Four in ten Americans have experienced online harassment, while 66% have witnessed it.  This kind of hateful online environment needs to stop.  In order to stop it, we first have to fully understand it. 

We need to hear from all communities about what they’re seeing and experiencing online. We need your voice in defining what hate speech online is. That’s why we’ve started “Decoding Hate”.

What is Decoding Hate?

“Decoding Hate” is a community focused initiative. It empowers people from across the country to influence how hate is detected online through AI technology.

At “Decoding Hate” events, individuals or groups will review comments from internet platforms. Each person will answer questions about these comments from their perspective. Questions like; Are they hateful? Who are they targeting? Are they saying one group is greater than another?  Do they indicate violence? The events can culminate with a guided discussion about “Decoding Hate” and our day-to-day experiences with the world online.

Why does your participation matter?

Your comment reviews are incorporated into a machine learning algorithm that helps makes technology better at identifying and understanding hate from a variety of perspectives.  The Online Hate Index algorithm will ultimately become a tool to provide a diverse, community-oriented conceptualization of hate speech online and furthermore help lead to safer and more inclusive online interactions. The more people and perspectives the machine can learn from, the better it we can build a safer online environment for everyone.

How Do I Sign Up?

You can sign up for “Decoding Hate” in two categories

  1. Be a Decoding Hate Host: Participate in or view an hour long training webinar with the team at UC Berkeley’s D-Lab. Then convene people in your community for the hour long Decoding Hate session.  No experience necessary!
  2. Be a host of one and participate in “Decoding Hate” as an individual!

To sign up for “Decoding Hate”, please fill out the form below and lend your voice to fight against hate online: