Causal inference, a cornerstone in disciplines such as economics, genomics, and medicine, is increasingly being recognized as fundamental to advancing the field of robotics. In particular, the ability to reason about cause and effect from observational data is crucial for robust generalization in robotic systems. However, the construction of a causal graphical model, a mechanism for representing causal relations, presents an immense challenge. Currently, a nuanced grasp of causal inference, coupled with an understanding of causal relationships, must be manually programmed into a causal graphical model. To address this difficulty, we present initial results towards a human-centered augmented reality framework for creating causal graphical models. Concretely, our system bootstraps the causal discovery process by involving humans in selecting variables, establishing relationships, performing interventions, generating counterfactual explanations, and evaluating the resulting causal graph at every step. We highlight the potential of our framework via a physical robot manipulator on a pick-and-place task.

"What we're hearing from our friends and thought leaders in this space that pay close attention to the regulations is just behave as though you're under the European Union's AI Act guidelines, whether you're in Europe, America, or anywhere else," says Roetzer. Regulations like the AI Act will be used as a template by other governments soon. You can't avoid issues around responsible and ethical AI. Regulations will force you to act. Even if you're an AI beginner, you'll quickly run into ethical issues around data, how it's used, and who provides it. You need an AI ethics policy or guidelines.

In 1950, computing pioneer Alan Turing predicted that in a few decades, computers would convincingly mimic human intelligence -- a feat known as passing the Turing Test. Fast-forward to earlier this year, when a Google software engineer announced that his conversations with the company's AI-powered chatbot had convinced him that it had become "sentient." "I know a person when I talk to it," he told the Washington Post. As AI technologies such as natural language processing, machine learning, and deep learning rapidly evolve, so does the idea that they will go from imitating humans to making us obsolete: Elon Musk has warned that a superintelligent machine could "take over the world." The fantasy -- or nightmare -- that people and AI will become locked in competition is remarkably enduring.

National, 24 February 2022: The pandemic has demonstrated that health care organizations can become more resilient, agile and innovative if they shift to digitally enabled business models with data at the core, according to an EY report, "Getting future ready", released at BioAsia 2022 - Asia's largest Life-sciences and Health-Tech convention. The report highlights how healthcare organizations need to create the right data environment to support a more human-centered approach. Hitesh Sharma, National Tax Leader -Life Sciences, EY India said, "Healthcare delivery is moving outside the four walls of the traditional health system. Health care providers who take the lead in engaging with users across the health care value chain will be best placed to integrate the physical and the virtual world. Healthcare delivery in India, predominantly recognised for sick care delivery, will have to innovate in the coming decade to build next-gen capabilities, and unlock the power of data to enhance the overall healthcare experience."

Click to learn more about author Ahmer Inam. How might artificial intelligence (AI) help physicians diagnose medical conditions more effectively? This question grows more important each year. Approximately 400,000 hospitalized patients experience some type of preventable harm each year, costing the health care industry $20 billion, according to NCBI. Mistakes hurt everyone – first and foremost patients, and also the medical staff who grapple with the emotional and mental toll of committing a misdiagnosis.

Editor's Note: In the diverse and multifaceted world of research, individual contributions can add up to significant results over time. In this new series of posts, we're connecting the dots to provide an overview of how researchers at Microsoft and their collaborators are working towards significant customer and societal outcomes that are broader than any single discipline. Here, we've curated a selection of the work Microsoft researchers are doing to advance responsible AI. Responsible AI is really all about the how: how do we design, develop and deploy these systems that are fair, reliable, safe and trustworthy. And to do this, we need to think of Responsible AI as a set of socio-technical problems.

Federal artificial intelligence (AI) technology leaders agreed today that a human-centered approach to data management and automation generates stakeholder buy-in and improves agencywide perceptions of AI projects. At CXO Tech Forum: AI and Robotics Process Automation (RPA) in Government on Dec. 5, government officials speaking on several panels emphasized the importance of human-centered design in the development of AI capabilities. Anil Tilbe, Director of Enterprise Measurement and Design at the Veterans Experience Office (VEO), said that human-centered design is "extremely important" in AI development. "Using human-centered design, you're prioritizing human intelligence," he said. At VEO, Tilbe and his team are using information gathered from veterans and Veterans Affairs employees to build the AI environment.

Silicon Valley may be behind much of the development of AI in the modern world, but it's vital that everyone feel included in the technology, said Fei-Fei Li, Google Cloud chief scientist for AI. "It's time to bring AI together with social science, with humanities, to really study the profound impact of AI to our society, to our legal system, to our organizations, to our society to democracy, to education, to our ethics," Li said. "Again I stress: AI doesn't belong to just a few tech giants in Silicon Valley, and these few companies in Silicon Valley have a responsibility to harness AI for the good of everyone, but they also have the responsibility to work with everybody, recognize we don't know it all, and to include everybody. "This is a historical moment, and we have a tremendous opportunity and responsibility and to really think about how to remedy this problem." Li delivered her remarks today in a discussion with former White House CTO and Shift7 CEO Megan Smith.