The United States needs a better strategy and more advanced tools for information operations, Lt. Gen. Dennis Crall, the Joint Staff's chief information officer, said Thursday. The government has become slower and less confident in its approach, a reticence it can't afford as artificial intelligence drastically increases the pace of messaging and information campaigns, said Crall, who is also the Joit Staff's director for command, control, communications, computers, and cyber. . "The speed at which machines and AI won some of these information campaigns changes the game drastically for us. If we study, if we're hesitant, if we don't have good left and right lateral limits, if every operation requires a new set of permissions...We're never going to compete." Crall made his remarks at the NDIA conference for Special Operations and Low Intensity Conflict, or SOLIC.

The European Union’s plan to regulate artificial intelligence is ill-equipped to protect people from flawed algorithms that deprive them of lifesaving benefits and discriminate against vulnerable populations, Human Rights Watch said in report on the regulation released today. The European Parliament should amend the regulation to better protect people’s rights to social security and an adequate standard of living.

Israel joined the Global Partnership on Artificial Intelligence today, Nov. 11, becoming the 20th member of the organization created two years ago under French and Canadian leadership. Four other countries were also accepted into the organization and four countries saw their candidacy rejected, at least for the moment, at the GPAI's annual event. The GPAI headquarters are located within the OECD in Paris, with another hub in Canada. He explained to Al-Monitor that the organization is made up of countries with advanced artificial intelligence technologies that believe in the values of equality and democracy promoted by the OECD. "Artificial intelligence has been a much-debated topic worldwide, also generating fears. These technologies bring about very positive impacts and possibilities, but are also quite complex and could be sensitive to society. And so the states wanted to have a multi-stakeholder initiative that could advise them and make recommendations," he said.

Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights, however, often remain opaque. In this paper, we show how Explainable AI (XAI) can help domain experts and data subjects validate, question, and improve models that classify psychological traits from digital footprints. We elaborate on two popular XAI methods (rule extraction and counterfactual explanations) in the context of Big Five personality predictions (traits and facets) from financial transactions data (N = 6,408). First, we demonstrate how global rule extraction sheds light on the spending patterns identified by the model as most predictive for personality, and discuss how these rules can be used to explain, validate, and improve the model. Second, we implement local rule extraction to show that individuals are assigned to personality classes because of their unique financial behavior, and that there exists a positive link between the model's prediction confidence and the number of features that contributed to the prediction. Our experiments highlight the importance of both global and local XAI methods. By better understanding how predictive models work in general as well as how they derive an outcome for a particular person, XAI promotes accountability in a world in which AI impacts the lives of billions of people around the world.

Medical AI algorithms can often experience degraded performance when evaluated on previously unseen sites. Addressing cross-site performance disparities is key to ensuring that AI is equitable and effective when deployed on diverse patient populations. Multi-site evaluations are key to diagnosing such disparities as they can test algorithms across a broader range of potential biases such as patient demographics, equipment types, and technical parameters. However, such tests do not explain why the model performs worse. Our framework provides a method for quantifying the marginal and cumulative effect of each type of bias on the overall performance difference when a model is evaluated on external data. We demonstrate its usefulness in a case study of a deep learning model trained to detect the presence of pneumothorax, where our framework can help explain up to 60% of the discrepancy in performance across different sites with known biases like disease comorbidities and imaging parameters.

Contributed chapter to "HANDBOOK OF RESEARCH ON ARTIFICIAL INTELLIGENCE, INNOVATION AND ENTREPRENEURSHIP" to be published by Edward Elgar Publishing Loizos Michael Abstract: This chapter discusses AI from the prism of an automated process for the organization of data, and exemplifies the role that explainability has to play in moving from the current generation of AI systems to the next one, where the role of humans is lifted from that of data annotators working for the AI systems to that of collaborators working with the AI systems. Keywords: data organization, automation, explainability, fourth AI revolution, learning, XIXO principle, machine coaching Acknowledgements: This work was supported by funding from the EU's Horizon 2020 Research and Innovation Programme under grant agreements no. Explainable automated organization of data. Although admittedly not a comprehensive definition of the wide scope of Artificial Intelligence (AI), this phrase does capture how AI has come to be ...

LOS ALAMOS, N.M., Nov. 10, 2021--Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret massive amounts of ground deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection. "Applying machine learning to InSAR data gives us a new way to understand the physics behind tectonic faults and earthquakes," said Bertrand Rouet-Leduc, a geophysicist in Los Alamos' Geophysics group. New satellites, such as the Sentinel 1 Satellite Constellation and the upcoming NISAR Satellite, are opening a new window into tectonic processes by allowing researchers to observe length and time scales that were not possible in the past. However, existing algorithms are not suited for the vast amount of InSAR data flowing in from these new satellites, and even more data will be available in the near future. In order to process all of this data, the team at Los Alamos developed the first tool based on machine learning algorithms to extract ground deformation from InSAR data, which enables the detection of ground deformation automatically--without human intervention--at a global scale.

The second of two leaders from NYU's AI Now Institute, a small but influential organization researching the social implications of artificial intelligence, just joined the Biden administration to lay the groundwork for government AI policy. Their previous work suggests their presence might encourage the government to require new transparency from tech companies about how their algorithms work. The Federal Trade Commission earlier this month created an entirely new role for AI Now co-founder Meredith Whittaker, who will serve as senior adviser on AI for an agency where tech staff has been in flux despite a mission to get tougher on tech. AI Now alumna Rashida Richardson -- a law professor who served as director of policy research for the group and has a background studying the impact of AI systems like predictive policing tools -- joined the White House Office of Science and Technology Policy in July as senior policy adviser for data and democracy. "[Whittaker's] hiring is just the latest evidence of the FTC's attention on algorithms and algorithmic issues," said Laura Riposo VanDruff, former assistant director in the FTC's privacy and identity protection division and a partner at law firm Kelley Drye & Warren.

The Wisconsin Department of Safety and Professional Services (DSPS) announced last month a partnership with Google Cloud and MTX to modernize its occupational licensing process. The shift to digitize licensing with the intent of increasing efficiency is gaining ground in government agencies for a variety of processes, from marijuana licensing to permitting for gun ownership. Many agencies have found legacy licensing systems are outdated or overly complex. For DSPS, the launch of the MavQ AI platform is part of a larger, multi-phase effort to modernize the department's infrastructure, according to DSPS Secretary Dawn Crim. Crim said occupational licensing was originally slated to be the third phase of the project, but it was moved forward in the plan to prioritize the needs of the workforce and to provide family-sustaining wages.

Last year, Anthony Murphy, a visually impaired man who lives in Erie, Pennsylvania, visited the website of eyewear retailer Eyebobs using screen reader software. Its synthesized voice attempted to read out the page's content, as well as navigation buttons and menus. Eyebobs used artificial intelligence software from Israeli startup AccessiBe that promised to make its site easier for people with disabilities to use. But Murphy found it made it harder. AccessiBe says it can simplify the work of making websites accessible to people with impaired vision or other challenges by "replacing a costly, manual process with an automated, state-of-the-art AI technology."