OpenAI has disabled ChatGPT’s privacy history, almost certainly because they had a security flaw where users were seeing each others’ histories.

My latest book continues to sell well. Its ranking hovers between 1,500 and 2,000 on Amazon . It’s been spied in airports.

Reviews are consistently good. I have been enjoying giving podcast interviews. It all feels pretty good right now.

You can order a signed book from me here .

For those of you in New York, I’m giving at book talk at the Ford Foundation on Thursday, April 6. Admission is free, but you have to register .

The New York Times is reporting that a US citizen’s phone was hacked by the Predator spyware.

A U.S. and Greek national who worked on Meta’s security and trust team while based in Greece was placed under a yearlong wiretap by the Greek national intelligence service and hacked with a powerful cyberespionage tool, according to documents obtained by The New York Times and officials with knowledge of the case.

The disclosure is the first known case of an American citizen being targeted in a European Union country by the advanced snooping technology, the use of which has been the subject of a widening scandal in Greece. It demonstrates that the illicit use of spyware is spreading beyond use by authoritarian governments against opposition figures and journalists, and has begun to creep into European democracies, even ensnaring a foreign national working for a major global corporation.

The simultaneous tapping of the target’s phone by the national intelligence service and the way she was hacked indicate that the spy service and whoever implanted the spyware, known as Predator, were working hand in hand.

In a matter of a few days, Silicon Valley Bank collapsed when a panic set in, causing a run on deposits . “The blue chip VCs suggested something, then that leaked to other ones, then other ones — we had all our investors calling us and basically demanding we pull our cash,” one source told Ryan Grim. This week on Deconstructed, Grim is joined by Damon Silvers, who has been involved in trying to prevent financial fraud and crisis for more than 20 years. He was the deputy chair of the Congressional Oversight Panel for the Troubled Asset Relief Program, the 2008 bank bailout, and was formerly the policy director of the AFL-CIO.

Grim and Silver discuss what led to a rush of Silicon Valley Bank depositors withdrawing all at once, the subsequent fallout, how the weakening of Dodd–Frank in 2018 paved the way for the current banking crisis , and what reforms are needed to prevent a future and even bigger economic catastrophe.

Transcript coming soon.

The post Understanding the Silicon Valley Bank Run appeared first on The Intercept .

At least, it seems to be a new species.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here .

A construção de um megaempreendimento de energia eólica ameaça soterrar sítios arqueológicos pré-históricos, secar nascentes de rios e devastar áreas de vegetação preservada entre o sertão da Paraíba e do Rio Grande do Norte – além de expulsar populações quilombolas e de agricultores familiares descendentes de indígenas ali estabelecidos.

Batizado de Complexo Eólico Pedra Lavrada, o projeto prevê a instalação de 372 aerogeradores, em aproximadamente 1,6 mil hectares, numa região conhecida como Seridó. Atualmente, o empreendimento aguarda a emissão da licença pela Superintendência de Administração do Meio Ambiente da Paraíba, onde tramita o processo, para iniciar as obras. O prazo estimado para conclusão da implantação do projeto é de 26 meses, a partir da obtenção da licença.

A empresa responsável é a Ventos de São Cleófas Energias Renováveis, um braço da Casa dos Ventos – gigante brasileira do setor, com 35% de capital da multinacional de combustíveis francesa TotalEnergies.

Oito cidades do Seridó fazem parte do projeto. No Rio Grande do Norte: Currais Novos, Acari, Carnaúba dos Dantas e Parelhas; e, na Paraíba, Pedra Lavrada, Nova Palmeira, Picuí e Frei Martinho.

As torres e acessos, quando instalados, vão ficar bem próximos de um conjunto de 40 sítios arqueológicos, visto como um dos principais patrimônios histórico-culturais do país, segundo o historiador e arqueólogo Joadson Vagner Silva, que já prestou consultoria para outros projetos eólicos. São lugares de vestígios únicos no mundo, com a presença de pinturas e gravuras rupestres em grutas, fragmentos de rochas lascadas utilizadas como ferramentas, restos de fogueiras e sepultamentos de populações ancestrais que ali viviam. Um dos restos ósseos mais antigos é de uma criança e remonta a aproximadamente 9,4 mil anos, de acordo com pesquisadores que fizeram as escavações no local.

