AI services are known to have unstable behavior when subjected to changes in data, models or users. Such behaviors, whether triggered by omission or commission, lead to trust issues when AI works with humans. The current approach of assessing AI services in a black box setting, where the consumer does not have access to the AI's source code or training data, is limited. The consumer has to rely on the AI developer's documentation and trust that the system has been built as stated. Further, if the AI consumer reuses the service to build other services which they sell to their customers, the consumer is at the risk of the service providers (both data and model providers). Our approach, in this context, is inspired by the success of nutritional labeling in food industry to promote health and seeks to assess and rate AI services for trust from the perspective of an independent stakeholder. The ratings become a means to communicate the behavior of AI systems so that the consumer is informed about the risks and can make an informed decision. In this paper, we will first describe recent progress in developing rating methods for text-based machine translator AI services that have been found promising with user studies. Then, we will outline challenges and vision for a principled, multi-modal, causality-based rating methodologies and its implication for decision-support in real-world scenarios like health and food recommendation.

The low-dimensional manifold hypothesis posits that the data found in many applications, such as those involving natural images, lie (approximately) on low-dimensional manifolds embedded in a high-dimensional Euclidean space. In this setting, a typical neural network defines a function that takes a finite number of vectors in the embedding space as input. However, one often needs to consider evaluating the optimized network at points outside the training distribution. This paper considers the case in which the training data is distributed in a linear subspace of $\mathbb R^d$. We derive estimates on the variation of the learning function, defined by a neural network, in the direction transversal to the subspace. We study the potential regularization effects associated with the network's depth and noise in the codimension of the data manifold. We also present additional side effects in training due to the presence of noise.

Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km square and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16th March 2019 to 6th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models' explainability, we discover that mobile app usage patterns have the potential to reveal socio-economic disparities in IRIS. Insights of this study provide several avenues for future interventions, including user temporal network analysis to understand evolving network patterns and exploration of alternative data sources.

A New cross-domain strategy based XAI models for fake news detection v0.1.1 ABSTRACT The Advancement in technology and rapid usage of social media has made communication easier and faster than ever before. Fake news threatens the community, democracy, egalitarianism and people's trust. Cross-domain text classification is a task of a model adopting a target domain by using the knowledge of the source domain. Natural Language Processing and Deep Learning models are used to identify misleading information. Explainability is crucial in understanding the behaviour of these complex models. In this study, we propose a four-level cross-domain strategy to study the impact of explainability on cross-domain models. The latest findings in the natural language process, the "Bidirectional Encoder Representations from Transformers" (BERT) model published by Devlin et al. (2018) google used to implement the concept of transfer learning. A fine-tune BERT model is used to perform cross-domain classification. Using this model, we conducted four experiments using datasets from different domains. Explanatory models like Anchor, ELI5, LIME and SHAP are used to design a novel explainable approach to cross-domain levels. The experimental analysis has given an ideal pair of XAI models on different levels of cross-domain. INTRODUCTION Nowadays, social media has become a potential influencing tool. According to the statistics published by Datareportal in July 2022, there is exponential growth in social media platforms, declaring that more than half of the world's population (59 per cent) is using them. Consequently, these platforms have deterministic effects on people's lives and the integrity of societies and local communities. Groups of people forming social media clusters use, unfortunately, these tools to spread speculation - so-called "fake news". In 2008, a journalist posted a report about Steve jobs medical condition. It has created massive confusion and controversy within societies and led to fluctuations in the stock price of Apple Inc. Rubin (2017). During the Covid-19 pandemic, fake news was largely spread among people and has created panic within societies. Recent statistics published by the United States support receiving reports from 80 per cent of consumers about the fake news outbreak. Insufficient data is one of the reasons behind unreliable communication, making it difficult to distinguish fake from real news. In 2016, fake news was popular mainly during the United States elections. They have created a great source of influence on people's opinions about two constants.

We propose a learning framework for calibrating predictive models to make loss-controlling prediction for exchangeable data, which extends our recently proposed conformal loss-controlling prediction for more general cases. By comparison, the predictors built by the proposed loss-controlling approach are not limited to set predictors, and the loss function can be any measurable function without the monotone assumption. To control the loss values in an efficient way, we introduce transformations preserving exchangeability to prove finite-sample controlling guarantee when the test label is obtained, and then develop an approximation approach to construct predictors. The transformations can be built on any predefined function, which include using optimization algorithms for parameter searching. This approach is a natural extension of conformal loss-controlling prediction, since it can be reduced to the latter when the set predictors have the nesting property and the loss functions are monotone. Our proposed method is applied to selective regression and high-impact weather forecasting problems, which demonstrates its effectiveness for general loss-controlling prediction.

