The search engine's definitions have been replaced with AI Overviews. Google appears to be running into some hiccups after the company began rolling out its updated, and even more AI-focused search experience at I/O 2026. Currently, searching for the words disregard, stop or ignore on Google no longer displays a snippet with a definition, and instead offers an AI Overview and a lot of blank space. Because users have complained about the issue on social media, and publications like and have reported on it, even if you don't get a definition, you might still get a collection of links to articles documenting the issue before the traditional list of links. Multiple members of Engadget's staff were able to recreate the strange AI Overview responses with their own personal Google searches.

Recent research illustrates how AI can be developed and deployed in a manner detached from the concrete social context of application. By abstracting from the contexts of AI application, practitioners also disengage from the distinct normative structures that govern them. Building upon Helen Nissenbaum's framework of contextual integrity, I illustrate how disregard for contextual norms can threaten the integrity of a context with often decisive ethical implications. I argue that efforts to promote responsible and ethical AI can inadvertently contribute to and seemingly legitimize this disregard for established contextual norms. Echoing a persistent undercurrent in technology ethics of understanding emerging technologies as uncharted moral territory, certain approaches to AI ethics can promote a notion of AI as a novel and distinct realm for ethical deliberation, norm setting, and virtue cultivation. This narrative of AI as new ethical ground, however, can come at the expense of practitioners, policymakers and ethicists engaging with already established norms and virtues that were gradually cultivated to promote successful and responsible practice within concrete social contexts. In response, I question the current narrow prioritization in AI ethics of moral innovation over moral preservation. Engaging also with emerging foundation models, I advocate for a moderately conservative approach to the ethics of AI that prioritizes the responsible and considered integration of AI within established social contexts and their respective normative structures.

The world is facing the collapse of the 1948 international order established in the wake of World War II, amid the brutal wars in Gaza and Ukraine, while authoritarian policies continue to spread, Amnesty International has warned. The report accused the world's most powerful governments, including China, Russia and the United States, of leading the global disregard for international rules and values enshrined in the Universal Declaration of Human Rights of December 1948. The war in Gaza, which began on October 7, was a "descent into hell", Secretary-General Agnes Callamard wrote in her preface to the report, where "the'never again' moral and legal lessons [of 1948] were torn into a million pieces". Noting that Hamas had committed "horrific crimes" in its assault on communities in southern Israel on October 7, Callamard said Israel's "campaign of retaliation" had become a "campaign of collective punishment". Amnesty said while Israel continued to disregard international human rights law, the US, its foremost ally, and other countries including the United Kingdom and Germany were guilty of "grotesque double standards" given their willingness to back Israeli and US authorities over Gaza while condemning war crimes by Russia in Ukraine.

Protecting against adversarial attacks is a common multiagent problem. Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans. Previous results show that modeling human behavior can significantly improve the performance of the algorithms. However, modeling humans correctly is a complex problem, and the models are often simplified and assume humans make mistakes according to some distribution or train parameters for the whole population from which they sample. In this work, we use data gathered by psychologists who identified personality types that increase the likelihood of performing malicious acts. However, in the previous work, the tests on a handmade game could not show strategic differences between the models. We created a novel model that links its parameters to psychological traits. We optimized over parametrized games and created games in which the differences are profound. Our work can help with automatic game generation when we need a game in which some models will behave differently and to identify situations in which the models do not align.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology'to mitigate' its risks. A class-action complaint filed Wednesday in the northern district of California alleges tech leaders OpenAI and Microsoft Corp. used "stolen and misappropriated" information from hundreds of millions of internet users without their knowledge to train and develop its artificial intelligence tech like chatbot ChatGPT. The 16 plaintiffs, who are represented by the Clarkson Law Firm and listed with initials, claimed the defendants "continue to unlawfully collect and feed additional personal data from millions" worldwide to that end and that they systematically scraped 300 billion words from the internet without consent. "Once trained on stolen data, defendants saw the immediate profit potential and rushed the products to market without implementing proper safeguards or controls to ensure that they would not produce or support harmful or malicious content and conduct that could further violate the law, infringe rights and endanger lives," Clarkson continued. "Without these safeguards, the products have already demonstrated their ability to harm humans, in real ways."

Though Elon Musk has famously warned humanity about the dangers of artificial intelligence, his shareholders might be well-served by having an algorithm on Tesla's board of directors. In recent years, Tesla has become a cautionary tale for how difficult it is for part-time directors to oversee charismatic, strong-willed CEOs--especially ones who are the founding visionaries of their companies. Given how Elon Musk has landed the company in hot water with the Securities and Exchange Commission with his erratic tweets and mocking disregard for the regulatory regime dictating the proper behavior of a publicly traded company, it's little wonder that Tesla's board has been accused of being "asleep at the wheel." Perhaps their seeming unwillingness to rein him in is due to the Tesla directors' personal loyalty to Musk. Or maybe they simply don't want to spend the time to "preapprove" Musk's tweets about the company, especially with the less conventional hours and fast pace the CEO keeps.

"There is a battle going on for fairness, inclusion and justice in the digital world." Darren Walker, president of the Ford Foundation, was referring to burgeoning research that has uncovered systematic racial, gender, and other biases built into algorithms used for everything from Netflix "recommended" titles to surveillance systems. In one dramatic case, researchers testing facial-recognition software found that it was reliable – when using photos of white males. But for photos of darker-skinned people, the systems failed as much as 35 percent of the time to correctly identify the gender of black women (Lhor, 2018). Such facial-recognition technology is being rapidly developed for a range of applications.

On giant screens in the European parliament building in Brussels last week, the face of Mark Zuckerberg looked down on the world's data protection and privacy commissioners assembled there for their annual conference. What he said was cautious and rather bland, but the imagery was potent: a young Big Brother issuing a tailored message to those who administer the laws of many lands. Zuckerberg did not take questions – a Facebook executive in the chamber did, after Zuckerberg faded from the screens into the green and sunny background of his distant locale. An actual dialogue with the controller of Facebook might have been illuminating. For example, does Facebook anticipate, as others speculate, that the internet will split into two, or three – the US internet, the China internet and the EU internet?

On giant screens in the European parliament building in Brussels last week, the face of Mark Zuckerberg looked down on the world's data protection and privacy commissioners assembled there for their annual conference. What he said was cautious and rather bland, but the imagery was potent: a young Big Brother issuing a tailored message to those who administer the laws of many lands. Zuckerberg did not take questions – a Facebook executive in the chamber did, after Zuckerberg faded from the screens into the green and sunny background of his distant locale. An actual dialogue with the controller of Facebook might have been illuminating. For example, does Facebook anticipate, as others speculate, that the internet will split into two, or three – the US internet, the China internet and the EU internet?

The "black box" effect occurs when algorithms learn and behave in ways that humans cannot understand. This happens primarily with deep learning models, in which the algorithm is given a goal, and it accomplishes that goal without any visible rationale on a step-by-step basis. AlphaGo is a good example of this type of learning -- the algorithm was given a goal (to win a game of the ancient Chinese game Go) and then figured out through self-play how to accomplish that goal. For most moves, though, the expert Go players thought AlphaGo was making bad decisions because they could not understand the rationale for each individual move. But in the end, through this series of apparently suboptimal moves, AlphaGo ended up winning against a world master.