Uma análise dos bens arqueológicos indica que, naquele território, houve densas populações indígenas nômades, divididas em pequenos grupos que viviam com base na caça e na coleta de frutos e plantas silvestres.

“O modo de vida deles era baseado em acampamentos e um dos principais vestígios deixados por eles são fogueiras, muito presentes aqui. A mais antiga identificada tem 3.600 anos. Isso tudo está em risco agora. O que causa estranheza é que a empresa não identificou nenhum desses vestígios”, afirmou Silva.

Ele faz parte de um grupo de 49 membros da sociedade civil que se uniram para tentar barrar o projeto como foi apresentado. Eles redigiram uma nota técnica, enviada ao Ministério Público Federal, às promotorias estaduais, a órgãos do governo dos dois estados envolvidos e a institutos como o Iphan e o Incra na qual apontam uma série de falhas e inconsistências no estudo e no relatório de impacto ambiental apresentados pela Ventos de São Cleófas.

‘O que nos preocupa é a proximidade do empreendimento com essas áreas. O patrimônio arqueológico é um testemunho, uma página de um livro da história de toda a população brasileira’.

Uma das inconsistências diz respeito justamente à falta de identificação dos sítios, especialmente das fogueiras, todas ignoradas pela empresa no primeiro estudo apresentado. No relatório de avaliação de impacto entregue ao Instituto do Patrimônio Histórico e Artístico Nacional, o Iphan, a companhia cita apenas a Cachoeira do Pedro, um dos 40 sítios arqueológicos do local. E, nessa única indicação, a distância entre o sítio e a área diretamente afetada pelo empreendimento está errada. A empresa indica que a localidade fica a 1,2 quilômetro do parque, quando fica bem mais próximo, a 290 metros, de acordo com os pesquisadores do Seridó Vivo.

“O que nos preocupa é a proximidade do empreendimento com essas áreas. O patrimônio arqueológico é um testemunho, uma página de um livro da história de toda a população brasileira”, defendeu Silva.

Outros sítios ignorados pela empresa ficam ainda mais próximos da área afetada, como o Casa de Pedra e o Sítio Pote, respectivamente a 40 metros e 60 metros da área onde serão instalados os aerogeradores. Há ainda um conjunto de 20 sítios pré-coloniais, que circundam os riachos do Bojo e Olho d’Água, por onde se pretende abrir uma estrada exclusiva para a operação do parque eólico. O sítio Casa Santa, por exemplo, um dos mais importantes da região, está situado a 530 metros de uma área reservada para três torres de produção de energia, segundo o arqueólogo Silva.

Próximos a essas localidades está o cemitério das Cruzes, um lugar de peregrinação no qual foram sepultadas vítimas de uma epidemia de cólera, no fim do século 19. E vizinho a ele, a 650 metros do complexo eólico, está o sítio Pedra do Alexandre, um cemitério indígena pré-histórico com registros de enterramentos feitos ao longo de 8 mil anos. Os possíveis danos a esses locais também estão ausentes no estudo de impacto elaborado pela Ventos de São Cleófas.

Os sepultamentos indígenas, comuns em todo o Seridó, rememoram um grande massacre cometido entre os séculos 17 e 18, que ficou conhecido como Guerra dos Bárbaros. Os pesquisadores julgam ser um dos episódios de resistência nativa contra a invasão portuguesa, com um fim comum à história de colonização no país: a escravização e a consequente dizimação das populações indígenas.

“Esse patrimônio arqueológico é o testemunho da grandeza de todos esses povos originários que existiam na área e deve ser visto com respeito e consideração a essas populações”, explicou Silva.

O arqueólogo se diz bem preocupado com as as grandes explosões necessárias para a abertura das estradas e das covas onde serão construídas as bases dos aerogeradores, equipamentos com 105 metros de altura por 150 metros de diâmetro, cada um.

“Nos estudos de impacto ambiental e na audiência pública não foi mencionada a autorização para os explosivos. Mas as explosões, em áreas montanhosas com rocha, são imprescindíveis em um empreendimento dessa envergadura”, alertou o arqueólogo.

