The use of automated decision support and decision-making systems (ADM) (Hardt, Price, and Srebro 2016) in applications with direct impact on people's lives has increasingly become a fact of life, e,g. in criminal justice (Kleinberg, Contributions. We propose a technique that uses Bias Mullainathan, and Raghavan 2016; Jain et al. 2020b; Dressel Parity Score (BPS) measures to characterize fairness and develop and Farid 2018), medical diagnosis (Kleinberg, Mullainathan, a family of corresponding loss functions that are used and Raghavan 2016; Ahsen, Ayvaci, and Raghunathan as regularizers during training of Neural Networks to enhance 2019), insurance (Baudry and Robert 2019), credit fairness of the trained models. The goal here is to permit card fraud detection (Dal Pozzolo et al. 2014), electronic the system to actively pursue fair solutions during training health record data (Gianfrancesco et al. 2018), credit scoring while maintaining as high a performance on the task as (Huang, Chen, and Wang 2007) and many more diverse possible. We apply the approach in the context of several domains. This, in turn, has lead to an urgent need fairness measures and investigate multiple loss function formulations for study and scrutiny of the bias-magnifying effects of machine and regularization weights in order to study the learning and Artificial Intelligence algorithms and thus performance as well as potential drawbacks and deployment their potential to introduce and emphasize social inequalities considerations. In these experiments we show that, if used and systematic discrimination in our society. Appropriately, with appropriate settings, the technique measurably reduces much research is being done currently to mitigate bias race-based bias in recidivism prediction, and demonstrate in AI-based decision support systems (Ahsen, Ayvaci, and on the gender-based Adult Income dataset that the proposed Raghunathan 2019; Kleinberg, Mullainathan, and Raghavan method can outperform state-of-the art techniques aimed at 2016; Noriega-Campero et al. 2019; Feldman 2015; more targeted aspects of bias and fairness.

We present a model of sense-making that greatly facilitates the collaboration between an intelligent analyst and a knowledge-based agent. It is a general model grounded in the science of evidence and the scientific method of hypothesis generation and testing, where sense-making hypotheses that explain an observation are generated, relevant evidence is then discovered, and the hypotheses are tested based on the discovered evidence. We illustrate how the model enables an analyst to directly instruct the agent to understand situations involving the possible production of weapons (e.g., chemical warfare agents) and how the agent becomes increasingly more competent in understanding other situations from that domain (e.g., possible production of centrifuge-enriched uranium or of stealth fighter aircraft).

This paper introduces SocialVec, a general framework for eliciting social world knowledge from social networks, and applies this framework to Twitter. SocialVec learns low-dimensional embeddings of popular accounts, which represent entities of general interest, based on their co-occurrences patterns within the accounts followed by individual users, thus modeling entity similarity in socio-demographic terms. Similar to word embeddings, which facilitate tasks that involve text processing, we expect social entity embeddings to benefit tasks of social flavor. We have learned social embeddings for roughly 200,000 popular accounts from a sample of the Twitter network that includes more than 1.3 million users and the accounts that they follow, and evaluate the resulting embeddings on two different tasks. The first task involves the automatic inference of personal traits of users from their social media profiles. In another study, we exploit SocialVec embeddings for gauging the political bias of news sources in Twitter. In both cases, we prove SocialVec embeddings to be advantageous compared with existing entity embedding schemes. We will make the SocialVec entity embeddings publicly available to support further exploration of social world knowledge as reflected in Twitter.

Smoke from a 2019 Northern California wildfire could be seen by astronauts aboard the International Space Station. Research by scientists from UCLA and Lawrence Livermore National Laboratory strengthens the case that climate change has been the main cause of the growing amount of land in the western U.S. that has been destroyed by large wildfires over the past two decades. Rong Fu, a UCLA professor of atmospheric and oceanic sciences and the study's corresponding author, said the trend is likely to worsen in the years ahead. "I am afraid that the record fire seasons in recent years are only the beginning of what will come, due to climate change, and our society is not prepared for the rapid increase of weather contributing to wildfires in the American West." The dramatic increase in destruction caused by wildfires is borne out by U.S. Geological Survey data.

