But as human intelligence relies on accurate information, so too do machines.
Algorithms need training data to learn from.
This training data is created, selected, collated and annotated by humans.
And therein lies the problem.
This bias can, and often does, find its way into AI platforms.
This happens completely under the radar and through no concerted effort from engineers.
Unfortunately, its very much a human-shaped problem.
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Data sets about humans are particularly susceptible to bias, while data about the physical world are less susceptible.
Step up Tay, Microsofts doomed social AI chat bot.
Not an Issue of Malice
Bias is more of a nuanced issue in AI development.
If anything, these AI programs are simply reflecting the example bias that already exists.
Race throws up even more worrying examples of the danger of bias in AI though.
There is a lot to unpack in this.
These predictions have led to defendants being handed longer sentences, as in the case of Wisconsin v. Loomis.
Such transparency is seen by many as an essential check to put in place alongside AI development.
Neural networks use deep learning algorithms, creating connections organically as they evolve.
The focus should both be on creating unbiased data sets as well as unbiased AI algorithms, says Jason.
People must recognize biased data and actively seek to counteract it.
This recognition takes training.
This is a key issue for companies utilising AI for their hiring programs.
Using historically restrictive data will only recycle the problem with these algorithms.
The cause of bias in AI is also its solution people.
Unfortunately, because bias is often so subtle, dedicated training is needed to weed it out.
So the primary danger is placing our faith where it doesnt belong, says Jason.
Hopefully, this translates into further research and resources for tackling the problem.
Unfortunately, someone at Microsoft accidentally activated her Twitter again before she was ready for release.
Cue poor old Tay tweeting about smoking kush in front of the police!
John Murrayis a tech correspondent focusing on machine learning atBinary District, where this article was originally published.
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