Procurada, a Casa dos Ventos disse que não há risco de soterramento de bens arqueológicos , “visto que o projeto está sendo concebido a uma distância segura dos sítios identificados, o que será ratificado tempestivamente dentro do trâmite do licenciamento arqueológico junto ao Iphan”.

Afirmou ainda que as atividades de detonação e movimentação de terra serão assistidas por programas ambientais seguindo “as melhores práticas da engenharia”. Questionada sobre a ausência dos sítios no relatório, a Casa dos Ventos informou que trata-se de um “arranjo preliminar” seguindo “a legislação vigente para esta etapa de análise”. A empresa reforçou que não identificou nenhum sítio na área diretamente afetada pelo projeto e que novos estudos serão feitos posteriormente com assistência do Iphan.

Patrimônio natural

A s serras buscadas pelo empreendimento contradizem o senso comum sobre a Caatinga, de que por lá só existem terra seca e pequenos arbustos retorcidos. O trecho de interesse da Ventos de São Cleófas é predominantemente úmido, esverdeado e possui um ecossistema rico em plantas e animais em risco de extinção.

Nesse local, a empresa planeja desmatar 1.477 hectares de vegetação nativa. A supressão vegetal nessa área para instalação do parque eólico pode decretar a morte da biodiversidade, transformando essa faixa preservada em deserto.
“A grande preocupação é que a Caatinga acabe desaparecendo”, explicou o biólogo Damião de Oliveira, mestre em Ecologia pela Universidade Federal do Rio Grande do Norte, e também integrante do projeto Seridó Vivo.

Embora a preservação vegetal conserve a umidade nas serras, o Seridó está no semiárido brasileiro e, naturalmente, sofre com a falta de chuvas. A média pluviométrica da região é de no máximo 600 milímetros ao ano – para fins de comparação, é a mesma quantidade de água registrada em três dias durante a trágica tempestade no litoral norte de São Paulo, no fim de fevereiro, de acordo com o Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura da Unicamp.

Grandes empreendimentos, como o Complexo Pedra Lavrada, demandam muita água local para a construção das estruturas dos aerogeradores e das estradas. Para suprir a falta de chuva, a Ventos de São Cleófas sugeriu a perfuração generalizada de poços artesianos. O problema é que a água subterrânea é um bem raro na região, já que, além da estiagem, a estrutura rochosa do Seridó naturalmente dificulta a infiltração da água no solo.

’A história dessas pessoas está inscrita nos territórios. Se você tira o território, não tem mais história, não tem mais memória, não tem mais marcas’.

Além do risco de secar os rios, existe a possibilidade de contaminar a água com o óleo que cai dos aerogeradores e do maquinário, conforme a própria empresa prevê no estudo ambiental. O documento indica que o projeto terá interferência direta em 16 cursos d’água que abastecem a bacia do rio Piranhas-Açu, a principal fonte hídrica das comunidades ali instaladas.

O parque também ameaça o movimento de aves que migram para a região nos períodos chuvosos em busca de refúgio para fazer seus ninhos. Além disso, aves de rapina são comumente vítimas das pás dos aerogeradores em parques eólicos já em funcionamento.

Populações de morcegos também podem ser drasticamente afetadas com a chegada do complexo – tanto pelo risco de colisão quanto pela destruição do habitat. Sem eles, a engrenagem da Caatinga não gira.

“Os morcegos realizam uma parte significativa da polinização de muitas plantas do bioma. Quanto mais empreendimentos, mais impacto a essas populações. Isso pode levar a um colapso desse ecossistema”, afirmou o biólogo.

A Casa dos Ventos diz que realizou estudos suficientes para avaliação “de todos os impactos ambientais para o empreendimento”. A empresa afirmou que o estudo foi conduzido por mais de 30 profissionais, ao longo de três anos de trabalho, e que, por meio dele, foram propostos 31 programas ambientais e ações de para garantir a viabilidade do projeto.

