Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Can Machine Learning Help Prevent Local Crime? Public safety has always been one of society’s most urgent priorities. From ...

Third, we must share the benefits. If AI boosts productivity, the gains should not enrich only tech giants and investors.

AI startups are revolutionizing U.S. police departments by enhancing crime investigations, predictive policing, and real-time ...

Senators demand that the Justice Department halt funding to predictive policing programs Investigations have shown how a predictive policing algorithm was both discriminatory and inaccurate.

August 25, 2022 Defund the Police Algorithms Law enforcement is increasingly turning to software to surveil and anticipate crime. But a grassroots movement is emerging to resist algorithmic policing.

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...

Facial recognition algorithms, predictive policing models, and other surveillance tools must be rigorously tested for fairness and accuracy, with a commitment to eliminating any biases that ...