Software Bias Is Being Addressed
February 27, 2017
Researchers are working to fix the problem of bias in software, we learn from the article, “He’s Brilliant, She’s Lovely: Teaching Computers to Be Less Sexist” at NPR’s blog, All Tech Considered. Writer Byrd Pinkerton begins by noting that this issue of software reflecting human biases is well-documented, citing this article from his colleague. He then informs us that Microsoft, for one, is doing something about it:
Adam Kalai thinks we should start with the bits of code that teach computers how to process language. He’s a researcher for Microsoft and his latest project — a joint effort with Boston University — has focused on something called a word embedding. ‘It’s kind of like a dictionary for a computer,’ he explains. Essentially, word embeddings are algorithms that translate the relationships between words into numbers so that a computer can work with them. You can grab a word embedding ‘dictionary’ that someone else has built and plug it into some bigger program that you are writing. …
Kalai and his colleagues have found a way to weed these biases out of word embedding algorithms. In a recent paper, they’ve shown that if you tell the algorithms to ignore certain relationships, they can extrapolate outwards.
And voila, a careful developer can teach an algorithm to fix its own bias. If only the process were so straightforward for humans. See the article for more about the technique.
Ultimately, though, the problem lies less with the biased algorithms themselves and more with the humans who seek to use them in decision-making. Researcher Kalai points to the marketing of health-related products as a project for which a company might actually want to differentiate between males and females. Pinkerton concludes:
For Kalai, the problem is not that people sometimes use word embedding algorithms that differentiate between gender or race, or even algorithms that reflect human bias. The problem is that people are using the algorithms as a black box piece of code, plugging them in to larger programs without considering the biases they contain, and without making careful decisions about whether or not they should be there.
So, though discoveries about biased software are concerning, it is good to know the issue is being addressed. We shall see how fast the effort progresses.
Cynthia Murrell, February 27, 2017
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