The rapid evolution of artificial intelligence (AI) and machine learning (ML) presents both opportunities and challenges to society. As we witness breakthroughs and milestone successes, the integration of AI into our daily routines has changed to way we work, shop, and communicate — from smartphones and car navigation systems to facial recognition and electronic health records.
Annual Reviews has curated a new review article collection to explore the impact AI and ML have already had on modern life, and how society might confront related challenges, opportunities, and consequences in the future. In this interdisciplinary collection, our experts discuss four key areas significantly influenced by AI, including:
- Social Implications of Artificial Intelligence
- Law Enforcement and Human Rights
- Medical Applications of Big Data
- Autonomous Systems and Robotics
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Last updated 05/2023
Social Implications of Artificial Intelligence
On the Timescales of Embodied Intelligence for Autonomous Adaptive Systems
Fumiya Iida and Fabio Giardina, Annual Review of Control, Robotics, and Autonomous Systems
Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing
Qingyu Chen, Robert Leaman, Alexis Allot, Ling Luo, Chih-Hsuan Wei, Shankai Yan, and Zhiyong Lu, Annual Review of Biomedical Data Science
Machine Learning for Social Science: An Agnostic Approach
Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart, Annual Review of Political Science
Syntactic Structure from Deep Learning
Tal Linzen and Marco Baroni, Annual Review of Linguistics
The Society of Algorithms
Jenna Burrell and Marion Fourcade, Annual Review of Sociology
Big Data in Industrial-Organizational Psychology and Human Resource Management: Forward Progress for Organizational Research and Practice
Frederick L. Oswald, Tara S. Behrend, Dan J. Putka, and Evan Sinar, Annual Review of Organizational Psychology and Organizational Behavior
The Challenge of Big Data and Data Science
Henry E. Brady, Annual Review of Political Science
Machine Learning Methods That Economists Should Know About
Susan Athey and Guido W. Imbens, Annual Review of Economics
Law Enforcement and Human Rights:
Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
Richard A. Berk, Annual Review of Criminology
Tool for Surveillance or Spotlight on Inequality? Big Data and the Law
Rebecca A. Johnson and Tanina Rostain, Annual Review of Law and Social Science
Human Rights and Technology: New Challenges for Justice and Accountability
Molly K. Land and Jay D. Aronson, Annual Review of Law and Social Science
Do Emerging Military Technologies Matter for International Politics?
Michael C. Horowitz, Annual Review of Political Science
Medical Applications of Big Data:
Ethical Machine Learning in Healthcare
Irene Y. Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi, Annual Review of Biomedical Data Science
Modern Clinical Text Mining: A Guide and Review
Bethany Percha, Annual Review of Biomedial Data Science
AI in Measurement Science
Chao Liu and Jiashu Sun, Annual Review of Analytical Chemistry
Artificial Intelligence in Drug Treatment
Eden L. Romm and Igor F. Tsigelny, Annual Review of Pharmacology and Toxicology
Big Data and Artificial Intelligence Modeling for Drug Discovery
Hao Zhu, Annual Review of Pharmacology and Toxicology
Large-Scale Analysis of Genetic and Clinical Patient Data
Marylyn D. Ritchie, Annual Review of Biomedical Data Science
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
Nikolaus Kriegeskorte, Annual Review of Vision Science
Autonomous Systems and Robotics
Into the Robotic Depths: Analysis and Insights from the DARPA Subterranean Challenge
Timothy H. Chung, Viktor Orekhov, and Angela Maio, Annual Review of Control, Robotics, and Autonomous Systems
Increasingly Intelligent Micromachines
Tian-Yun Huang, Nongri Gu, and Bradley J. Nelson, Annual Review of Control, Robotics, and Autonomous Systems
Autonomy in Surgical Robotics
Aleks Attanasio, Bruno Scaglioni, Elena De Momi, Paolo Fiorini, and Pietro Valdastri, Annual Review of Control, Robotics, and Autonomous Systems
Autonomous Vehicles and Public Health
David Rojas-Rueda, Mark J. Nieuwenhuijsen, Haneen Khreis, and Howard Frumkin, Annual Review of Public Health
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Lukas Hewing, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger, Annual Review of Control, Robotics, and Autonomous Systems
Data-Driven Predictive Control for Autonomous Systems
Ugo Rosolia, Xiaojing Zhang, and Francesco Borrelli, Annual Review of Control, Robotics, and Autonomous Systems
Autonomy in Rehabilitation Robotics: An Intersection
Brenna D. Argall, Annual Review of Control, Robotics, and Autonomous Systems
Annual Reviews is a nonprofit publisher with a mission to synthesize and integrate knowledge for the progress of science and the benefit of society. We currently publish 51 highly cited journals in the Biomedical, Life, Physical, and Social Sciences, including Economics.
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