companies make decisions with huge consequences for democracy, but little democratic oversight. We don’t hear about political trade-offs they are making. Do LLM-powered chatbots and search engines favor some viewpoints over others? Do they skirt controversial topics completely? Currently, we have to trust companies to tell us the truth about the trade-offs they face.

A public option LLM would provide a vital independent source of information and a testing ground for technological choices with big democratic consequences. This could work much like public option health care plans, which increase access to health services while also providing more transparency into operations in the sector and putting productive pressure on the pricing and features of private products. It would also allow us to figure out the limits of LLMs and direct their applications with those in mind.

We know that LLMs often “hallucinate,” inferring facts that aren’t real. It isn’t clear whether this is an unavoidable flaw of how they work, or whether it can be corrected for. Democracy could be undermined if citizens trust technologies that just make stuff up at random, and the companies trying to sell these technologies can’t be trusted to admit their flaws.

But a public option AI could do more than check technology companies’ honesty. It could test new applications that could support democracy rather than undermining it.

Most obviously, LLMs could help us formulate and express our perspectives and policy positions, making political arguments more cogent and informed, whether in social media, letters to the editor, or comments to rule-making agencies in response to policy proposals. By this we don’t mean that AI will replace humans in the political debate, only that they can help us express ourselves. If you’ve ever used a Hallmark greeting card or signed a petition, you’ve already demonstrated that you’re OK with accepting help to articulate your personal sentiments or political beliefs. AI will make it easier to generate first drafts, and provide editing help and suggest alternative phrasings. How these AI uses are perceived will change over time, and there is still much room for improvement in LLMs—but their assistive power is real. People are already testing and speculating on their potential for speechwriting, lobbying, and campaign messaging. Highly influential people often rely on professional speechwriters and staff to help develop their thoughts, and AI could serve a similar role for everyday citizens.

If the hallucination problem can be solved, LLMs could also become explainers and educators. Imagine citizens being able to query an LLM that has expert-level knowledge of a policy issue, or that has command of the positions of a particular candidate or party. Instead of having to parse bland and evasive statements calibrated for a mass audience, individual citizens could gain real political understanding through question-and-answer sessions with LLMs that could be unfailingly available and endlessly patient in ways that no human could ever be.

Finally, and most ambitiously, AI could help facilitate radical democracy at scale. As Carnegie Mellon professor of statistics Cosma Shalizi has observed, we delegate decisions to elected politicians in part because we don’t have time to deliberate on every issue. But AI could manage massive political conversations in chat rooms, on social networking sites, and elsewhere: identifying common positions and summarizing them, surfacing unusual arguments that seem compelling to those who have heard them, and keeping attacks and insults to a minimum.

AI chatbots could run national electronic town hall meetings and automatically summarize the perspectives of diverse participants. This type of AI-moderated civic debate could also be a dynamic alternative to opinion polling. Politicians turn to opinion surveys to capture snapshots of popular opinion because they can only hear directly from a small number of voters, but want to understand where voters agree or disagree.

Looking further into the future, these technologies could help groups reach consensus and make decisions. Early experiments by the AI company DeepMind suggest that LLMs can build bridges between people who disagree, helping bring them to consensus. Science fiction writer Ruthanna Emrys, in her remarkable novel A Half-Built Garden, imagines how AI might help people have better conversations and make better decisions—rather than taking advantage of these biases to maximize profits.

This future requires an AI public option. Building one, through a government-directed model development and deployment program, would require a lot of effort—and the greatest challenges in developing public AI systems would be political.

Some technological tools are already publicly available. In fairness, tech giants like Google and Meta have made many of their latest and greatest AI tools freely available for years, in cooperation with the academic community. Although OpenAI has not made the source code and trained features of its latest models public, competitors such as Hugging Face have done so for similar systems.

While state-of-the-art LLMs achieve spectacular results, they do so using techniques that are mostly well known and widely used throughout the industry. OpenAI has only revealed limited details of how it trained its latest model, but its major advance over its earlier ChatGPT model is no secret: a multi-modal training process that accepts both image and textual inputs.

