'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.

Race on to establish globally recognised'AI-free' logo Organisations worldwide are racing to develop a universally recognised label for human-made products and services as part of the growing backlash against AI use. Declarations like Proudly Human, Human-made, 'No A.I and AI-free are appearing across films, marketing, books and websites. It is in response to fears that jobs or entire professions are being swept away in a wave of AI-powered automation. BBC News has counted at least eight different initiatives trying to come up with a label that could get the kind of global recognition that the Fair Trade logo has for ethically made products. But with so many competing labels - as well as confusion over the definition of AI-free - experts say consumers are in danger of being left confused unless a single standard can be agreed on.

Author ordering is determined by coin flip. For what purpose was the dataset created? Was there a specific task in mind? In order to systematically compare the location encoders' performance and their impact on the Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? Dr. Gengchen Mai acknowledges the Microsoft Research What do the instances that comprise the dataset represent (e.g., documents, photos, people, The instances in all 17 datasets represent images.

We focus on the setting where firms are not aware of demand functions and how reference prices areformed but haveaccess to an oracle that provides a measure of consumers' responsiveness to the current posted prices.

Online retailing has become increasingly popular over the last decades [17, 28, 52]. The way of product ranking is the crux for online retailers because it determines the consumers' shopping behaviors [17] and thus influences the retailers' revenue [20, 49]. For instance, the probability of consumers' purchasing from a firm or clicking an advertisement is strongly related to the display order[8,3,33].