""Part of the problem is the data being fed in," Crider said.
"Historical data is being fed in [to algorithms] and they are replicating the [existing] bias."
Webb agrees. "A lot of [the issue] is about the data that the algorithm learns from," she said. "For example, a lot of facial recognition technology has come out ... the problem is, a lot of [those] systems were trained on a lot of white, male faces.
"So when the software comes to be used it's very good at recognizing white men, but not so good at recognizing women and people of color. And that comes from the data and the way the data was put into the algorithm."
Webb added that she believed the problems could partly be mitigated through "a greater attention to inclusivity in datasets" and a push to add a greater "multiplicity of voices" around the development of algorithms."