Síndrome de turbina e ameaça aos quilombolas

À s margens da área prevista para o complexo eólico, estão comunidades quilombolas e de agricultores familiares com ascendência indígena, em ao menos seis dos oito municípios envolvidos no projeto. Muitas comunidades não são tituladas pelo Incra, embora sejam reconhecidas por si próprias e pela tradição oral que se manteve dos antepassados. De acordo com os pesquisadores do Seridó Vivo, muitas não foram consultadas adequadamente sobre o projeto, conforme prevê a Convenção 169 da Organização Internacional do Trabalho, a OIT.

“O relatório de impacto ambiental só conta as comunidades cadastradas no Incra. Como poucas comunidades são tituladas, eles não levam em conta quase nenhuma comunidade quilombola, nem os descendentes de indígenas. Eles tentam apagar que existem comunidades tradicionais que vão ser afetadas”, criticou a antropóloga Julie Antoinette Cavignac, professora da Universidade Federal do Rio Grande Norte.

Alguns aerogeradores estão projetados para ficar distantes 400 ou 500 metros das casas dos quilombolas, o que pode comprometer as estruturas dos imóveis e gerar distúrbios mentais relacionados ao constante barulho das hélices. O principal deles é conhecido pela comunidade científica como “síndrome da turbina”, que gera dores de cabeça, náuseas, raiva, ansiedade, insônia e falta de concentração.

Os efeitos, de acordo com Cavignac, tendem a expulsar gradualmente as populações tradicionais do território .“A história dessas pessoas está inscrita nos territórios. Se você tira o território, não tem mais história, não tem mais memória, não tem mais marcas”, analisou a antropóloga.

Uma das comunidades diretamente afetadas, não reconhecida pelo Incra, é a Comunidade Negra Serra do Abreu, onde a Ventos de São Cleófas planejou abrir uma estrada. Lá, atualmente, vivem 34 famílias.

Diana Barbosa dos Santos, presidente da Associação Comunidade Negra Serra do Abreu, contou que, há alguns anos, representantes da Casa dos Ventos começaram a visitar a comunidade e a apresentar o projeto eólico como se fossem mil maravilhas.

“Procuraram meu sogro diversas vezes para ele assinar documentos. Ele assinou vários papéis sem ler, porque o estudo dele é pouco. Diziam que iam pagar alguma coisa, mas nunca recebeu um centavo”, detalhou.

Para Maria das Neves Valentim, articuladora do Fórum Mudanças Climáticas do núcleo do Rio Grande do Norte, a possível repulsão das comunidades tradicionais é a continuidade de uma história de exploração e descaso no semiárido nordestino.
“A natureza desse projeto é a atualização, com a falácia da sustentabilidade, do velho colonialismo extrativista, como foi com a cana-de-açúcar. Ou seja, somos um quintalzão. Agora, nós somos uma fazenda para produzir energia”, comparou.

The post Projeto de energia eólica ameaça destruir passado e futuro do Brasil numa tacada só appeared first on The Intercept .

This is a current list of where and when I am scheduled to speak:

• I’m speaking on “ How to Reclaim Power in the Digital World ” at EPFL in Lausanne, Switzerland, on Thursday, March 16, 2023, at 5:30 PM CET.
• I’ll be discussing my new book A Hacker’s Mind: How the Powerful Bend Society’s Rules at Harvard Science Center in Cambridge, Massachusetts, USA, on Friday, March 31, 2023, at 6:00 PM EDT.
• I’ll be discussing my book A Hacker’s Mind with Julia Angwin at the Ford Foundation Center for Social Justice in New York City, on Thursday, April 6, 2023, at 6:30 PM EDT.
• I’m speaking at IT-S Now 2023 in Vienna, Austria, on June 1-2, 2023.

The list is maintained on this page .

By Nathan E. Sanders & Bruce Schneier

Nearly 90% of the multibillion-dollar federal lobbying apparatus in the United States serves corporate interests. In some cases, the objective of that money is obvious. Google pours millions into lobbying on bills related to antitrust regulation. Big energy companies expect action whenever there is a move to end drilling leases for federal lands, in exchange for the tens of millions they contribute to congressional reelection campaigns.

But lobbying strategies are not always so blunt, and the interests involved are not always so obvious. Consider, for example, a 2013 Massachusetts bill that tried to restrict the commercial use of data collected from K-12 students using services accessed via the internet. The bill appealed to many privacy-conscious education advocates, and appropriately so. But behind the justification of protecting students lay a market-altering policy: the bill was introduced at the behest of Microsoft lobbyists, in an effort to exclude Google Docs from classrooms.

