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経済産業省は、工場を建設する目的で平成27年（1月～12月期）に1,000平方メートル以上の用地を取得した製造業、ガス業、熱供給業、電気業の事業者を対象に「工場立地動向調査」を実施し、結果を取りまとめました。

4月5日から9日まで、韓国のソウルにおいて、日中韓自由貿易協定(FTA)交渉の第10回交渉会合(局長/局次長会合)が開催されます。

経済産業省は、昨年10月に「活力あふれる『ビンテージ・ソサエティ』の実現に向けた取組に係る研究会」を立ち上げ、検討を重ね、今般報告書をとりまとめました。

交通事故の削減や渋滞緩和、環境負荷低減等の課題解決が期待される協調ITS（※）の実現普及に必要とされる地図データベースの整備や維持・更新のコスト削減に役立つ日本発の国際規格が制定されました。

平成28年3月4日に、東京電力からありました改正電気事業法に基づく会社分割に係る申請について、このたび申請のとおり認可しました。

Federal agencies like Immigration and Customs Enforcement (ICE) and Customs and Border Protection (CBP) have descended into utter lawlessness, most recently in Minnesota. The violence is shocking. So are the intrusions on digital rights. For example, we have a First Amendment right to record on-duty police, including ICE and CBP, but federal agents are violating this right. Indeed, Alex Pretti was exercising this right shortly before federal agents shot and killed him. So were the many people who filmed agents shooting and killing Pretti and Renee Good – thereby creating valuable evidence that contradicts false claims by government leaders.

To protect our digital rights, we need the rule of law. When an armed agent of the government breaks the law, the civilian they injure must be made whole. This includes a lawsuit by the civilian (or their survivor) against the agent, seeking money damages to compensate them for their injury. Such systems of accountability encourage agents to follow the law, whereas impunity encourages them to break it.

Unfortunately, there is a gaping hole in the rule of law: when a federal agent violates the U.S. Constitution, it is increasingly difficult to sue them for damages. For these reasons, EFF supports new statutes to fill this hole, including California S.B. 747.

The Problem

In 1871, at the height of Reconstruction following the Civil War, Congress enacted a landmark statute empowering people to sue state and local officials who violated their constitutional rights. This was a direct response to state-sanctioned violence against Black people that continued despite the formal end of slavery. The law is codified today at 42 U.S.C. § 1983.

However, there is no comparable statute empowering people to sue federal officials who violate the U.S. Constitution.

So in 1971, the U.S. Supreme Court stepped into this gap, in a watershed case called Bivens v. Six Unknown FBI Agents. The plaintiff alleged that FBI agents unlawfully searched his home and used excessive force against him. Justice Brennan, writing for a six-Justice majority of the Court, ruled that “damages may be obtained for injuries consequent upon a violation of the Fourth Amendment by federal officials.”  He explained: “Historically, damages have been regarded as the ordinary remedy for an invasion of personal interests in liberty.” Further: “The very essence of civil liberty certainly consists of the right of every individual to claim the protection of the laws, whenever he receives an injury.”

Subsequently, the Court expanded Bivens in cases where federal officials violated the U.S. Constitution by discriminating in a workplace, and by failing to provide medical care in a prison.

In more recent years, however, the Court has whittled Bivens down to increasing irrelevance. For example, the Court has rejected damages litigation against federal officials who allegedly violated the U.S. Constitution by strip searching a detained person, and by shooting a person located across the border.

In 2022, the Court by a six-to-three vote rejected a damages claim against a Border Patrol agent who used excessive force when investigating alleged smuggling.  In an opinion concurring in the judgment, Justice Gorsuch conceded that he “struggle[d] to see how this set of facts differs meaningfully from those in Bivens itself.” But then he argued that Bivens should be overruled because it supposedly “crossed the line” against courts “assuming legislative authority.”

Last year, the Court unanimously declined to extend Bivens to excessive force in a prison.

The Solution

At this juncture, legislatures must solve the problem. We join calls for Congress to enact a federal statute, parallel to the one it enacted during Reconstruction, to empower people to sue federal officials (and not just state and local officials) who violate the U.S. Constitution.

In the meantime, it is heartening to see state legislatures step forward fill this hole. One such effort is California S.B. 747, which EFF is proud to endorse.

State laws like this one do not violate the Supremacy Clause of the U.S. Constitution, which provides that the Constitution is the supreme law of the land. In the words of one legal explainer, this kind of state law “furthers the ultimate supremacy of the federal Constitution by helping people vindicate their fundamental constitutional rights.” 

This kind of state law goes by many names. The author of S.B. 747, California Senator Scott Wiener, calls it the “No Kings Act.” Protect Democracy, which wrote a model bill, calls it the “Universal Constitutional Remedies Act.” The originator of this idea, Professor Akhil Amar, calls it a “converse 1983”: instead of Congress authorizing suit against state officials for violating the U.S. Constitution, states would authorize suit against federal officials for doing the same thing.

