Twitter Unveils Algorithmic Fairness Initiative to Offer More Transparency in Its AI

Twitter said the plan aims to tackle "the potential harmful effects of algorithmic decisions."

By Agence France-Presse | Updated: 15 April 2021 09:44 IST

The initiative calls for "taking responsibility for our algorithmic decisions"

Highlights

Twitter move follows series of controversies at Google's AI ethics team
The Twitter team is also building explainable ML solutions
The team would be sharing what it learns with outside researchers

Twitter said Wednesday it was launching an initiative on "responsible machine learning" that will include reviews of algorithmic fairness on the social media platform.

The California messaging service said the plan aims to offer more transparency in its artificial intelligence and tackle "the potential harmful effects of algorithmic decisions."

The move comes amid heightened concerns over algorithms used by online services, which some say can promote violence or extremist content or reinforce racial or gender bias.

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"Responsible technological use includes studying the effects it can have over time," said a blog post by Jutta Williams and Rumman Chowdhury of Twitter's ethics and transparency team.

"When Twitter uses (machine learning), it can impact hundreds of millions of tweets per day and sometimes, the way a system was designed to help could start to behave differently than was intended."

The initiative calls for "taking responsibility for our algorithmic decisions" with the aim of "equity and fairness of outcomes," according to the researchers.

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"We're also building explainable ML solutions so you can better understand our algorithms, what informs them, and how they impact what you see on Twitter."

Williams and Chowdhury said the team would be sharing what it learns with outside researchers "to improve the industry's collective understanding of this topic, help us improve our approach, and hold us accountable."

The Twitter move follows a series of controversies at Google's AI ethics team which resulted in the firing of two top researchers and the resignation of a high-ranking scientist.

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