What would happen if such legal-but-sneaky strategies for tilting the rules in favor of one group over another become more widespread and effective? We can see hints of an answer in the remarkable pace at which artificial-intelligence tools for everything from writing to graphic design are being developed and improved. And the unavoidable conclusion is that AI will make lobbying more guileful, and perhaps more successful.

It turns out there is a natural opening for this technology: microlegislation.

“Microlegislation” is a term for small pieces of proposed law that cater—sometimes unexpectedly—to narrow interests. Political scientist Amy McKay coined the term. She studied the 564 amendments to the Affordable Care Act (“Obamacare”) considered by the Senate Finance Committee in 2009, as well as the positions of 866 lobbying groups and their campaign contributions. She documented instances where lobbyist comments—on health-care research, vaccine services, and other provisions—were translated directly into microlegislation in the form of amendments. And she found that those groups’ financial contributions to specific senators on the committee increased the amendments’ chances of passing.

Her finding that lobbying works was no surprise. More important, McKay’s work demonstrated that computer models can predict the likely fate of proposed legislative amendments, as well as the paths by which lobbyists can most effectively secure their desired outcomes. And that turns out to be a critical piece of creating an AI lobbyist.

Lobbying has long been part of the give-and-take among human policymakers and advocates working to balance their competing interests. The danger of microlegislation—a danger greatly exacerbated by AI—is that it can be used in a way that makes it difficult to figure out who the legislation truly benefits.

Another word for a strategy like this is a “hack.” Hacks follow the rules of a system but subvert their intent. Hacking is often associated with computer systems, but the concept is also applicable to social systems like financial markets, tax codes, and legislative processes.

While the idea of monied interests incorporating AI assistive technologies into their lobbying remains hypothetical, specific machine-learning technologies exist today that would enable them to do so. We should expect these techniques to get better and their utilization to grow, just as we’ve seen in so many other domains.

Here’s how it might work.

Crafting an AI microlegislator

To make microlegislation, machine-learning systems must be able to uncover the smallest modification that could be made to a bill or existing law that would make the biggest impact on a narrow interest.

There are three basic challenges involved. First, you must create a policy proposal— small suggested changes to legal text—and anticipate whether or not a human reader would recognize the alteration as substantive. This is important; a change that isn’t detectable is more likely to pass without controversy. Second, you need to do an impact assessment to project the implications of that change for the short- or long-range financial interests of companies. Third, you need a lobbying strategizer to identify what levers of power to pull to get the best proposal into law.

Existing AI tools can tackle all three of these.

The first step, the policy proposal , leverages the core function of generative AI . Large language models, the sort that have been used for general-purpose chatbots such as ChatGPT, can easily be adapted to write like a native in different specialized domains after seeing a relatively small number of examples. This process is called fine-tuning . For example, a model “pre-trained” on a large library of generic text samples from books and the internet can be “fine-tuned” to work effectively on medical literature, computer science papers, and product reviews.

Given this flexibility and capacity for adaptation, a large language model could be fine-tuned to produce draft legislative texts, given a data set of previously offered amendments and the bills they were associated with. Training data is available. At the federal level, it’s provided by the US Government Publishing Office , and there are already tools for downloading and interacting with it. Most other jurisdictions provide similar data feeds, and there are even convenient assemblages of that data.

Meanwhile, large language models like the one underlying ChatGPT are routinely used for summarizing long, complex documents (even law s and computer code ) to capture the essential points, and they are optimized to match human expectations. This capability could allow an AI assistant to automatically predict how detectable the true effect of a policy insertion may be to a human reader.

Today, it can take a highly paid team of human lobbyists days or weeks to generate and analyze alternative pieces of microlegislation on behalf of a client. With AI assistance, that could be done instantaneously and cheaply. This opens the door to dramatic increases in the scope of this kind of microlegislating, with a potential to scale across any number of bills in any jurisdiction.

