Trump's 50 percent Canada tariffs: What to know US President Donald Trump's administration has said it will expand a voluntary pledge seeking to shield consumers from the energy costs of the rapid expansion of data centres, mostly used by artificial intelligence companies. The White House announced on Thursday that it would add state governors and electricity companies to the agreement, first announced with tech and AI firms in March. But the US administration stopped short of any enforceable protections. The pledge is a "public commitment that hyperscalers, AI companies, and the utilities and data-center developers behind them will build, bring, or buy every kilowatt their facilities need -- and cover every dollar of the infrastructure that delivers it". It says consumers would not foot the bill for AI's energy needs.

The European Union has fined Google 890 million euros ($1bn), saying the technology giant broke digital antitrust rules by steering users of Google Play and its search engine towards its own services and apps at the expense of rivals. Thursday's penalty is the latest in Brussels' crackdown on Big Tech, which has seen the bloc lead the world in reining in the largest firms from Silicon Valley to Beijing. The European Commission, the bloc's executive branch, said it was acting in the interest of consumers. "The best products should succeed because they're better, not because they're owned by the company running the search engine. And European consumers have a right to be told by app developers where to sign up to the best offers, even when the app store owner does not get a cut," said Teresa Ribera, the commission's executive vice president for clean, just and competitive transition.

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Firms' algorithm development practices are often homogeneous. Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions. In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing firms, but whether or not this conduct is legal depends on the mechanisms of achieving collusion. We investigate the precise mechanisms through a formal game-theoretic model. Indeed, we find that (1) higher correlation diminishes consumer welfare and (2) as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination. We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results demonstrate a new mechanism for achieving collusion through correlation, which allows us to analyze its legal implications. Correlation through algorithms is a new frontier of anti-competitive behavior that is largely unconsidered by US antitrust law.

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PCWorld highlights Acer's PM1 system, a portable USB-C monitor with magnetic keyboard that transforms smartphones into laptop-like devices using features like Samsung DeX and Google Desktop Mode.

'Creepy' Listening Tool for Targeted Ads Didn't Actually Work, FTC Says Three firms will pay nearly $1 million for selling "Active Listening" technology that they claimed tapped people's phones for advertising. The FTC alleges the "tech" was just pricey email lists. The Federal Trade Commission announced on Thursday that Cox Media Group and two other marketing companies, MindSift LLC and 1010 Digital Works, have agreed to collectively pay nearly $1 million to settle allegations that they deceived their customers--other businesses--by claiming that they could help target ads based on audio recordings collected from consumers' smart devices via a marketing service called Active Listening. In a statement to WIRED, a spokesperson for CMG says, "We are pleased to have this matter resolved. Our local marketing team relied on marketing materials provided to us by a third-party vendor about their product. We withdrew the materials expeditiously and stopped further use of the product."

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.

Two-sided marketplaces are standard business models of many online platforms (e.g., Amazon, Facebook, LinkedIn), wherein the platforms have consumers, buyers or content viewers on one side and producers, sellers or content-creators on the other. Consumer side measurement of the impact of a treatment variant can be done via simple online A/B testing. Producer side measurement is more challenging because the producer experience depends on the treatment assignment of the consumers. Existing approaches for producer side measurement are either based on graph cluster-based randomization or on certain treatment propagation assumptions. The former approach results in low-powered experiments as the producer-consumer network density increases and the latter approach lacks a strict notion of error control. In this paper, we propose (i) a quantification of the quality of a producer side experiment design, and (ii) a new experiment design mechanism that generates high-quality experiments based on this quantification.