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Tinder is charging over-30s up to 48% more

Daily Mail - Science & tech

Tinder is charging people over 30 up to 48 per cent more for its premium service, an investigation has revealed. Which? said its findings suggest possible discrimination and a potential breach of UK law by the popular dating app. The consumer group also initially accused Tinder of hiking prices for young gay and lesbian users aged 18-29, but has since backtracked on this. A statement from Which? said: 'Having initially chosen not to provide further information, Tinder has since revealed that it offers discounts to users aged 28 and under in the UK.' It added that the dating app'claimed that by including 29-year-olds in our analysis of the relationship between price with age and sexual orientation, "the results would be skewed to make it appear that LGBTQAI members paid more based upon orientation, when in fact, it was based upon age".' Which? said that in light of the new information, it has'no evidence that sexual orientation impacts pricing for young Tinder users'. Tinder had previously said it was'categorically untrue' that its pricing structure discriminates by sexual preference.


Tinder is charging young gay and lesbian users and over-30s up to 48% more

Daily Mail - Science & tech

Tinder is charging young gay and lesbian users and people over 30 up to 48 per cent more for its premium service, an investigation has revealed. Consumer group Which? said its findings suggest possible discrimination and a potential breach of UK law by the popular dating app. Tinder said it was'categorically untrue' that its pricing structure discriminates by sexual preference. It would not explain why people are charged different prices for its Tinder Plus service, rather than just a blanket fee, but did admit that older people have to pay more in some countries. The dating app claimed that this price difference was'a discount for younger users', but Which?


The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

arXiv.org Artificial Intelligence

There is mounting public concern over the influence that AI based systems has in our society. Coalitions in all sectors are acting worldwide to resist hamful applications of AI. From indigenous people addressing the lack of reliable data, to smart city stakeholders, to students protesting the academic relationships with sex trafficker and MIT donor Jeffery Epstein, the questionable ethics and values of those heavily investing in and profiting from AI are under global scrutiny. There are biased, wrongful, and disturbing assumptions embedded in AI algorithms that could get locked in without intervention. Our best human judgment is needed to contain AI's harmful impact. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.


Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks

arXiv.org Artificial Intelligence

Most existing personalization systems promote items that match a user's previous choices or those that are popular among similar users. This results in recommendations that are highly similar to the ones users are already exposed to, resulting in their isolation inside familiar but insulated information silos. In this context, we develop a novel recommendation framework with a goal of improving information diversity using a modified random walk exploration of the user-item graph. We focus on the problem of political content recommendation, while addressing a general problem applicable to personalization tasks in other social and information networks. For recommending political content on social networks, we first propose a new model to estimate the ideological positions for both users and the content they share, which is able to recover ideological positions with high accuracy. Based on these estimated positions, we generate diversified personalized recommendations using our new random-walk based recommendation algorithm. With experimental evaluations on large datasets of Twitter discussions, we show that our method based on \emph{random walks with erasure} is able to generate more ideologically diverse recommendations. Our approach does not depend on the availability of labels regarding the bias of users or content producers. With experiments on open benchmark datasets from other social and information networks, we also demonstrate the effectiveness of our method in recommending diverse long-tail items.