Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.

The use of dynamic pricing by profit-maximizing firms gives rise to demand fairness concerns, measured by discrepancies in consumer groups' demand responses to a given pricing strategy. Notably, dynamic pricing may result in buyer distributions unreflective of those of the underlying population, which can be problematic in markets where fair representation is socially desirable. To address this, policy makers might leverage tools such as taxation and subsidy to adapt policy mechanisms dependent upon their social objective. In this paper, we explore the potential for AI methods to assist such intervention strategies. To this end, we design a basic simulated economy, wherein we introduce a dynamic social planner (SP) to generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours, and to use the collected tax budget to subsidize consumption among underrepresented groups. To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and finally as a full reinforcement learning (RL) problem, evaluating welfare outcomes from each case. To alleviate the difficulty in retaining meaningful tax rates that apply to less frequently occurring brackets, we introduce FairReplayBuffer, which ensures that our RL agent samples experiences uniformly across a discretized fairness space. We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13.19% in the full RL setting.

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.

The prominence of artificial intelligence (AI) has significantly grown in pop culture and science fiction over the years. It has speculated on how AI can change people's lives, the places we live and our day-to-day activities. However, despite the increase of AI in popular films such as I, Robot, Star Trek and WALL-E, it's continued depiction and futuristic tendencies throughout the years have altered individual perceptions about the true meaning of AI and how it is already playing a vital part in our everyday lives. A recent survey conducted by O'Reilly paints this exact picture. It gives AI-creators an in-depth look at how consumers identify and use AI technology, showcasing the heightened misunderstanding that consumers have of AI and its use.

Opponents of the Federal Communications Commission have outlined their chief arguments on net neutrality to a federal appeals court in Washington, in hopes of undoing the FCC's move last year to repeal its own rules for Internet service providers. The legal briefs reflect a widening front in the multipronged campaign by consumer groups and tech companies to rescue the ISP regulations, which originally barred providers from blocking websites or slowing them. With the FCC's changes, Internet providers may legally manipulate Internet traffic as it travels over their infrastructure, as long as they disclose their practices to consumers. The FCC's decision last year to repeal the rules was "arbitrary and capricious," said officials from the state of New York, the California Public Utilities Commission and others in court documents Monday -- asking the U.S. Court of Appeals for the District of Columbia Circuit to overrule the agency. The FCC was too credulous in accepting industry promises "to refrain from harmful practices," the officials said, "notwithstanding substantial record evidence showing that [Internet] providers have abused and will abuse their gatekeeper roles in ways that harm consumers and threaten public safety."

Tesla's cars will in August suddenly activate "full self-driving features," the company's chief executive Elon Musk tweeted on Sunday, three days after federal investigators said a Tesla SUV driving semi-autonomously had accelerated over 70 mph and smashed into a highway barrier. Musk's promotion to his millions of followers -- that the fantastic future of self-driving cars might only be a few months away -- appeared to give the company a leg-up in the auto industry's most competitive technological race. Tesla's stock price jumped Monday by more than 4.5 percent. A Tesla spokesperson on Monday said the cars would only start offering a limited number of as-yet-undisclosed features, not full autonomy itself. But safety experts worried the grand promises of full self-driving capabilities could lull drivers into a false sense of security for technologies that are still largely unproven on the road.

The tech is pretty cool, but don't let new developments in partially self-driving cars distract you from your responsibilities behind the wheel. A Tesla that the driver said was in Autopilot mode struck a parked police vehicle in Laguna Beach, Calif. SAN FRANCISCO -- Tesla's innovative and controversial Autopilot software -- which powers the partially self-driving features of its electric cars -- is most often used for highway driving, according to the initial findings of an MIT study using volunteer owners. The research, shared at a conference in Cambridge, Mass. Wednesday, came a day after the latest crash of a Tesla using Autopilot, and as two consumer groups renewed criticism of the software's name and marketing, which they say dangerously misleads drivers.

Crashes involving drivers using Tesla's semi-automated Autopilot feature when those accidents occurred spurred two consumer groups to call on U.S. regulators to investigate company claims about the technology they call deceptive and that contribute to a misunderstanding of its limitations. In a letter sent to the Federal Trade Commission on Wednesday, the Center for Auto Safety and Consumer Watchdog said marketing of Autopilot's capabilities by Tesla and CEO Elon Musk's public comments have led customers to believe their cars are capable of driving themselves. There've been two U.S. fatalities involving Tesla drivers who had Autopilot engaged when the accidents happened, and the National Transportation Safety Board is investigating two recent crashes in which Model S drivers in Los Angeles and Utah plowed into parked fire trucks while using it. "Consumers in the market for a new Tesla see advertisements proclaiming, Full Self-Driving Hardware on All Cars." They are directed to videos of Tesla vehicles driving themselves through busy public roads, with no human operation whatsoever," the groups said in their joint letter to FTC Chairman Joseph Simons. "They see press releases alleging that Autopilot reduces the likelihood of an accident by 40%.

As supporters and critics debate self-driving vehicles, 125,000 senior citizens who live in a central Florida retirement community will take them for a ride in the world's largest self-driving experiment. Voyage, an autonomous vehicle (AV) startup specializing in a robo-taxi service, will pick them up at their homes and drive them free of charge to and from grocery stores, theaters, pools, golf and tennis with only a "technician" on board to monitor the system -- and take the wheel if necessary. Later on, the technician will be dropped and a transportation fee added. If this rollout proves successful, it could pave the way for AVs to assist seniors nationwide with needed services. It could also give a lift to this fledgling industry at a time when automakers are coming under fire for moving too fast on self-driving vehicles -- and the federal government for moving too slowly. But in this community, older Americans seem to like it.

Before you start this tutorial, set up your device. In the article, you set up your Azure IoT device and IoT hub, and you deploy a sample application to run on your device. The application sends collected sensor data to your IoT hub. Machine learning is a technique of data science that helps computers learn from existing data to forecast future behaviors, outcomes, and trends. Azure Machine Learning is a cloud predictive analytics service that makes it possible to quickly create and deploy predictive models as analytics solutions.