Google Executive Warns of Face ID Bias (bbc.com)

Facial recognition technology does not yet have "the diversity it needs" and has "inherent biases," a top Google executive has warned. From a report: The remarks, from the firm's director of cloud computing, Diane Greene, came after rival Amazon's software wrongly identified 28 members of Congress, disproportionately people of colour, as police suspects. Google, which has not opened its facial recognition technology to public use, was working on gathering vast sums of data to improve reliability, Ms Greene said. However, she refused to discuss the company's controversial work with the military. "Bad things happen when I talk about Maven," Ms Greene said, referring to a soon-to-be abandoned project with the US military to develop artificial intelligence technology for drones. After considerable employee pressure, including resignations, Google said it would not renew its contract with the Pentagon after it lapses some time in 2019. The firm has not commented on the deal since, only to release a set of "AI principles" that stated it would not use artificial intelligence or machine learning to create weapons.

Comments

No, training sets may

[SuperKendall]

The technology reflects the biases of its inventors

The "technology" behind this is neural networks. How do they reflect bias at al?

They are nothing more than the ultimately transparent black box, reflecting whatever you choose to put into it...

They are so non-biases in fact, the same technology is used to detect if something is a cat or a slice of pizza, or even if tumors are cancerous or not. Yet you could claim racial bias (you did not state that but it was implied).

Like I said, care needs to be taken both in training and in testing, that is where bias may be introduced. But the technology itself is inherently unbiased and a great tool, if used correctly.

That is not right

[SuperKendall]

the two most straightforward are Bad training data

Which is not inherent in the technology as I've pointed out in every post.

and Bugs. Bugs are an issue in ANY software and neural networks are no different and bugs can result in biases.

That reflects a lack of understanding of how neural networks work. They actually are not buggy themselves as they are very simple; all bugs are entirely represented in training data and testing, not in the software itself. You put something in the black box and it classifies it in some desired way, the way the box works is all about the training data.

Furthermore it's a bit odd to claim bugs are a kind of "bias" as usually they are more about failure than bias...