Illinois law permits people to sue companies that don't get consent before collecting consumers' data. Attorneys that sued Facebook in 2015 alleged that the company's photo tagging feature was powered by facial recognition data used to create and store "face templates."

It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference. We, however, hypothesize that a different `mechanism', hitherto not articulated in the literature, may also be responsible for machine's bias, namely that biases may originate from (i) the programmers' cultural background, such as education or line of work, or (ii) the contextual programming environment, such as software requirements or developer tools. Combining an experimental and comparative design, we studied the effects of cultural metaphors and contextual metaphors, and tested whether each of these would `transfer' from the programmer to program, thus constituting a machine bias. The results show (i) that cultural metaphors influence the programmer's choices and (ii) that `induced' contextual metaphors can be used to moderate or exacerbate the effects of the cultural metaphors. This supports our hypothesis that biases in automated systems do not always originate from within the machine's training data. Instead, machines may also `replicate' and `reproduce' biases from the programmers' cultural background by the transfer of cultural metaphors into the programming process. Implications for academia and professional practice range from the micro programming-level to the macro national-regulations or educational level, and span across all societal domains where software-based systems are operating such as the popular AI-based automated decision support systems.

Solar activity It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on performance evaluation of BELFIS as a predictor by forecasting solar cycles 16 to 24. The performance of BELFIS is compared with other computational models used for this purpose, and in particular with adaptive neuro-fuzzy inference system (ANFIS).

Our work fits within a larger context of recent advances in RL. RL has been used to train AIs to win competitive games, such as Go, Dota, and Starcraft. In those settings, the RL objective is inherently adversarial ("beat-the-other-team"). Machine learning has also been used for the design of auction rules. In this work, we instead focus on the opportunity to use AI to promote social welfare through the design of optimal tax policies in dynamic economies. Many studies have shown that high income inequality can negatively impact economic growth and economic opportunity.

It automates the process by which companies and website owners make their content accessible to users with hearing, visual, and motor impairments as well as some other functional disabilities. The total funding company raised is $12.5M in funding over 2 rounds. It's an AI-based solution scans website and automatically offers key modifications to transmit data and accessible content to end-users in a manner compliant with the international and US disability standards, which includes the Web Content Accessibility Guidelines and the Americans with Disabilities Act. Executive Opinion Shir Ekerling, Co-founder and CEO of accessiBe, said, "What excites us most about our partnership with K1 is that now, with the amazing support of our investors, we can bring accessibility to the world. Our vision is to make the internet truly accessible to everyone. By utilizing machine learning, our solution can help millions of businesses comply with legislation and avoid lawsuits on the one hand, while enabling users with disabilities to browse the internet effectively on the other. Mike Velcich, Principal at K1, said, "K1 is excited to partner with Shir Ekerling and the accessiBe team as they continue their rapid global expansion.

Latanya Sweeney is a professor of government and technology in residence at Harvard University's Department of Government, editor-in-chief of Technology Science and the founding director of the Technology Science Initiative and the Data Privacy Lab at the Institute for Quantitative Social Science at Harvard. Max Weiss is a senior at Harvard University and the student who implemented the Deepfake Text experiment. As federal agencies take increasingly stringent actions to try to limit the spread of the novel coronavirus pandemic within the U.S., how can individual Americans and U.S. companies affected by these rules weigh in with their opinions and experiences? Because many of the new rules, such as travel restrictions and increased surveillance, require expansions of federal power beyond normal circumstances, our laws require the federal government to post these rules publicly and allow the public to contribute their comments to the proposed rules online. But are federal public comment websites -- a vital institution for American democracy -- secure in this time of crisis?

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The technology company Agric10x announced a collaboration with the government of India. Their work is aimed at creating a connection between farmers, enterprises and markets. As a result, farmers in the country will be able to sell their products faster and with the greatest profit directly to consumers. Agric10x is an agricultural E-Market with artificial intelligence and blockchain. Using the platform, farmers will be able to sell their products and minimize the number of intermediaries.

UK's estimated coronavirus infection rate is now between 0.7 and 1 The UK's coronavirus R value – the estimated number of people each person infects – is now between 0.7 and 1, according to the government's scientific advisory group for emergencies (SAGE). Five days ago, UK prime minister Boris Johnson said R was between 0.5 and 0.9. The government's science advisors say the increase is not a reflection of coronavirus restrictions being eased in England this week, but rather due to a lag in the data that is used to model the R value. We won't know how easing restrictions has impacted the current R value for another three weeks. Only 1500 of a total of 18,000 coronavirus contact tracers – just over 8 per cent – have been recruited by the UK government by its mid-May deadline, a cabinet minister said today. The government had previously refused to say exactly how many contact tracers it had employed. Up to 8 million people could be on waiting lists for National Health Service (NHS) ...

Amid the COVID-19 pandemic, federal agencies are broadening and expediting robotic process automation efforts to deal with disruptions and drive productivity. During a virtual ATARC panel Tuesday, officials from three agencies offered a glimpse into automaton use cases that are boosting operations, detailed how they're gaining buy-in from personnel along the way and considered how the present pandemic-induced reality could transform the future of work. The officials were joined by experts with insights into industry and state governments. "None of our job descriptions said'you will cut and paste, cut and paste, cut and paste.' So that's what RPA is letting us give up," Stacy Dawn, senior adviser for cybersecurity and privacy at the Housing and Urban Development Department, said.