Teaching machines to assess impact

Impact assessment is more complicated. There is a rich series of methods for quantifying the predicted outcome of a decision or policy, and then also optimizing the return under that model. This kind of approach goes by different names in different circles— mathematical programming in management science, utility maximization in economics, and rational design in the life sciences.

To train an AI to do this, we would need to specify some way to calculate the benefit to different parties as a result of a policy choice. That could mean estimating the financial return to different companies under a few different scenarios of taxation or regulation. Economists are skilled at building risk models like this, and companies are already required to formulate and disclose regulatory compliance risk factors to investors. Such a mathematical model could translate directly into a reward function, a grading system that could provide feedback for the model used to create policy proposals and direct the process of training it.

The real challenge in impact assessment for generative AI models would be to parse the textual output of a model like ChatGPT in terms that an economic model could readily use. Automating this would require extracting structured financial information from the draft amendment or any legalese surrounding it. This kind of information extraction, too, is an area where AI has a long history; for example, AI systems have been trained to recognize clinical details in doctors’ notes. Early indications are that large language models are fairly good at recognizing financial information in texts such as investor call transcripts. While it remains an open challenge in the field, they may even be capable of writing out multi-step plans based on descriptions in free text.

Machines as strategists

The last piece of the puzzle is a lobbying strategizer to figure out what actions to take to convince lawmakers to adopt the amendment.

Passing legislation requires a keen understanding of the complex interrelated networks of legislative offices, outside groups, executive agencies, and other stakeholders vying to serve their own interests. Each actor in this network has a baseline perspective and different factors that influence that point of view. For example, a legislator may be moved by seeing an allied stakeholder take a firm position, or by a negative news story, or by a campaign contribution.

It turns out that AI developers are very experienced at modeling these kinds of networks. Machine-learning models for network graphs have been built, refined, improved, and iterated by hundreds of researchers working on incredibly diverse problems: lidar scans used to guide self-driving cars, the chemical functions of molecular structures, the capture of motion in actors’ joints for computer graphics, behaviors in social networks, and more.

In the context of AI-assisted lobbying, political actors like legislators and lobbyists are nodes on a graph, just like users in a social network. Relations between them are graph edges, like social connections. Information can be passed along those edges, like messages sent to a friend or campaign contributions made to a member. AI models can use past examples to learn to estimate how that information changes the network. Calculating the likelihood that a campaign contribution of a given size will flip a legislator’s vote on an amendment is one application.

McKay’s work has already shown us that there are significant, predictable relationships between these actions and the outcomes of legislation, and that the work of discovering those can be automated. Others have shown that graphs of neural network models like those described above can be applied to political systems. The full-scale use of these technologies to guide lobbying strategy is theoretical, but plausible.

Put together, these three components could create an automatic system for generating profitable microlegislation. The policy proposal system would create millions, even billions, of possible amendments. The impact assessor would identify the few that promise to be most profitable to the client. And the lobbying strategy tool would produce a blueprint for getting them passed.

What remains is for human lobbyists to walk the floors of the Capitol or state house, and perhaps supply some cash to grease the wheels. These final two aspects of lobbying—access and financing—cannot be supplied by the AI tools we envision. This suggests that lobbying will continue to primarily benefit those who are already influential and wealthy, and AI assistance will amplify their existing advantages.

The transformative benefit that AI offers to lobbyists and their clients is scale. While individual lobbyists tend to focus on the federal level or a single state, with AI assistance they could more easily infiltrate a large number of state-level (or even local-level) law-making bodies and elections. At that level, where the average cost of a seat is measured in the tens of thousands of dollars instead of millions, a single donor can wield a lot of influence—if automation makes it possible to coordinate lobbying across districts.

How to stop them

When it comes to combating the potentially adverse effects of assistive AI, the first response always seems to be to try to detect whether or not content was AI-generated. We could imagine a defensive AI that detects anomalous lobbyist spending associated with amendments that benefit the contributing group. But by then, the damage might already be done.

In general, methods for detecting the work of AI tend not to keep pace with its ability to generate convincing content. And these strategies won’t be implemented by AIs alone. The lobbyists will still be humans who take the results of an AI microlegislator and further refine the computer’s strategies. These hybrid human-AI systems will not be detectable from their output.

