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.

A Japanese company is pushing ahead with plans to launch a private moon lander by the end of 2022, a year packed with other moonshot ambitions and rehearsals that could foretell how soon humans get back to the lunar surface. If the plans hold, the company, ispace, which is based in Tokyo, would accomplish the first intact landing by a Japanese spacecraft on the moon. And by the time it arrives, it may find other new visitors that already started exploring the moon's regolith this year from Russia and the United States. Other missions in 2022 plan to orbit the moon, particularly the NASA Artemis-1 mission, a crucial uncrewed test of the American hardware that is to carry astronauts back to the moon. South Korea could also launch its first lunar orbiter later this year.

Analysis For campaigners hoping that 2022 could be the year that deepfaked imagery falls within a stricter legal purview, the early indicators are unpromising. Last Thursday the European Parliament ratified amendments to the Digital Services Act (DSA, due to take effect in 2023), in regards to the dissemination of deepfakes. The modifications address deepfakes across two sections, each directly related to online advertising: amendment 1709 pertaining to Article 30, and a related amendment to article 63. 'Where a very large online platform becomes aware that a piece of content is a generated or manipulated image, audio or video content that appreciably resembles existing persons, objects, places or other entities or events and falsely appears to a person to be authentic or truthful (deep fakes), the provider shall label the content in a way that informs that the content is inauthentic and that is clearly visible for the recipient of the services.' The second adds text to the existing article 63, which is itself mainly concerned with increasing the transparency of large advertising platforms. 'In addition, very large online platforms should label any known deep fake videos, audio or other files.'

Many applications affecting human lives rely on models that have come to be known under the umbrella of machine learning and artificial intelligence. These AI models are usually complicated mathematical functions that map from an input space to an output space. Stakeholders are interested to know the rationales behind models' decisions and functional behavior. We study this functional behavior in relation to the data used to create the models. On this topic, scholars have often assumed that models do not extrapolate, i.e., they learn from their training samples and process new input by interpolation. This assumption is questionable: we show that models extrapolate frequently; the extent of extrapolation varies and can be socially consequential. We demonstrate that extrapolation happens for a substantial portion of datasets more than one would consider reasonable. How can we trust models if we do not know whether they are extrapolating? Given a model trained to recommend clinical procedures for patients, can we trust the recommendation when the model considers a patient older or younger than all the samples in the training set? If the training set is mostly Whites, to what extent can we trust its recommendations about Black and Hispanic patients? Which dimension (race, gender, or age) does extrapolation happen? Even if a model is trained on people of all races, it still may extrapolate in significant ways related to race. The leading question is, to what extent can we trust AI models when they process inputs that fall outside their training set? This paper investigates several social applications of AI, showing how models extrapolate without notice. We also look at different sub-spaces of extrapolation for specific individuals subject to AI models and report how these extrapolations can be interpreted, not mathematically, but from a humanistic point of view.

To benefit from AI advances, users and operators of AI systems must have reason to trust it. Trust arises from multiple interactions, where predictable and desirable behavior is reinforced over time. Providing the system's users with some understanding of AI operations can support predictability, but forcing AI to explain itself risks constraining AI capabilities to only those reconcilable with human cognition. We argue that AI systems should be designed with features that build trust by bringing decision-analytic perspectives and formal tools into AI. Instead of trying to achieve explainable AI, we should develop interpretable and actionable AI. Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations. In doing so, it will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making and ensure broad benefits from deploying and advancing its computational capabilities.

Terrestrial vegetation is a critical component of global biogeochemical cycles and provides important ecosystem services to support human life [1]. Given its importance, it is essential to know the spatial-temporal variations of vegetation [2]. These variations are due to several determining factors such as global climate variability, climate gradients, and anthropogenic factors such as Land Use and Land Cover Change (LULCC). The diversity in climatic conditions and vegetation types pose different obstacles to monitoring and classifying land cover using remote sensing. Mexico is considered one of the mega-diverse countries on the planet due to its location in a transition zone between Nearctic and Neotropic regions making it more difficult for land use classification and monitoring. The anthropogenic factors, could be a trigger for deforestation and forest degradation [3] and have a severe impact on the global carbon cycle, soil erosion, hydrological cycles, and in general, affect on the ecosystem services that sustain society [4]. As a result, timely land cover monitoring and classification are of crucial importance for assessing gradual degradation-ecosystem processes. Furthermore, it is important to be in line with the United Nations Sustainable Development Goals (SDGs) specifically SDG 15 concerning "Life on Land" [5].

Language models increasingly rely on massive web dumps for diverse text data. However, these sources are rife with undesirable content. As such, resources like Wikipedia, books, and newswire often serve as anchors for automatically selecting web text most suitable for language modeling, a process typically referred to as quality filtering. Using a new dataset of U.S. high school newspaper articles -- written by students from across the country -- we investigate whose language is preferred by the quality filter used for GPT-3. We find that newspapers from larger schools, located in wealthier, educated, and urban ZIP codes are more likely to be classified as high quality. We then demonstrate that the filter's measurement of quality is unaligned with other sensible metrics, such as factuality or literary acclaim. We argue that privileging any corpus as high quality entails a language ideology, and more care is needed to construct training corpora for language models, with better transparency and justification for the inclusion or exclusion of various texts.

Legendary director James Cameron has warned of the dangers that deepfakes pose to society. Deepfakes leverage machine learning and AI techniques to convincingly manipulate or generate visual and audio content. Their high potential to deceive makes them a powerful tool for spreading disinformation, committing fraud, trolling, and more. "Every time we improve these tools, we're actually in a sense building a toolset to create fake media -- and we're seeing it happening now," said Cameron in a BBC video interview. "Right now the tools are -- the people just playing around on apps aren't that great. But over time, those limitations will go away. Things that you see and fully believe you're seeing could be faked."

Cyber-activists opposed to the president of Belarus, Alexander Lukashenko, say they have penetrated the state-run railway's computer system and threatened to paralyse trains moving Russian troops and artillery to the country for a potential attack on Ukraine. Their goals include freeing political prisoners, removing Russian soldiers from Belarus, and preventing Belarusians from "dying for this meaningless war", a person involved in the attack told the Guardian. A member of the "Cyberpartisans" said the hacktivist group had so far encrypted or destroyed internal databases that the Belarusian railways use to control traffic, customs and stations, an action that could cause delays to commercial and non-commercial trains and "indirectly affect Russia troops movement". They had so far avoided taking more drastic steps to paralyse trains by downing the signalling and emergency control systems, but said they "might do that in the future if we're confident innocent people won't get injured as a result". The group has demanded that Belarus cease serving as a staging ground for a buildup of Russian troops and military weaponry, some of it just miles from the Ukrainian border.

NASA's Perseverance rover has tried out a nifty new feature for the first time, which let it'spit out' a piece of Mars rock that had been clogging its sampling tube. The trick means that Perseverance can now continue taking samples of rock from the Red Planet to search for possible signs of ancient life. The SUV-sized vehicle has been on the Red Planet since February 2021, and is slowly trundling through the Jezero Crater taking rock samples for later retrieval. On December 29, while retrieving a sample from a rock, its sixth so far, NASA engineers found they couldn't get the rock to go into the storage area. This was due to a pebble-sized piece of debris obstructing the robotic arm, blocking the entrance to the tube docking area - nearly a month later, this has been solved. NASA used an untested'un-choking procedure', that involved pointing the drill containing a clogged test tube towards the ground and rotating it at high speed.