Algorithm Bias Quotes

Algorithms are opinions embedded in code. It’s really different from what you think most people think of algorithms. They think algorithms are objective and true and scientific. That’s a marketing trick. It’s also a marketing trick to intimidate you with algorithms, to make you trust and fear algorithms because you trust and fear mathematics. A lot can go wrong when we put blind faith in big data.

Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.

I would argue that one of the major problems with our blind trust in algorithms is that we can propagate discriminatory patterns without acknowledging any kind of intent.

If we all went to Google right now, or went to Yelp right now, we'd all get the same results, and that seems really, really broken to me. Foursquare should understand the neighborhoods I've spent a lot of time in, and the restaurants that I went to once but never went back to.

My well-discussed 'paranoia' urges me to believe that some tiny segment of the NSA's parsing algorithm is finely tuned to my voice.

If you Google some sites about the link between vaccines and autism, you can very quickly find that Google is repeating back to you your view about whether that link exists and not what scientists know, which is that there isn't a link between vaccines and autism. It's a feedback loop that's invisible.

I don't think anybody's ever notified that they were sentenced to an extra two years because their recidivism score had been high, or notified that this beat cop happened to be in their neighborhood checking people's pockets for pot because of a predictive policing algorithm. That's just not how it works.

There are lots of different ways that algorithms can go wrong, and what we have now is a system in which we assume because it's shiny new technology with a mathematical aura that it's perfect and it doesn't require further vetting. Of course, we never have that assumption with other kinds of technology.

With recidivism algorithms, for example, I worry about racist outcomes. With personality tests [for hiring], I worry about filtering out people with mental health problems from jobs. And with a teacher value-added model algorithm [used in New York City to score teachers], I worry literally that it's not meaningful. That it's almost a random number generator.

Competitors argue that Google rigs its search algorithms to demote listings for competing search engines. Many of the allegations of demotion come generally from sites of pretty questionable quality, such as Nextag and Foundem. Some of Google's primary competitors in 'specialized search' clearly place well in search results - Amazon and Yelp.

Britain and America are two examples where social media will only show you what you like.

Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape how we are categorized and advertised to.

The most important goal I had in mind was to convince people to stop blindly trusting algorithms and assuming that they are inherently fair and objective.

Just because you listen to The National, Spotify might tell you that you want to listen to The Lumineers' music. Well, maybe you don't.