Smart Software: Perhaps Objective Processes Are Inherently Biased?
June 22, 2021
Is prejudice innate or learned? Does language contain biases which are unnoticed by those who speak one? “The Efforts to Make Text-Based AI Less Racist and Terrible” answers these questions clearly. Smart software is going to generate remarkable distinctions.
The summary of the the projects which are laboring to deal with this built in behavior is interesting. The notion of amplification is fascinating as well. There are different types of “amplification” and none of them is immune to the magic of software ingesting human content and outputting — I hate to say it — outputs.
And those outputs are a challenge to the whiz kids who want to produce unbiased, accurate (from someone’s point of view), objective (from someone’s assumptions about what is “objective”), and useful smart software.
The write provides an example:
Other researchers are trying different approaches. Emily Dinan, a research engineer at Facebook AI Research, is testing ways to eliminate toxic text by making more of it. Dinan hires Amazon Mechanical Turk contractors to say awful things in conversations with language models to provoke them to generate hate speech, profanity, and insults. Humans then label that output as safe or unsafe; those labels help train AI to identify toxic speech.
Are there biases in intentional content? Oh, I think so.
Here’s an interesting statement from the write up:
Text generated by large language models is coming ever closer to language that looks or sounds like it came from a human, yet it still fails to understand things requiring reasoning that almost all people understand.
Smart software? Sure, no problem. Trying is good: Grants, desperate companies, and those chasing tenure are part of the motivational feedback loop. Amplification, a fancy word indeed.
Stephen E Arnold, June 22, 2021
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