We call these laws a commonsense way to protect the rule of law, which is a necessary condition to preserve our digital rights. EFF has long supported effective judicial remedies, including support for nationwide injunctions and private rights of action, and opposition to qualified immunity.

We also support federal and state legislation to guarantee our right to sue federal agents for damages when they violate the U.S. Constitution.

The phrase "artificial intelligence" has been around for a long time, covering everything from computers with "brains"—think Data from Star Trek or Hal 9000 from 2001: A Space Odyssey—to the autocomplete function that too often has you sending emails to the wrong person. It's a term that sweeps a wide array of uses into it—some well-established, others still being developed.

Recent news shows us a rapidly expanding catalog of potential harms that may result from companies pushing AI into every new feature and aspect of public life—like the automation of bias that follows from relying on a backward-looking technology to make consequential decisions about people's housing, employment, education, and so on. Complicating matters, the computation needed for some AI services requires vast amounts of water and electricity, leading to sometimes difficult questions about whether the increased fossil fuel use or consumption of water is justified.

We are also inundated with advertisements and exhortations to use the latest AI-powered apps, and with hype insisting AI can solve any problem.

Obscured by this hype, there are some real examples of AI proving to be a helpful tool. For example, machine learning is especially useful for scientists looking at everything from the inner workings of our biology to cosmic bodies in outer space. AI tools can also improve accessibility for people with disabilities, facilitate police accountability initiatives, and more. There are reasons why these problems are amenable to machine learning and why excitement over these uses shouldn’t translate into a perception that just any language model or AI technology possesses expert knowledge or can solve whatever problem it’s marketed as solving.

EFF has long fought for sensible, balanced tech policies because we’ve seen how regulators can focus entirely on use cases they don’t like (such as the use of encryption to hide criminal behavior) and cause enormous collateral harm to other uses (such as using encryption to hide dissident resistance). Similarly, calls to completely preempt state regulation of AI would thwart important efforts to protect people from the real harms of AI technologies. Context matters. Large language models (LLMs) and the tools that rely on them are not magic wands—they are general-purpose technologies. And if we want to regulate those technologies in a way that doesn’t shut down beneficial innovations, we have to focus on the impact(s) of a given use or tool, by a given entity, in a specific context. Then, and only then, can we even hope to figure out what to do about it.

So let’s look at the real-world landscape.

AI’s Real and Potential Harms

Thinking ahead about potential negative uses of AI helps us spot risks. Too often, the corporations developing AI tools—as well as governments that use them—lose sight of the real risks, or don’t care. For example, companies and governments use AI to do all sorts of things that hurt people, from price collusion to mass surveillance. AI should never be part of a decision about whether a person will be arrested, deported, placed into foster care, or denied access to important government benefits like disability payments or medical care.

There is too much at stake, and governments have a duty to make responsible, fair, and explainable decisions, which AI can’t reliably do yet. Why? Because AI tools are designed to identify and reproduce patterns in data that they are “trained” on.  If you train AI on records of biased government decisions, such as records of past arrests, it will “learn” to replicate those discriminatory decisions.

And simply having a human in the decision chain will not fix this foundational problem. Studies have shown that having a human “in the loop” doesn’t adequately correct for AI bias, both because the human tends to defer to the AI and because the AI can provide cover for a biased human to ratify decisions that agree with their biases and override the AI at other times.

These biases don’t just arise in obvious contexts, like when a government agency is making decisions about people. It can also arise in equally life-affecting contexts like medical care. Whenever AI is used for analysis in a context with systemic disparities and whenever the costs of an incorrect decision fall on someone other than those deciding whether to use the tool.  For example, dermatology has historically underserved people of color because of a focus on white skin, with the resulting bias affecting AI tools trained on the existing and biased image data.

These kinds of errors are difficult to detect and correct because it’s hard or even impossible to understand how an AI tool arrives at individual decisions. These tools can sometimes find and apply patterns that a human being wouldn't even consider, such as basing diagnostic decisions on which hospital a scan was done at. Or determining that malignant tumors are the ones where there is a ruler next to them—something that a human would automatically exclude from their evaluation of an image. Unlike a human, AI does not know that the ruler is not part of the cancer.

Auditing and correcting for these kinds of mistakes is vital, but in some cases, might negate any sort of speed or efficiency arguments made in favor of the tool. We all understand that the more important a decision is, the more guardrails against disaster need to be in place. For many AI tools, those don't exist yet. Sometimes, the stakes will be too high to justify the use of AI. In general, the higher the stakes, the less this technology should be used.

We also need to acknowledge the risk of over-reliance on AI, at least as it is currently being released. We've seen shades of a similar problem before online (see: "Dr. Google"), but the speed and scale of AI use—and the increasing market incentive to shoe-horn “AI” into every business model—have compounded the issue.