Artificial intelligence (AI) machine learning is rapidly becoming a valuable tool in health care, oncology, and precision medicine. A new study published in Cancer Research, a journal of the American Association of Cancer Research, shows how an AI algorithm can rank the accuracy of drug therapies to treat bile duct cancer. "The application of computational approaches to make treatment recommendations based on the biological data of a given cancer holds great promise," wrote the researchers affiliated with the Queen Mary University of London, The Alan Turing Institute, Kinomica Ltd, Kings College Hospital, Foundation for Liver Research, and the King's College London conducted the study. Bile duct cancer, cholangiocarcinoma (CCA), is a rare disease where malignant cells form in the network of tubes called bile ducts located either inside (intrahepatic) or outside (extrahepatic) the liver according to the National Cancer Institute at the National Institutes of Health. Bile is a thick greenish-yellow fluid consisting of bile salts, cholesterol, bilirubin, and waste products secreted by the liver to carry away waste and break down fats during digestion.

After Canada, now Australia has found that controversial facial recognition company, Clearview AI, broke national privacy laws when it covertly collected citizens' facial biometrics and incorporated them into its AI-powered identity matching service -- which it sells to law enforcement agencies and others. In a statement today, Australia's information commissioner and privacy commissioner, Angelene Falk, said Clearview AI's facial recognition tool breached the country's Privacy Act 1988 by: In what looks like a major win for privacy down under, the regulator has ordered Clearview to stop collecting facial biometrics and biometric templates from Australians; and to destroy all existing images and templates that it holds. The Office of the Australian Information Commissioner (OAIC) undertook a joint investigation into Clearview with the UK data protection agency, the Information Commission's Office (IOC). However the UK regulator has yet to announce any conclusions. In a separate statement today -- which possibly reads slightly flustered -- the ICO said it is "considering its next steps and any formal regulatory action that may be appropriate under the UK data protection laws".

Even as numerous leaders in the tech world have repeatedly warned of the dangers that artificial intelligence- (AI) enabled weapons could present for the future of mankind, Russian Federation president Vladimir Putin has long been a firm supporter of AI weapons. In 2017, Putin proclaimed that the country that leads AI development could have a significant advantage over its rivals. Putin also admitted that the development of AI could present challenges for humanity. "Artificial intelligence is the future, not only for Russia, but for all humankind," Putin remarked. "It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world."

Meta is shutting down Facebook's controversial face recognition feature and deleting the face data collected from users through the social media network, citing "growing societal concerns". But privacy campaigners are concerned that the company hasn't been clear on whether the algorithms trained on that data will be deleted. Images uploaded to Facebook have been scanned by artificial intelligence (AI) tools since 2010, giving the uploader the option of "tagging" people in the image. Meta, then known as Facebook itself, attracted criticism when the feature first launched for failing to ask permission from users, and has since struggled to align it with local privacy laws. In 2012, the company switched off face recognition for people in the EU after a German data protection commissioner said that it violated European Union law – it returned in 2018 with an explicit opt-in requirement.

The destruction of mankind by swarms of intelligent robots is a film trope long held dear by Hollywood producers and directors. Whether it's the Terminator, or Ultron or the computer in WarGames, ever since the introduction of the first computer, we've been worrying about ways in which such technology could destroy us. Thankfully, visions of an invading army of kill-bot is well wide of the mark, at least within the bounds of foreseeable technology, but the fact is artificial intelligence (AI) is already being used for nefarious purposes, and the world's biggest nations are falling behind in their attempts to keep ahead of the threat. That is certainly the conclusion made by the National Security Commission on Artificial Intelligence, which reported recently to both the US Congress and President Joe Biden that: "America is not prepared to defend or compete in the AI era. This is the tough reality we must face. And it is this reality that demands comprehensive, whole-of-nation action."

It's been a long couple of years in politics. And it can be frustrating to feel like politicians treat it like a game, with people as pawns. But a political journalist thinks there may be some appeal in a new strategy game that gives players a front-row seat to the ins and outs of politics in a fictional political world. The game tackles the good, the bad -- and yes, the very, very ugly. It's called Political Arena and its creator is Eliot Nelson, a journalist who wrote a political newsletter for HuffPost until 2018.