Quando o reconhecimento facial erra mais (ITA 2020)
About seven years ago, three researchers at the University of Toronto built a system that could analyze thousands of photos and teach itself to recognize everyday objects, like dogs, cars and flowers. The system was so effective that Google bought the tiny start-up these researchers were only just getting off the ground. And soon, their system sparked a technological revolution. Suddenly, machines could “see” in a way that was not possible in the past. This made it easier for a smartphone app to search your personal photos and find the images you were looking for. It accelerated the progress of driverless cars and other robotics. And it improved the accuracy of facial recognition services, for social networks like Facebook and for the country’s law enforcement agencies. But soon, researchers noticed that these facial recognition services were less accurate when used with women and people of color. Activists raised concerns over how companies were collecting the huge amounts of data needed to train these kinds of systems. Others worried these systems would eventually lead to mass surveillance or autonomous weapons. Fonte: Matz, Cade. Seeking Ground Rules for A. I. www.nytimes.com, 01/03/2019. Adaptado. Acessado em Agosto/2019.)
Fonte: The New York Times, 2019-03-01.
De acordo com as informações do texto, selecione a alternativa que melhor complete a afirmação: The new system proved to be less precise when
- A) applied to driverless cars.
- B) adjusted to users’ face recognition in social networks.
- C) identifying inanimate objects like cars and plants.
- D) used to identify Africans and African descendants.
- E) tested by American law enforcement agencies.