But the good news is: the same strategies that have long been used to combat misbehavior by human lobbyists can still be effective when those lobbyists get an AI assist. We don’t need to reinvent our democracy to stave off the worst risks of AI; we just need to more fully implement long-standing ideals.

First, we should reduce the dependence of legislatures on monolithic, multi-thousand-page omnibus bills voted on under deadline. This style of legislating exploded in the 1980s and 1990s and continues through to the most recent federal budget bill . Notwithstanding their legitimate benefits to the political system, omnibus bills present an obvious and proven vehicle for inserting unnoticed provisions that may later surprise the same legislators who approved them.

The issue is not that individual legislators need more time to read and understand each bill (that isn’t realistic or even necessary ). It’s that omnibus bills must pass . There is an imperative to pass a federal budget bill, and so the capacity to push back on individual provisions that may seem deleterious (or just impertinent ) to any particular group is small. Bills that are too big to fail are ripe for hacking by microlegislation.

Moreover, the incentive for legislators to introduce microlegislation catering to a narrow interest is greater if the threat of exposure is lower. To strengthen the threat of exposure for misbehaving legislative sponsors, bills should focus more tightly on individual substantive areas and, after the introduction of amendments, allow more time before the committee and floor votes. During this time, we should encourage public review and testimony to provide greater oversight.

Second, we should strengthen disclosure requirements on lobbyists, whether they’re entirely human or AI-assisted. State laws regarding lobbying disclosure are a hodgepodge. North Dakota, for example, only requires lobbying reports to be filed annually, so that by the time a disclosure is made, the policy is likely already decided. A lobbying disclosure scorecard created by Open Secrets, a group researching the influence of money in US politics, tracks nine states that do not even require lobbyists to report their compensation.

Ideally, it would be great for the public to see all communication between lobbyists and legislators, whether it takes the form of a proposed amendment or not. Absent that, let’s give the public the benefit of reviewing what lobbyists are lobbying for—and why. Lobbying is traditionally an activity that happens behind closed doors. Right now, many states reinforce that: they actually exempt testimony delivered publicly to a legislature from being reported as lobbying.

In those jurisdictions, if you reveal your position to the public, you’re no longer lobbying. Let’s do the inverse: require lobbyists to reveal their positions on issues. Some jurisdictions already require a statement of position (a ‘yea’ or ‘nay’) from registered lobbyists. And in most (but not all ) states, you could make a public records request regarding meetings held with a state legislator and hope to get something substantive back. But we can expect more—lobbyists could be required to proactively publish, within a few days, a brief summary of what they demanded of policymakers during meetings and why they believe it’s in the general interest.

We can’t rely on corporations to be forthcoming and wholly honest about the reasons behind their lobbying positions. But having them on the record about their intentions would at least provide a baseline for accountability.

Finally, consider the role AI assistive technologies may have on lobbying firms themselves and the labor market for lobbyists. Many observers are rightfully concerned about the possibility of AI replacing or devaluing the human labor it automates. If the automating potential of AI ends up commodifying the work of political strategizing and message development, it may indeed put some professionals on K Street out of work.

But don’t expect that to disrupt the careers of the most astronomical ly compensated lobbyists: former members Congress and other insiders who have passed through the revolving door . There is no shortage of reform ideas for limiting the ability of government officials turned lobbyists to sell access to their colleagues still in government, and they should be adopted and—equally important—maintained and enforced in successive Congresses and administrations.

None of these solutions are really original, specific to the threats posed by AI, or even predominantly focused on microlegislation—and that’s the point. Good governance should and can be robust to threats from a variety of techniques and actors.

But what makes the risks posed by AI especially pressing now is how fast the field is developing. We expect the scale, strategies, and effectiveness of humans engaged in lobbying to evolve over years and decades. Advancements in AI, meanwhile, seem to be making impressive breakthroughs at a much faster pace—and it’s still accelerating.

The legislative process is a constant struggle between parties trying to control the rules of our society as they are updated, rewritten, and expanded at the federal, state, and local levels. Lobbying is an important tool for balancing various interests through our system. If it’s well-regulated, perhaps lobbying can support policymakers in making equitable decisions on behalf of us all.

This essay originally appeared in MIT Technology Review .