Moreover, AI may reinforce a user’s pre-existing beliefs—even if they’re wrong or unhealthy. Many users may not understand how AI works, what it is programmed to do, and how to fact check it. Companies have chosen to release these tools widely without adequate information about how to use them properly and what their limitations are. Instead they market them as easy and reliable. Worse, some companies also resist transparency in the name of trade secrets and reducing liability, making it harder for anyone to evaluate AI-generated answers. 

Other considerations may weigh against AI uses are its environmental impact and potential labor market effects. Delving into these is beyond the scope of this post, but it is an important factor in determining if AI is doing good somewhere and whether any benefits from AI are equitably distributed.

Research into the extent of AI harms and means of avoiding them is ongoing, but it should be part of the analysis.

AI’s Real and Potential Benefits

However harmful AI technologies can sometimes be, in the right hands and circumstances, they can do things that humans simply can’t. Machine learning technology has powered search tools for over a decade. It’s undoubtedly useful for machines to help human experts pore through vast bodies of literature and data to find starting points for research—things that no number of research assistants could do in a single year. If an actual expert is involved and has a strong incentive to reach valid conclusions, the weaknesses of AI are less significant at the early stage of generating research leads. Many of the following examples fall into this category.

Machine learning differs from traditional statistics in that the analysis doesn’t make assumptions about what factors are significant to the outcome. Rather, the machine learning process computes which patterns in the data have the most predictive power and then relies upon them, often using complex formulae that are unintelligible to humans. These aren’t discoveries of laws of nature—AI is bad at generalizing that way and coming up with explanations. Rather, they’re descriptions of what the AI has already seen in its data set.

To be clear, we don't endorse any products and recognize initial results are not proof of ultimate success. But these cases show us the difference between something AI can actually do versus what hype claims it can do.

Researchers are using AI to discover better alternatives to today’s lithium-ion batteries, which require large amounts of toxic, expensive, and highly combustible materials. Now, AI is rapidly advancing battery development: by allowing researchers to analyze millions of candidate materials and generate new ones. New battery technologies discovered with the help of AI have a long way to go before they can power our cars and computers, but this field has come further in the past few years than it had in a long time.

AI Advancements in Scientific and Medical Research

AI tools can also help facilitate weather prediction. AI forecasting models are less computationally intensive and often more reliable than traditional tools based on simulating the physical thermodynamics of the atmosphere. Questions remain, though about how they will handle especially extreme events or systemic climate changes over time.

For example:

• The National Oceanic and Atmospheric Administration has developed new machine learning models to improve weather prediction, including a first-of-its-kind hybrid system that  uses an AI model in concert with a traditional physics-based model to deliver more accurate forecasts than either model does on its own. to augment its traditional forecasts, with improvements in accuracy when the AI model is used in concert with the physics-based model.
• Several models were used to forecast a recent hurricane. Google DeepMind’s AI system performed the best, even beating official forecasts from the U.S. National Hurricane Center (which now uses DeepMind’s AI model).

 Researchers are using AI to help develop new medical treatments:

• Deep learning tools, like the Nobel Prize-winning model AlphaFold, are helping researchers understand protein folding. Over 3 million researchers have used AlphaFold to analyze biological processes and design drugs that target disease-causing malfunctions in those processes.
• Researchers used machine learning simulate and computationally test a large range of new antibiotic candidates hoping they will help treat drug-resistant bacteria, a growing threat that kills millions of people each year.
• Researchers used AI to identify a new treatment for idiopathic pulmonary fibrosis, a progressive lung disease with few treatment options. The new treatment has successfully completed a Phase IIa clinical trial. Such drugs still need to be proven safe and effective in larger clinical trials and gain FDA approval before they can help patients, but this new treatment for pulmonary fibrosis could be the first to reach that milestone.
• Machine learning has been used for years to aid in vaccine development—including the development of the first COVID-19 vaccines––accelerating the process by rapidly identifying potential vaccine targets for researchers to focus on.

AI Uses for Accessibility and Accountability 

AI technologies can improve accessibility for people with disabilities. But, as with many uses of this technology, safeguards are essential. Many tools lack adequate privacy protections, aren’t designed for disabled users, and can even harbor bias against people with disabilities. Inclusive design, privacy, and anti-bias safeguards are crucial. But here are two very interesting examples:

• AI voice generators are giving people their voices back, after losing their ability to speak. For example, while serving in Congress, Rep. Jennifer Wexton developed a debilitating neurological condition that left her unable to speak. She used her cloned voice to deliver a speech from the floor of the House of Representatives advocating for disability rights.
• Those who are blind or low-vision, as well as those who are deaf or hard-of-hearing, have benefited from accessibility tools while also discussing their limitations and drawbacks. At present, AI tools often provide information in a more easily accessible format than traditional web search tools and many websites that are difficult to navigate for users that rely on a screen reader. Other tools can help blind and low vision users navigate and understand the world around them by providing descriptions of their surroundings. While these visual descriptions may not always be as good as the ones a human may provide, they can still be useful in situations when users can’t or don’t want to ask another human to describe something. For more on this, check out our recent podcast episode on “Building the Tactile Internet.”

