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Her research focuses on the role of artificial intelligence in journalism. Broussard has published features and essays in many outlets including The Atlantic, Harper’s Magazine, and Slate Magazine. Broussard has published a wide range of books examining the intersection of technology and social practice.
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions made by such models after a learning process may be considered unfair if they were based on variables considered sensitive (e.g., gender, ethnicity, sexual orientation, or disability).
Friedman and Nissenbaum identify three categories of bias in computer systems: existing bias, technical bias, and emergent bias. [27] In natural language processing , problems can arise from the text corpus —the source material the algorithm uses to learn about the relationships between different words.
Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. [2] For example, algorithmic bias has been observed in search engine results and social media platforms.
The letter highlights both the positive and negative effects of artificial intelligence. [7] According to Bloomberg Business, Professor Max Tegmark of MIT circulated the letter in order to find common ground between signatories who consider super intelligent AI a significant existential risk, and signatories such as Professor Oren Etzioni, who believe the AI field was being "impugned" by a one ...
From this higher body, following the recommendations made by the R&D Strategy on Artificial Intelligence of 2018, [133] the National Artificial Intelligence Strategy (2020) was developed, which already provided for actions concerning the governance of artificial intelligence and the ethical standards that should govern its use. This project was ...
Recursive self-improvement (RSI) is a process in which an early or weak artificial general intelligence (AGI) system enhances its own capabilities and intelligence without human intervention, leading to a superintelligence or intelligence explosion.
The Riemann hypothesis catastrophe thought experiment provides one example of instrumental convergence. Marvin Minsky, the co-founder of MIT's AI laboratory, suggested that an artificial intelligence designed to solve the Riemann hypothesis might decide to take over all of Earth's resources to build supercomputers to help achieve its goal. [2]