Status Quo: Posts falsely flagged for toxicity
Skewed data leads to a biased AI
Content output on social media has exploded in the last decade. To handle basic moderation, machines have been tasked with assessing human expression. While hate speech frequently falls through the cracks of the algorithm, minority voices are invisibly and systematically suppressed.
Machines learning from datasets about humans are falsely considered to be objective arbiters of truth. The Perspective API developed by Jigsaw (an incubator of Google's Alphabet) is a prime example that publicly illustrates the scope and depth of the issue that bias in AI poses for leading tech players. The Perspective AI is tasked with calculating a toxicity score that indicates how healthy a comment is to a given conversation. Every small change in wording, grammar and context can have a significant effect on the toxicity score. What sounds like a straightforward concept can have extremely undesirable consequences: When using biased training data that demonstrates underrepresentation of oppressed voices, the AI automatically learns to suppress their free expression.
Social media is today’s way of expressing feelings and opinions. It depicts the core of our speech. If the assessment of harmful posts or comments is skewed, this must be addressed. Once an AI has picked up a certain bias, the effects are amplified. What are silenced individuals today, will be entire minorities tomorrow. Without a channel of expression, they will fade into obscurity.
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