When there is a lot of data to comb through, as with police accountability, AI is very useful for researchers and policymakers:

•  The Human Rights Data Analysis Group used LLMs to analyze millions of pages of records regarding police misconduct. This is essentially the reverse of harmful use cases relating to surveillance; when the power to rapidly analyze large amounts of data is used by the public to scrutinize the state there is a potential to reveal abuses of power and, given the power imbalance, very little risk that undeserved consequences will befall those being studied.
• An EFF client, Project Recon, used an AI system to review massive volumes of transcripts of prison parole hearings to identify biased parole decisions. This innovative use of technology to identify systemic biases, including racial disparities, is the type of AI use we should support and encourage.

It is not a coincidence that the best examples of positive uses of AI come in places where experts, with access to infrastructure to help them use the technology and the requisite experience to evaluate the results, are involved. Moreover, academic researchers are already accustomed to explaining what they have done and being transparent about it—and it has been hard won knowledge that ethics are a vital step in work like this.

Nor is it a coincidence that other beneficial uses involve specific, discrete solutions to problems faced by those whose needs are often unmet by traditional channels or vendors. The ultimate outcome is beneficial, but it is moderated by human expertise and/or tailored to specific needs.

Context Matters

It can be very tempting—and easy—to make a blanket determination about something, especially when the stakes seem so high. But we urge everyone—users, policymakers, the companies themselves—to cut through the hype. In the meantime, EFF will continue to work against the harms caused by AI while also making sure that beneficial uses can advance.

情報通信審議会　電気通信事業政策部会（第88回）の開催について

今後のBS及び東経110度CSに係る衛星放送インフラのあり方等に関する意見募集

アジア・太平洋電気通信共同体（APT）上級幹部政策研修ワークショップの開催結果

情報通信審議会 電気通信事業政策部会 消費者保護政策委員会（第4回）の開催について

電波監理審議会（第1152回）会議資料

新聞やテレビ、通信社には「首相番」がいる。最高権力者である首相に張り付いて、「首相動静」や「首相の一日」といった各社の欄で、一挙手一投足を報じる。2019年7月2日午前11時21分、首相の安倍晋三が、自民党幹事長代行の萩生田光一と党本部で会ったと各社は報じている。だが各社の報道には、重大な事実が欠落していた。安倍は萩生田と二人きりで話をしたわけではなく、統一教会の幹部たちを交えて会談していたのだ。

自民党は「自虐史観」への抵抗が強い。日本の過去の植民地支配と戦争を、正当化したり美化したりする。安倍晋三はその典型だった。安倍政権が歴史の教科書の検定基準を改定。その後、「従軍慰安婦」や「強制連行」などをめぐり、歴史の教科書に政府見解が記述されるようになった。現総裁の高市早苗は、そんな安倍を師と仰ぎ、2026年1月には安倍の遺影と共に伊勢神宮を参拝した。だが統一教会の教義は、日本が贖罪のために韓国に尽くさねばならないというもの。日本の信者に教義を刷り込み、高額な献金をさせた。統一教会総裁のハン・ハクチャ(韓鶴子)は、安倍への対策をとるよう指示した。日本の幹部たちは、安倍を何とか懐柔しようとする。

自民党総裁の高市早苗は、「スパイ防止法」に前のめりだ。2025年11月26日、国会での党首討論で「もう今年、検討を開始して速やかに法案を策定することを考えている」と言った。だがスパイ防止法の制定は、高市が急に言い始めたわけではない。自民党と統一教会の半世紀越しの悲願だ。TM特別報告では、統一教会の政治工作を担い、スパイ防止法制定に血道を上げた「国際勝共連合」と、自民党の政治家たちとの蜜月が描かれている。

　全国各地の自衛隊基地で進む長射程ミサイル配備や弾薬庫増設。20年８月、第４次安倍政権下の自民政務調査会「敵基地攻撃能力」提言は22年、岸田政権の「安保３文書」明記で現実化した。「戦争準備」に抗い「戦争させない、平和を守れ」と立ち上がった沖縄、九州、西日本の市民の取り組みを11月号に続き報告する。　【大分】ミサイル配備NO　他国を攻撃する長射程ミサイル配備が予定される陸上自衛隊大分分屯地で、９棟の大型弾薬庫新設工事が始まる11月29日を前に大分市では同月22日、市民が「ミサイ..