Financially, the largest-scale LLMs being trained today cost hundreds of millions of dollars. That’s beyond ordinary people’s reach, but it’s a pittance compared to U.S. federal military spending—and a great bargain for the potential return. While we may not want to expand the scope of existing agencies to accommodate this task, we have our choice of government labs, like the National Institute of Standards and Technology, the Lawrence Livermore National Laboratory, and other Department of Energy labs, as well as universities and nonprofits, with the AI expertise and capability to oversee this effort.

Instead of releasing half-finished AI systems for the public to test, we need to make sure that they are robust before they’re released—and that they strengthen democracy rather than undermine it. The key advance that made recent AI chatbot models dramatically more useful was feedback from real people. Companies employ teams to interact with early versions of their software to teach them which outputs are useful and which are not. These paid users train the models to align to corporate interests, with applications like web search (integrating commercial advertisements) and business productivity assistive software in mind.

To build assistive AI for democracy, we would need to capture human feedback for specific democratic use cases, such as moderating a polarized policy discussion, explaining the nuance of a legal proposal, or articulating one’s perspective within a larger debate. This gives us a path to “align” LLMs with our democratic values: by having models generate answers to questions, make mistakes, and learn from the responses of human users, without having these mistakes damage users and the public arena.

Capturing that kind of user interaction and feedback within a political environment suspicious of both AI and technology generally will be challenging. It’s easy to imagine the same politicians who rail against the untrustworthiness of companies like Meta getting far more riled up by the idea of government having a role in technology development.

As Karl Popper, the great theorist of the open society, argued, we shouldn’t try to solve complex problems with grand hubristic plans. Instead, we should apply AI through piecemeal democratic engineering, carefully determining what works and what does not. The best way forward is to start small, applying these technologies to local decisions with more constrained stakeholder groups and smaller impacts.

The next generation of AI experimentation should happen in the laboratories of democracy: states and municipalities. Online town halls to discuss local participatory budgeting proposals could be an easy first step. Commercially available and open-source LLMs could bootstrap this process and build momentum toward federal investment in a public AI option.

Even with these approaches, building and fielding a democratic AI option will be messy and hard. But the alternative—shrugging our shoulders as a fight for commercial AI domination undermines democratic politics—will be much messier and much worse.

This essay was written with Henry Farrell and Nathan Sanders, and previously appeared on Slate.com.

EDITED TO ADD: Linux Weekly News discussion.

Me on Public-Interest Tech

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/06/me-on-public-interest-tech.html

Back in November 2020, in the middle of the COVID-19 pandemic, I gave a virtual talk at the International Symposium on Technology and Society: “The Story of the Internet and How it Broke Bad: A Call for Public-Interest Technologists.” It was something I was really proud of, and it’s finally up on the net.

A New Cybersecurity “Social Contract”

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/02/a-new-cybersecurity-social-contract.html

The US National Cyber Director Chris Inglis wrote an essay outlining a new social contract for the cyber age:

The United States needs a new social contract for the digital age — one that meaningfully alters the relationship between public and private sectors and proposes a new set of obligations for each. Such a shift is momentous but not without precedent. From the Pure Food and Drug Act of 1906 to the Clean Air Act of 1963 and the public-private revolution in airline safety in the 1990s, the United States has made important adjustments following profound changes in the economy and technology.

A similarly innovative shift in the cyber-realm will likely require an intense process of development and iteration. Still, its contours are already clear: the private sector must prioritize long-term investments in a digital ecosystem that equitably distributes the burden of cyberdefense. Government, in turn, must provide more timely and comprehensive threat information while simultaneously treating industry as a vital partner. Finally, both the public and private sectors must commit to moving toward true collaboration — contributing resources, attention, expertise, and people toward institutions designed to prevent, counter, and recover from cyber-incidents.

The devil is in the details, of course, but he’s 100% right when he writes that the market cannot solve this: that the incentives are all wrong. While he never actually uses the word “regulation,” the future he postulates won’t be possible without it. Regulation is how society aligns market incentives with its own values. He also leaves out the NSA — whose effectiveness rests on all of these global insecurities — and the FBI, whose incessant push for encryption backdoors goes against his vision of increased cybersecurity. I’m not sure how he’s going to get them on board. Or the surveillance capitalists, for that matter. A lot of what he wants will require reining in that particular business model.

Good essay — worth reading in full.