Self-flying airplanes are gearing up for take-off, as Boeing, Airbus and other companies are testing autonomous systems and craft - but pilots are pushing back over safety risks. The technologies enable autonomous landings, handle-inflight emergencies and relax the Federal Aviation Administration's law requiring two pilots in the cockpit. Pilots have shared their concerns on Twitter, with many stating that two pilots are required in an emergency. Tony Driza, who has been an airline pilot for 40 years, posted that he can'equivocally state that when an emergency situation arises in the cockpit, a full crew is necessary to deal with it.' While autonomous airplanes are still early, Boeing's CEO Dave Calhoun said in a Bloomberg TV interview the technology will'come to all airplanes eventually.' Boeing has developed an autonomous refueling plane for the US Navy, the MQ-25.

Offering real-time identification and automated tendon measurements of the patellar tendon, plantar fascia and Achilles tendon, a new artificial intelligence (AI)-powered musculoskeletal ultrasound imaging application has received 510(k) clearance from the Food and Drug Administration (FDA). Clarius Mobile Health said the AI model identifies viewed tendons with a transparent color overlay, labels the tendon and provides subsequent measurement calipers that align with the bottom and top of the tendon at its thickest region. Users of the AI musculoskeletal ultrasound application can then adjust the measurements to facilitate clinical decision-making, according to the company. Alan Hirahara, M.D., says the new AI musculoskeletal application is "ground-breaking technology" that will assist new ultrasound users in learning musculoskeletal structures and enhance efficiency for radiologist assessment of musculoskeletal structures. "The technology will … help current users standardize how structures are measured. In research, interobserver variability exists for any measurement of structures. With the AI standardization of measurements, interobserver reliability problems will now be non-existent. I am excited to see where this technology will go …," noted Dr. Hirahara, an orthopedic surgeon in private practice in Sacramento, Calif.

A new $2.2 billion US arms package for Ukraine includes a new rocket-propelled precision bomb that could nearly double Kyiv's strike range against the Russians, the Pentagon said Friday. Pentagon spokesman Pat Ryder said the new package includes the ground-launched small-diameter bombs (GLSDB), a munition that can fly up to 150 kilometers (93 miles), which would threaten Russian positions and depots far behind the front lines. "This gives them a longer-range capability... that will enable them to conduct operations in defense of their country and to take back their sovereign territory," Ryder said. Ukraine had been asking the United States for munitions that can fly farther than the HIMARS rockets with an 80-kilometer (50-mile) range. The GLSDB potentially gives Ukraine forces an ability to strike anywhere in the Russian-occupied Donbas, Zaporizhzhia and Kherson regions, and the northern part of occupied Crimea.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Italy's Data Protection Agency said on Friday it was prohibiting artificial intelligence (AI) chatbot company Replika from using the personal data of Italian users, citing risks to minors and emotionally fragile people. Replika, a San Francisco startup launched in 2017, offers users customized avatars that talk and listen to them. It has led the way among English speakers, and is free to use, though it brings in around $2 million in monthly revenue from selling bonus features such as voice chats.

Former federal prosecutor Trey Gowdy gives his take on the Alex Murdaugh trial and Hunter Biden's attorney calling for criminal probe of the laptop on'The Story.' High-profile lawyer Abbe Lowell again entered the national spotlight this week representing Hunter Biden in the legal battle involving his infamous laptop, and Lowell's hiring signals how seriously Biden is taking his situation, an attorney tells Fox News Digital. "Abbe is not cheap, and you don't bring in Abbe unless you want to go to war or prevent one," said the source who's worked with Lowell. He hasn't been charged with anything, but they're trying to prevent that because that would be bad for [President] Biden and Hunter." Lowell made a splash this week with letters urging prosecutors to launch state and federal investigations into John Paul Mac Isaac, who he accused of "unlawfully" accessing the younger Biden's personal data on his laptop after it was left at his repair shop in 2019. Former President Donald Trump's lawyer Rudy Giuliani, Steve Bannon and other notable Biden critics were also listed in the lawsuit for their role in disseminating the information to the public. Mac Isaac chose to work with President Donald Trump's personal lawyer to weaponize Mr. Biden's personal computer data against his father, Joseph R. Biden, by unlawfully causing the provision of Mr. Biden's personal data to the New York Post," Lowell wrote Wednesday.