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。

In the wake of rolling out its own proposal to significantly limit a key law protecting internet users’ speech in the summer of 2020, the Department of Justice under the first Trump administration actively worked with lawmakers to support further efforts to stifle online speech.

The new documents, disclosed in an EFF Freedom of Information Act (FOIA) lawsuit, show officials were talking with Senate staffers working to pass speech- and privacy-chilling bills like the EARN IT Act and PACT Act (neither became law). DOJ officials also communicated with an organization that sought to condition Section 230’s legal protections on websites using age-verification systems if they hosted sexual content.

Section 230 protects users’ online speech by protecting the online intermediaries we all rely on to communicate on blogs, social media platforms, and educational and cultural platforms like Wikipedia and the Internet Archive. Section 230 embodies the principle that we should all be responsible for our own actions and statements online, but generally not those of others. The law prevents most civil suits against users or services that are based on what others say.

DOJ’s work to weaken Section 230 began before President Donald Trump issued an executive order targeting social media services in 2020, and officials in DOJ appeared to be blindsided by the order. EFF was counsel to plaintiffs who challenged the order, and President Joe Biden later rescinded it. EFF filed two FOIA suits seeking records about the executive order and the DOJ’s work to weaken Section 230.

The DOJ’s latest release provides more detail on a general theme that has been apparent for years: that the DOJ in 2020 flexed its powers to try to undermine or rewrite Section 230. The documents show that in addition to meeting with congressional staffers, DOJ was critical of a proposed amendment to the EARN IT Act, with one official stating that it “completely undermines” the sponsors’ argument for rejecting DOJ’s proposal to exempt so-called “Bad Samaritan” websites from Section 230.

Further, DOJ reviewed and proposed edits to a rulemaking petition to the Federal Communications Commission that tried to reinterpret Section 230. That effort never moved forward given the FCC lacked any legal authority to reinterpret the law.

You can read the latest release of documents here, and all the documents released in this case are here.

Related Cases: EFF v. OMB (Trump 230 Executive Order FOIA)

　ノンフィクション･ジャンルからチョイスした本の紹介です(刊行順･販価は税別)◆伊藤俊治『昭和百年への鎮魂─江成常夫のレンズがとらえた戦争』集英社新書 8/8刊 1800円“戦争の昭和”を正確に撮影してきた写真家・江成常夫。彼はアジア･太平洋戦争の実相にカメラを向け続けてきた。沖縄戦の犠牲者が逃げ込んだ洞窟の痕跡、戦争花嫁、満洲に取り残された日本人戦争孤児、ヒロシマ・ナガサキの被爆者、戦火の傷跡を刻んだ遺骨や遺品……。数多くの写真を通して彼の仕事の本質を解明し、もう一つの戦後..

The White House’s recently-unveiled “AI Action Plan” wages war on so-called “woke AI”—including large language models (LLMs) that provide information inconsistent with the administration’s views on climate change, gender, and other issues. It also targets measures designed to mitigate the generation of racial and gender biased content and even hate speech. The reproduction of this bias is a pernicious problem that AI developers have struggled to solve for over a decade.

A new executive order called “Preventing Woke AI in the Federal Government,” released alongside the AI Action Plan, seeks to strong-arm AI companies into modifying their models to conform with the Trump Administration’s ideological agenda.

The executive order requires AI companies that receive federal contracts to prove that their LLMs are free from purported “ideological biases” like “diversity, equity, and inclusion.” This heavy-handed censorship will not make models more accurate or “trustworthy,” as the Trump Administration claims, but is a blatant attempt to censor the development of LLMs and restrict them as a tool of expression and information access. While the First Amendment permits the government to choose to purchase only services that reflect government viewpoints, the government may not use that power to influence what services and information are available to the public. Lucrative government contracts can push commercial companies to implement features (or biases) that they wouldn't otherwise, and those often roll down to the user. Doing so would impact the 60 percent of Americans who get information from LLMs, and it would force developers to roll back efforts to reduce biases—making the models much less accurate, and far more likely to cause harm, especially in the hands of the government. 

Less Accuracy, More Bias and Discrimination

It’s no secret that AI models—including gen AI—tend to discriminate against racial and gender minorities. AI models use machine learning to identify and reproduce patterns in data that they are “trained” on. If the training data reflects biases against racial, ethnic, and gender minorities—which it often does—then the AI model will “learn” to discriminate against those groups. In other words, garbage in, garbage out. Models also often reflect the biases of the people who train, test, and evaluate them. 

This is true across different types of AI. For example, “predictive policing” tools trained on arrest data that reflects overpolicing of black neighborhoods frequently recommend heightened levels of policing in those neighborhoods, often based on inaccurate predictions that crime will occur there. Generative AI models are also implicated. LLMs already recommend more criminal convictions, harsher sentences, and less prestigious jobs for people of color. Despite that people of color account for less than half of the U.S. prison population, 80 percent of Stable Diffusion's AI-generated images of inmates have darker skin. Over 90 percent of AI-generated images of judges were men; in real life, 34 percent of judges are women. 

These models aren’t just biased—they’re fundamentally incorrect. Race and gender aren’t objective criteria for deciding who gets hired or convicted of a crime. Those discriminatory decisions reflected trends in the training data that could be caused by bias or chance—not some “objective” reality. Setting fairness aside, biased models are just worse models: they make more mistakes, more often. Efforts to reduce bias-induced errors will ultimately make models more accurate, not less. 

Biased LLMs Cause Serious Harm—Especially in the Hands of the Government

But inaccuracy is far from the only problem. When government agencies start using biased AI to make decisions, real people suffer. Government officials routinely make decisions that impact people’s personal freedom and access to financial resources, healthcare, housing, and more. The White House’s AI Action Plan calls for a massive increase in agencies’ use of LLMs and other AI—while all but requiring the use of biased models that automate systemic, historical injustice. Using AI simply to entrench the way things have always been done squanders the promise of this new technology.

We need strong safeguards to prevent government agencies from procuring biased, harmful AI tools. In a series of executive orders, as well as his AI Action Plan, the Trump Administration has rolled back the already-feeble Biden-era AI safeguards. This makes AI-enabled civil rights abuses far more likely, putting everyone’s rights at risk. 

And the Administration could easily exploit the new rules to pressure companies to make publicly available models worse, too. Corporations like healthcare companies and landlords increasingly use AI to make high-impact decisions about people, so more biased commercial models would also cause harm. 

We have argued against using machine learning to make predictive policing decisions or other punitive judgments for just these reasons, and will continue to protect your right not to be subject to biased government determinations influenced by machine learning.

令和7年度地方財政審議会（7月2日）議事要旨

令和7年度地方財政審議会（7月15日）議事要旨

郵便貯金銀行及び郵便保険会社に係る移行期間中の業務の制限等に関する命令の一部を改正する命令（案）に関する意見募集の結果等