In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences. Building and maintaining public trust in AI has been identified as the key to successful and sustainable innovation. This chapter discusses the challenges related to operationalizing ethical AI principles and presents an integrated view that covers high-level ethical AI principles, the general notion of trust/trustworthiness, and product/process support in the context of responsible AI, which helps improve both trust and trustworthiness of AI for a wider set of stakeholders.

Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have led to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems' disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance. We close with alternative visions for a more equitable future for educational AI research.

Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophistication displayed by attackers in hiding their activity. However, existing methods often exhibit important limitations in terms of the quantity and relevance of the generated alerts. Recently, knowledge graphs are finding application in the cybersecurity domain, showing the potential to alleviate some of these drawbacks thanks to their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies. We discuss the application of machine learning on knowledge graphs for intrusion detection and experimentally evaluate a link-prediction method for scoring anomalous activity in industrial systems. After initial unsupervised training, the proposed method is shown to produce intuitively well-calibrated and interpretable alerts in a diverse range of scenarios, hinting at the potential benefits of relational machine learning on knowledge graphs for intrusion detection purposes.

Future advances in AI that automate away human labor may have stark implications for labor markets and inequality. This paper proposes a framework to analyze the effects of specific types of AI systems on the labor market, based on how much labor demand they will create versus displace, while taking into account that productivity gains also make society wealthier and thereby contribute to additional labor demand. This analysis enables ethically-minded companies creating or deploying AI systems as well as researchers and policymakers to take into account the effects of their actions on labor markets and inequality, and therefore to steer progress in AI in a direction that advances shared prosperity and an inclusive economic future for all of humanity.

According the LA Times, the California Department of Motor Vehicles appears to be actively investigating Tesla over CEO Elon Musk's audacious claims about his company's Full Self-Driving technology. The news comes barely a week after Tesla engineers privately admitted to the DMV that Musk had exaggerated the FSD system's capabilities on social media. The FSD is a $10,000 option for Tesla models and promises to do everything from change lanes in freeway traffic and take exits on its own to independently stopping at traffic lights and signs. However, this does not make them "fully" autonomous. Tesla vehicles currently operate at Level 2 autonomy, director of Autopilot software CJ Moore told DMV investigators on a March 9th teleconference call.

Welcome back to the "Life in 2050" series! So far, we've looked at how ongoing developments in science, technology, and geopolitics will be reflected in terms of warfare and the economy. Today, we are shifting gears a little and looking at how the turbulence of this century will affect the way people live from day to day. As noted in the previous two installments, changes in the 21st century will be driven by two major factors. These include the disruption caused by rapidly accelerating technological progress, and the disruption caused by rising global temperatures, and the environmental impact this will have (aka. These factors will be pulling the world in opposite directions, and simultaneously at that.

… today they will extend the comment period on the request for information on financial institutions’ use of artificial intelligence (AI) until July 1, 2021.

Stock futures cut some losses last week on Thursday and Friday as markets rallied, but today looks like more of the same with selling pressure out of the early session. Inflation worries amid a massive corporate earnings quarter saw the S&P 500 fall as much as 4% last week, so if one thing is for sure, it is that volatility appears to be making a comeback. This week, we will get more information on how the Fed is feeling about inflation with the Fed minutes to be released Wednesday amid some massive consumer earnings cues from multinationals such as Walmart WMT, Home Depot HD, and Macy's M on Tuesday. For investors looking to find the best opportunities, the deep learning algorithms at Q.ai have crunched the data to give you a set of Top Buys. Our Artificial Intelligence ("AI") systems assessed each firm on parameters of Technicals, Growth, Low Volatility Momentum, and Quality Value to find the best Top Buys.

A new AI tool funded in part by the U.S. military has proven adept at a task that has traditionally been very difficult for computer programs: detecting the human art of sarcasm. It could help intelligence officers or agencies better apply artificial intelligence to trend analysis by avoiding social media posts that aren't serious. Certain words in specific combinations can be a predictable indicator of sarcasm in a social media post, even if there isn't much other context, two researchers from the University of Central Florida noted in a March paper in the journal Entropy. Using a variety of datasets of posts from Twitter, Reddit, various dialogues and even headlines from The Onion, Garibay and his colleague Ramya Akula mapped out how some key words relate to other words. "For instance, words such as'just', 'again', 'totally', '!', have darker edges connecting them with every other word in a sentence. These are the words in the sentence that hint at sarcasm and, as expected, these receive higher attention than others," they write.

The EU Commission recently proposed a new set of stringent rules to regulate AI, citing an urgent need. With the global race to regulate AI officially on, the EU published a detailed proposal on how AI should be regulated, explicitly banning some uses and defining those it considers "high-risk," planning to ban the use of AI that threatens people's rights and safety. We can all agree with the sentiment of Margrethe Vestager, the European Commission executive vice president, when she said that when it comes to "artificial intelligence, trust is a must, not a nice to have," but is regulation the most effective and efficient way to secure this reality? The takeaways from the commission are incredibly in-depth, but the ones that make the most sense to me are those that stress regulated AI should aim to increase human well-being. However, regulation should not overly constrain experimentation and development of AI systems.