Three Carnegie Mellon University research teams have received funding through the Program on Fairness in Artificial Intelligence, which the National Science Foundation sponsors in partnership with Amazon. The program supports computational research focused on fairness in AI, with the goal of building trustworthy AI systems that can be deployed to tackle grand challenges facing society. "There have been increasing concerns over biases in AI systems, for example computer vision algorithms working worse for Blacks than for other races, or ads for higher paying jobs only being shown to men," said Jason Hong, a professor in the Human-Computer Interaction Institute (HCII). "Machine learning researchers are developing new tools and techniques to improve fairness from a quantitative perspective, but there are still many blind spots that defy pure quantification." The CMU projects address new methods for detecting bias, translating fairness goals into public policy and increasing the diversity of people able to use systems that recognize human speech.

Criminals are getting smarter and the healthcare industry is no exception. In 2021 alone, the Department of Justice (DOJ) recovered more than $5.6 billion from civil fraud and false claims cases. This is the DOJ's biggest haul since 2014, but a drop in the bucket compared to the estimated $380 billion is lost every year to fraud, waste, and abuse. These numbers add up to higher premiums and out-of-pocket expenses for consumers, as well as reduced benefits or coverage. What's more, relaxed telehealth mandates put into place during the COVID-19 pandemic, the increased digitization of health, and the emergence of telehealth platforms have made it easier than ever for fraudsters to operate are all contributing to a growing problem.

This paper reviews exploration techniques in deep reinforcement learning. Exploration techniques are of primary importance when solving sparse reward problems. In sparse reward problems, the reward is rare, which means that the agent will not find the reward often by acting randomly. In such a scenario, it is challenging for reinforcement learning to learn rewards and actions association. Thus more sophisticated exploration methods need to be devised. This review provides a comprehensive overview of existing exploration approaches, which are categorized based on the key contributions as follows reward novel states, reward diverse behaviours, goal-based methods, probabilistic methods, imitation-based methods, safe exploration and random-based methods. Then, the unsolved challenges are discussed to provide valuable future research directions. Finally, the approaches of different categories are compared in terms of complexity, computational effort and overall performance.

Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate scientific discoveries and societal applications. This Review gives a snapshot of nuclear physics research which has been transformed by machine learning techniques.

CLARA Analytics ("CLARA"), the leading provider of artificial intelligence (AI) technology in the commercial insurance industry, announced that it has made two new strategic hires, adding Ram Rangaraj as the company's new Chief Technology Officer and enlisting Mubbin Rabbani as the new Vice President of Product. Ram Rangaraj is a veteran IT leader with over two decades of experience at Kaiser Permanente, where he served in a range of technology leadership roles, most recently as Senior Director of Revenue Management Integration Engineering. During his tenure at Kaiser, he led numerous strategic IT initiatives, including the innovative application of data analytics to improve the company's claims management performance. Rangaraj will lead the evolution and operations of CLARA's AI platform, serving as a core member of the executive leadership team and reporting directly to CLARA CEO Heather H. Wilson. "Ram Rangaraj is an elite performer," said Wilson.

In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve.

LiveRamp is the leading data connectivity platform. We are committed to connecting the world's data safely and effectively, advancing innovation, and empowering people to do good. Our platform powers customer experiences centered around the needs and concerns of real people, keeping the Internet open for all. We enable individuals around the world to connect with the brands and products they love. LiveRampers thrive on solving challenging problems for the good of humanity--and we're always looking for smart, kind, and creative people to help us get there.

The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use of neural networks in high stakes decision-making such as in climate change applications. We address both issues by successfully implementing a Bayesian Neural Network (BNN), where parameters are distributions rather than deterministic, and applying novel implementations of explainable AI (XAI) techniques. The uncertainty analysis from the BNN provides a comprehensive overview of the prediction more suited to practitioners' needs than predictions from a classical neural network. Using a BNN means we can calculate the entropy (i.e. uncertainty) of the predictions and determine if the probability of an outcome is statistically significant. To enhance trustworthiness, we also spatially apply the two XAI techniques of Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP) values. These XAI methods reveal the extent to which the BNN is suitable and/or trustworthy. Using two techniques gives a more holistic view of BNN skill and its uncertainty, as LRP considers neural network parameters, whereas SHAP considers changes to outputs. We verify these techniques using comparison with intuition from physical theory. The differences in explanation identify potential areas where new physical theory guided studies are needed.

Background: The dementia epidemic is progressing fast. As the world's older population keeps skyrocketing, the traditional incompetent, time-consuming, and laborious interventions are becoming increasingly insufficient to address dementia patients' health care needs. This is particularly true amid COVID-19. Instead, efficient, cost-effective, and technology-based strategies, such as sixth-generation communication solutions (6G) and artificial intelligence (AI)-empowered health solutions, might be the key to successfully managing the dementia epidemic until a cure becomes available. However, while 6G and AI technologies hold great promise, no research has examined how 6G and AI applications can effectively and efficiently address dementia patients' health care needs and improve their quality of life.

Resonance, a powerful and pervasive phenomenon, appears to play a major role in human interactions. This article investigates the relationship between the physical mechanism of resonance and the human experience of resonance, and considers possibilities for enhancing the experience of resonance within human–robot interactions. We first introduce resonance as a widespread cultural and scientific metaphor. Then, we review the nature of “sympathetic resonance” as a physical mechanism. Following this introduction, the remainder of the article is organized in two parts. In part one, we review the role of resonance (including synchronization and rhythmic entrainment) in human cognition and social interactions. Then, in part two, we review resonance-related phenomena in robotics and artificial intelligence (AI). These two reviews serve as ground for the introduction of a design strategy and combinatorial design space for shaping resonant interactions with robots and AI. We conclude by posing hypotheses and research questions for future empirical studies and discuss a range of ethical and aesthetic issues associated with resonance in human–robot interactions.