Search results
Results from the WOW.Com Content Network
Clearview AI, Inc. is an American facial recognition company, providing software primarily to law enforcement and other government agencies. [2] The company's algorithm matches faces to a database of more than 20 billion images collected from the Internet, including social media applications. [ 1 ]
Law enforcement officials worry investigators will waste time and resources trying to identify and track down exploited children who don’t really exist. Lawmakers, meanwhile, are passing a flurry of legislation to ensure local prosecutors can bring charges under state laws for AI-generated “deepfakes” and other sexually explicit images of ...
The New York Times has learned that over 600 law enforcement agencies in the US and Canada have signed up in the past year to use software from little-known startup Clearview AI that can match ...
This bias often stems from training data that reflects historical and systemic inequalities. For example, AI systems used in hiring, law enforcement, or healthcare may disproportionately disadvantage certain racial groups by reinforcing existing stereotypes or underrepresenting them in key areas.
The comments from the Justice Department's No. 2 leader underscore the extent to which law enforcement officials are concerned about how the rapidly developing technology could be exploited, by ...
The Algorithmic Justice League (AJL) is a digital advocacy non-profit organization based in Cambridge, Massachusetts.Founded in 2016 by computer scientist Joy Buolamwini, the AJL uses research, artwork, and policy advocacy to increase societal awareness regarding the use of artificial intelligence (AI) in society and the harms and biases that AI can pose to society. [1]
Los Angeles Times owner Patrick Soon-Shiong, who blocked the newspaper’s endorsement of Kamala Harris and plans to overhaul its editorial board, says he will implement an artificial intelligence ...
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).