Thank you Chairman Walden, Ranking Member Pallone, and the committee, for the opportunity to speak on behalf of Twitter to the American people. I look forward to our conversation about our commitment to impartiality, transparency, and accountability.
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Our early and strong defense of open and free exchange has enabled Twitter to be THE platform for activists, marginalized communities, whistleblowers, journalists, governments and the most influential people around the world. Twitter will always default to open and free exchange.
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A default to free expression left unchecked can generate risks and dangers for people. It’s important Twitter distinguishes between people’s opinions and behaviors, and disarms behavior intending to silence another person, or adversely interfere with their universal human rights.
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We build our policies and rules with a principle of impartiality: objective criteria, rather than on the basis of bias, prejudice, or preferring the benefit to one person over another for improper reasons. If we learn we failed to create impartial outcomes, we work hard to fix.
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In the spirit of accountability and transparency: recently we failed our intended impartiality. Our algorithms were unfairly filtering 600,000 accounts, including some members of Congress, from our search auto-complete and latest results. We fixed it. But how did it happen?
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Our technology was using a decision making criteria that considers the behavior of people following these accounts. We decided that wasn’t fair, and corrected. We‘ll always improve our technology and algorithms to drive healthier usage, and measure the impartiality of outcomes.
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Bias in algorithms is an important topic. Our responsibility is to understand, measure, and reduce accidental bias due to factors such as the quality of the data used to train our algorithms. This is an extremely complex challenge facing everyone applying artificial intelligence.
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For our part, machine learning teams at Twitter are experimenting with these techniques and developing roadmaps to ensure present and future machine learning models uphold a high standard when it comes to algorithmic fairness. It’s an important step towards ensuring impartiality.
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Looking at the data, we analyzed tweets sent by all members of the House and Senate, and found no statistically significant difference between the number of times a tweet by a Democrat is viewed versus a Republican, even after our ranking and filtering of tweets has been applied.
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Also, there’s a distinction we need to make clear. When people follow you, you’ve earned that audience. And we have a responsibility to make sure they can see your tweets. We do not have a responsibility, nor you a right, to amplify your tweets to audiences that don’t follow you.
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What our algorithms decide to show in shared spaces, like search results, is based on thousands of signals that constantly learn and evolve over time. Some of those signals are engagement, some are the number of abuse reports. We balance all of these to prevent gaming our system.
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We acknowledge the growing concern people have of the power held by companies like Twitter. We believe it’s dangerous to ask Twitter to regulate opinions or be the arbiter of truth. We’d rather be judged by the impartiality of outcomes, and criticized when we fail this principle.
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In closing, when I think of our work, I think of my mom and dad in St. Louis, a Democrat and a Republican. We had lots of frustrating and heated debates, but looking back, I appreciate I was able to hear and challenge different perspectives. And I appreciate I felt safe to do so.
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We believe Twitter helps people connect to something bigger than themselves, shows all the amazing things happening in the world, and all the things we need to acknowledge and address. We‘re constantly learning how to make it freer and healthier for all to participate. Thank you.
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End of conversation
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No one wants the makers of Twitter to personally rule by fiat. We want the makers of Twitter to take ideas like moderation and safety seriously, and set up effective distributed community systems for signal boost, attenuation, and adjudication. If you're trying, we can't tell.
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What you ARE doing is exactly the opposite of what you claim. You're not sharing the moderation load with the community; you're concentrating what little moderation and adjudication there is behind an opaque wall controlled by the company.
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You're providing users with coarse block/mute tools that divide us, taking arbitrary actions with no explanation, and operating a report/sanction Star Chamber that no one can see or appeal, and no one is satisfied with.
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If you are even 1/100th serious about everything you're spewing in this tweetstorm, then you NEED to start working on transparent moderation processes that are community operated, not just either [doing nothing | flinging banhammers] based on a logic only known inside Twitter.
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Some people get banned from Twitter for incomprehensibly trivial reasons, while others threaten nuclear wars and don't even get a timeout.
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And if the person is sufficiently notable, you drag yourself out here to explain/apologize/proselytize about the reasons for your choices. Please, save us the platitudes. "It's my website and I do what I want" is fine, but the idea you can't do this better is false.
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Involve. The. Community. Lots of us have vast experience of moderation and we can help, at scale. You clearly are swinging a sword in a small, dark room. Stop.
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