Mitigating these biases relies on exposing and removingnuisance components.

Insteadof (2), consider (Q 1 ,..., Q q ) = argmin Rubin, D. Statistics, pages Rubin, D. comment.

Generalized random forests arXiv:1610.01271 build upon the well-established success of conventional forests (Breiman, 2001) to offer a flexible and powerful non-parametric method for estimating local solutions of heterogeneous estimating equations. Estimators are constructed by leveraging random forests as an adaptive kernel weighting algorithm and implemented through a gradient-based tree-growing procedure. By expressing this gradient-based approximation as being induced from a single Newton-Raphson root-finding iteration, and drawing upon the connection between estimating equations and fixed-point problems arXiv:2110.11074, we propose a new tree-growing rule for generalized random forests induced from a fixed-point iteration type of approximation, enabling gradient-free optimization, and yielding substantial time savings for tasks involving even modest dimensionality of the target quantity (e.g. multiple/multi-level treatment effects). We develop an asymptotic theory for estimators obtained from forests whose trees are grown through the fixed-point splitting rule, and provide numerical simulations demonstrating that the estimators obtained from such forests are comparable to those obtained from the more costly gradient-based rule.

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.

As artificial intelligence and machine learning deploy across financial sectors, federal government needs a way to ensure standards for stability and inclusion are followed. Measuring risks and setting benchmarks for emerging fintech is top of mind for agencies such as the National Institute of Standards and Technology and the Commerce Department. In her first public engagement since being sworn in earlier this month, NIST Director Laurie Locascio told an audience at Stanford University on Wednesday that the president's 2023 budget request calls for an additional $80 million to expand and strengthen NIST capabilities for targeting critical and emerging technologies. Listing ways the agency is trying to enable trustworthy AI, she said NIST scientists and engineers are developing taxonomies, terminology and testbeds for measuring AI risks. "NIST is developing a resource center of documents, software and standards and related tools that continue to better understanding and better identification of measurement, and management of various risks associated with AI systems," she said during the Artificial Intelligence and the Economy Conference.

AI has the potential to improve human lives and a company's bottom line, but it can also accelerate inequality and eliminate jobs during the worst U.S. recession since the Great Depression. This dual promise and peril led members of the House Budget Committee to hold a hearing today to discuss the impact of AI on economic recovery, the future of work, and the federal budget. Expert witnesses recommended approaches that ranged from giving people lifelong upskilling accounts to creating regional investment districts and portable benefits. Daron Acemoglu warned the committee about the dangers of excessive automation. The MIT professor and economist recently found that every robot replaces 3.3 human jobs in the U.S. In a working paper published by the National Bureau of Economic Research, Acemoglu detailed how excessive automation looks for ways to replace workers with machines or algorithms but produces few new jobs.

These days, artificial intelligence systems make our steering wheels vibrate when we drive unsafely, suggest how to invest our money, and recommend workplace hiring decisions. In these situations, the AI has been intentionally designed to alter our behavior in beneficial ways: We slow the car, take the investment advice, and hire people we might not have otherwise considered. Each of these AI systems also keeps humans in the decision-making loop. That's because, while AIs are much better than humans at some tasks (e.g., seeing 360 degrees around a self-driving car), they are often less adept at handling unusual circumstances (e.g., erratic drivers). In addition, giving too much authority to AI systems can unintentionally reduce human motivation.

This year's AI Ethics, Policy, and Governance event brought together more than 900 people from academia, industry, and government to discuss the future of AI (or automated computer systems able to perform tasks that normally require human intelligence). Discussions at the conference highlighted how companies, governments, and people around the world are grappling with AI's ethical, policy, and governance implications. In this panel, Expanding Human Experience, Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business and faculty associate director at Stanford HAI, spoke about AI's impact on the economy. It's critical, she said, that AI creates shared prosperity and expands -- rather than replaces -- the human experience in life and at work. Humans, after all, understand things in a way that may be difficult to codify in AI.

An underlying theme emerged from the Stanford Institute for Human-Centered Artificial Intelligence's fall conference: AI must be truly beneficial for humanity and not undermine people in a cold calculus of efficiency. Titled AI Ethics, Policy, and Governance, the event brought together more than 900 people from academia, industry, civil society, and government to discuss the future of AI (or automated computer systems able to perform tasks that normally require human intelligence). Discussions at the conference highlighted how companies, governments, and people around the world are grappling with AI's ethical, policy, and governance implications. Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business and faculty associate director at Stanford HAI, spoke about AI's impact on the economy. It's critical, she said, that AI creates shared prosperity and expands -- rather than replaces -- the human experience in life and at work.

The application of machine learning in economics is the cool thing. Machine learning uses algorithms and statistical models that perform tasks based on patterns and inferences. It is not really a new idea, but its application in economics is going to increase significantly as seen by the recent working paper by a team of RBI economists, which looks at machine learning for economic forecasting. To get an idea on machine learning in economics, one should go back to the Jean Monnet lecture by Susan Athey, Professor, Economics of Technology, Stanford University. Her lecture'Machine learning in economics' was delivered at the 4th ECB Annual Research Conference on September 5-6.