Representational harms are widely recognized among fairness-related harms caused by generative language systems. However, their definitions are commonly under-specified. We present a framework, grounded in speech act theory (Austin, 1962), that conceptualizes representational harms caused by generative language systems as the perlocutionary effects (i.e., real-world impacts) of particular types of illocutionary acts (i.e., system behaviors). Building on this argument and drawing on relevant literature from linguistic anthropology and sociolinguistics, we provide new definitions stereotyping, demeaning, and erasure. We then use our framework to develop a granular taxonomy of illocutionary acts that cause representational harms, going beyond the high-level taxonomies presented in previous work. We also discuss the ways that our framework and taxonomy can support the development of valid measurement instruments. Finally, we demonstrate the utility of our framework and taxonomy via a case study that engages with recent conceptual debates about what constitutes a representational harm and how such harms should be measured.

This will make your project more interesting and potentially more impactful. This could include using a new machine learning library, implementing a novel algorithm, or combining multiple techniques in a creative way. Think about how your project could solve a real-world problem or make a difference in people's lives. Make sure to include clear explanations of your methods and results, along with any code or data you used. Overall, the key to standing out with your machine learning projects is to be creative, think outside the box, and focus on the real-world impact of your work.

Artificial intelligence is today's most discussed and debated technology, generating widespread adulation and anxiety, and significant government and business interest and investments. But six years after DeepMind's AlphaGo defeated a Go champion, countless research papers showing AI's superior performance over humans in a variety of tasks, and numerous surveys reporting rapid adoption, what is the actual business impact of AI? "2021 was the year that AI went from an emerging technology to a mature technology... that has real-world impact, both positive and negative," declared the 2022 AI Index Report. The 5th installment of the index measures the growing impact of AI in a number of ways, including private investment in AI, the number of AI patents filed, and the number of bills related to AI that were passed into law in legislatures of 25 countries around the world. There is nothing in the report, however, about "real-world impact" as I would define it--measurably successful, long-lasting and significant deployments of AI. There is also no definition of "AI" in the report. Going back to the first installment of the AI Index report, published in 2017, still does not yield a definition of what the report is all about.

BELMONT, MASSACHUSETTS - The recently released Stanford University 2022 AI Index Report argues that "2021 was the year that artificial intelligence (AI) went from an emerging technology to a mature technology... that has real-world impact, both positive and negative." The fifth installment of the index measures the growing impact of AI in several ways: Private investment in AI more than doubled in 2021; the cost to train an image classification has decreased by 63.6 percent and training times have improved by 94.4 percent since 2018; the number of AI patents filed in 2021 was more than 30 times higher than in 2015; and, since 2015, 18 times more AI-related bills were passed into law in the legislatures of 25 countries around the world. However, there is very little in the report about successful deployments of AI, and nothing regarding the progress of AI in healthcare, a field that has been considered a prime candidate for demonstrating AI's real-world impact. The first study to demonstrate AI's potential in detecting disease from medical images was released in 2016. Describing the application of deep learning (DL) to screening for diabetic retinopathy, this study by Google researchers was selected by the editors of the Journal of the American Medical Association as being one of the top 10 most influential papers of the decade. Also in 2016, Turing-award winner Geoffrey Hinton, widely considered to be the "godfather of deep learning," suggested that the training of radiologists should be abolished, as it was "completely obvious" to him that "within five years, deep learning is going to do better."

A scientist who wrote a leading textbook on artificial intelligence has said experts are "spooked" by their own success in the field, comparing the advance of AI to the development of the atom bomb. Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology. "The AI community has not yet adjusted to the fact that we are now starting to have a really big impact in the real world," he told the Guardian. "That simply wasn't the case for most of the history of the field – we were just in the lab, developing things, trying to get stuff to work, mostly failing to get stuff to work. So the question of real-world impact was just not germane at all. And we have to grow up very quickly to catch up."

A scientist who wrote a leading textbook on artificial intelligence has said experts are "spooked" by their own success in the field, comparing the advance of AI to the development of the atom bomb. Prof Stuart Russell, the founder of the Center for Human-Compatible Artificial Intelligence at the University of California, Berkeley, said most experts believed that machines more intelligent than humans would be developed this century, and he called for international treaties to regulate the development of the technology. "The AI community has not yet adjusted to the fact that we are now starting to have a really big impact in the real world," he told the Guardian. "That simply wasn't the case for most of the history of the field – we were just in the lab, developing things, trying to get stuff to work, mostly failing to get stuff to work. So the question of real-world impact was just not germane at all. And we have to grow up very quickly to catch up."

ZzappMalaria, a Jerusalem-based startup whose mobile app aims to help identify potential sources of malaria, has won a first prize of $3 million in the IBM Watson AI XPRIZE competition. The firm was also selected as the "Most Inspiring Team" in the People's Choice Award. The IBM Watson AI XPRIZE Challenge was launched in 2016 to promote the use of AI to solve the world's most pressing problems. Aifred Health, a Montreal-based digital health company focused on providing support for clinical decisions for mental health, won second place, getting a $1 million prize. Marinus Analytics, a Pittsburg, US-based firm that uses AI to quickly turn big data into actionable intelligence, helps fight human trafficking by saving hours and sometimes days of investigative time to find traffickers and recover victims.