With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This study addresses this gap by specifically studying the economic implications of using ML in clinical psychiatry. In this paper, we evaluate the economic implications of using ML in clinical psychiatry through using three problem-oriented case studies, literature on economics, socioeconomic and medical AI, and two types of health economic evaluations. In addition, we provide details on fairness, legal, ethics and other considerations for ML in clinical psychiatry.

We present a theoretical framework assessing the economic implications of bias in AI-powered emergency response systems. Integrating health economics, welfare economics, and artificial intelligence, we analyze how algorithmic bias affects resource allocation, health outcomes, and social welfare. By incorporating a bias function into health production and social welfare models, we quantify its impact on demographic groups, showing that bias leads to suboptimal resource distribution, increased costs, and welfare losses. The framework highlights efficiency-equity trade-offs and provides economic interpretations. We propose mitigation strategies, including fairness-constrained optimization, algorithmic adjustments, and policy interventions. Our findings offer insights for policymakers, emergency service providers, and technology developers, emphasizing the need for AI systems that are efficient and equitable. By addressing the economic consequences of biased AI, this study contributes to policies and technologies promoting fairness, efficiency, and social welfare in emergency response services.

In September this year, Tesla presented a glimpse of their general-purpose humanoid robot'Optimus'. Unfortunately, as many humorously remarked, the launch was "suboptimal". The demo couldn't do much to excite the audience for what's coming next. However, many roboticists and experts in the field believe that if sufficient resources are thrown into it, the vision of an "all-purpose" robot is practical. I am aware of critics who say that the prototype had nothing new that they haven't seen elsewhere, and that there are other more impressive humanoids. There are also people who have doubts on the aggressive timeline Elon had proposed, and I do not necessarily disagree with them.

In his final, posthumously published book, famed physicist Stephen Hawking raises an alarm about the dangers of artificial intelligence, or AI, and the existential threat it could pose to humanity. In "Brief Answers to the Big Questions," Hawking writes, "a super-intelligent AI will be extremely good at accomplishing goals, and if those goals aren't aligned with ours, we're in trouble." University of Virginia economist Anton Korinek could not agree more, and he believes that the kind of AI that Hawking refers to – "general artificial intelligence" that can equal or surpass human intelligence – could be just a few decades away. "I believe that, by the second half of this century, AI – robots and programs – will be better than us humans at nearly everything," said Korinek, who holds a joint appointment in UVA's Economics Department and the Darden School of Business. "The fundamental question becomes, 'What will happen to humans if we are no longer the most generally intelligent beings on Earth?'" Korinek has written and co-written several published and forthcoming papers on the economic impact of increasing artificial intelligence, including a paper published by the National Bureau of Economic Research and several works in progress.

"In January 2018, advocates for data privacy celebrated when the Chinese government released a new national standard on the protection of personal information, which contains more comprehensive and onerous requirements than even the European Union's General Data Protection Regulation, per analysis by some experts." In the decades ahead, the countries that dominate AI in any domain could influence how our world is shaped. Jeff Ding leads research on China's development of artificial intelligence at the Future of Humanity Institute's Governance of AI Program at Oxford University. He's been interested in studying China since his high school years. Ding says that once he realized the potential of AI, he became more interested in China's investment in this area. Ding's new study, Deciphering China's AI Dream, is a detailed analysis of the country's AI strategy moving forward.

The brains behind IBM's Jeopardy-winning, disease-tracking, weather-mapping Watson supercomputer plan to embark on a lobbying blitz in Washington, D.C., this week, hoping to show federal lawmakers that artificial intelligence isn't going to kill jobs -- or humans. To hear IBM tell it, much of the recent criticism around machine learning, robotics and other kinds of AI amounts to merely "fear mongering." The company's senior vice president for Watson, David Kenny, aims to convey that message to members of Congress beginning with a letter on Tuesday, stressing the "real disaster would be abandoning or inhibiting cognitive technology before its full potential can be realized." Labor experts and reams of data released in recent months argue otherwise: They foretell vast economic consequences upon the mass-market arrival of AI, as entire industries are displaced -- not just blue-collar jobs like trucking, as self-driving vehicles replace humans at the wheel, but white-collar positions like stock trading too. Others fear the privacy, security and safety implications as more tasks, from managing the country's roads to reading patients' X-ray results, are automated -- and the most dire warnings, from the likes of SpaceX and Tesla founder Elon Musk, include the potential arrival of "robots capable of destroying mankind."

A law professor at the University of Surrey is arguing that it should be possible for computer-based artificial intelligence (AI) systems to be formally considered as inventors for any invention they contribute to, much in the same way a person would. The argument forms part of a paper, which has been published in the Boston College Law Review, entitled I Think, Therefore I Invent: Creative Computers and the Future of Patent Law. In its introduction the report makes the point that while inventions by computers have been granted patents previously, the concept of computer inventorship has never actually been considered by the courts. The concept of giving creative computers the credit for their own inventions may sound surreal but, in reality, they have been generating potentially patentable ideas for decades without acknowledgment. As Professor Ryan Abbott points out in his paper, 'machines have been autonomously generating patentable results for at least twenty years and the pace of such invention is likely increasing.'

As a part of this initiative, the Federal Government is working to leverage AI for public good and to aid in promoting more effective government. OSTP is in the process of co-hosting four public workshops in 2016 on topics in AI in order to spur public dialogue on these topics and to identify challenges and opportunities related to this emerging technology. These topics include the legal and governance issues for AI, AI for public good, safety and control for AI, and the social and economic implications of AI. A new National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence has also been established. This group will monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally, as well as help coordinate Federal activity in this space.

SUMMARY: Artificial intelligence (AI) technologies offer great promise for creating new and innovative products, growing the economy, and advancing national priorities in areas such as education, mental and physical health, addressing climate change, and more. Like any transformative technology, however, AI carries risks and presents complex policy challenges along a number of different fronts. The Office of Science and Technology Policy (OSTP) is interested in developing a view of AI across all sectors for the purpose of recommending directions for research and determining challenges and opportunities in this field. The views of the American people, including stakeholders such as consumers, academic and industry researchers, private companies, and charitable foundations, are important to inform an understanding of current and future needs for AI in diverse fields. The purpose of this RFI is to solicit feedback on overarching questions in AI, including AI research and the tools, technologies, and training that are needed to answer these questions.