“There are billions of posts, comments and messages across our services each day, and since it’s impossible to review all of them, we review content once it is reported to us. There have been terribly tragic events — like suicides, some live streamed — that perhaps could have been prevented if someone had realized what was happening and reported them sooner. There are cases of bullying and harassment every day, that our team must be alerted to before we can help out. These stories show we must find a way to do more.” — Mark Zuckerberg
This is hard.
I built a company (Two Hat Security) that’s also contracted to process 4 billion chat messages, comments, and photos a day. We specifically look for high-risk content in real-time, such as bullying, harassment, threats of self-harm, and hate speech. It is not easy.
“There are cases of bullying and harassment every day, that our team must be alerted to before we can help out. These stories show we must find a way to do more.”
I must ask — why wait until cases get reported?
If you wait for a report to be filed by someone, haven’t they already been hurt? Some things that are reported can never be unseen. Some like Amanda Todd cannot have that image retracted. Others post when they are enraged or drunk and the words like air cannot be taken back. The saying goes, “What happens in Vegas stays in Vegas, Facebook, Twitter and Instagram forever” so maybe some things should never go live. What if you could proactively create a safe global community for people by preventing (or pausing) personal attacks in real-time instead?
This, it appears, is key to creating the next vision point.
“How do we help people build an informed community that exposes us to new ideas and builds common understanding in a world where every person has a voice?”
One of the biggest challenges to free speech online in 2017 is that we allow a small group of toxic trolls the ‘right’ to shut up a larger group of people. Ironically, these users’ claim to free speech often ends up becoming hate speech and harassment, destroying the opportunity for anyone else to speak up, much like bullies in the lunchroom. Why would someone share their deepest thoughts if others would just attack them? Instead, the dream for real conversations gets lost beneath a blanket of fear. Instead, we get puppy pictures, non-committal thumbs up, and posts that are ‘safe.’ If we want to create an inclusive community, people need to be able to share ideas and information online without fear of abuse from toxic bullies. I applaud your manifesto, as it calls this out, and calls us all to work together to achieve this.
Fourteen years ago, we both set out to change the social network of our world. We were both entrepreneurial engineers, hacking together experiments using the power of code. It was back in the days of MySpace and Friendster and the later Orkut. We had to browse to every single friend we had on MySpace just to see if they wrote anything new. To solve this I created myTWU — a social stream of all the latest blogs and photos of fellow students, alumni and sports teams on our internal social tool. Our office was in charge of building online learning but we realized that education is not about ideas but community. It was not enough to dump curriculum online for independent study, people needed places of belonging.
A year later “The Facebook” came out. You reached beyond the walls of one University and over time opened it to the world.
So I pivoted. As part of our community, we had a little chat room where you could waddle around and talk to others. It was a skin of a little experiment my brother was running. He was caught by surprise when it grew to a million users which showed how users long for community and places of belonging. In those days chat rooms were the dark part of the web and it was nearly impossible to keep up with the creative ways users tried to hurt each other.
So I was helping my brother code the safety mechanisms for his little social game. That little social game grew to become a global community with over 300 million users and Disney bought it back in 2007. I remember huddling in my brother’s basement rapidly building the backend to fix the latest trick to get around the filter. Club Penguin was huge.
After a decade of kids breaking the filter and building tools to moderate the millions upon millions of user reports, I had a breakthrough. By then I was security at Disney, with the job to hack everything with a Mouse logo on it. In my training, we learned that if someone DDoS’es a network or tries to break the system, you find a signature of what they are doing and turn up the firewall against that.
“What if we did that with social networks and social attacks?” I thought.
I’ve spent the last five years building an AI system with signatures and firewalls as it relates to social content. As we process billions of messages with Community Sift, we build reputation scores in real-time. We know who the trolls are — they leave digital signatures everywhere they go. Moreover, I can adjust the AI to turn up the sensitivity only where it counts. In so doing we drastically dropped false positives, opened communication with the masses while detecting the highest risk when it matters.
I had to build whole new AI algorithms to do this since traditional methods only hit 90–95% percent. That is great for most AI tasks but when it comes to cyber-bullying, hate-speech, and suicide the stakes are too high for the current state of art in NLP.
“To prevent harm, we can build social infrastructure to help our community identify problems before they happen. When someone is thinking of suicide or hurting themselves, we’ve built infrastructure to give their friends and community tools that could save their life.”
Since Two Hat is a security company, we are uniquely positioned to prevent harm with the largest vault of high-risk signatures, like grooming conversations and CSAM (child sexual abuse material.) In collaboration with our partners at the RCMP (Royal Canadian Mounted Police), we are developing a system to predict and prevent child exploitation before it happens to complement the efforts our friends at Microsoft have made with PhotoDNA. With CEASE.ai, we are training AI models to find CSAM, and have lined up millions of dollars of Ph.D. research to give students world-class experience in working with our team.
“Artificial intelligence can help provide a better approach. We are researching systems that can look at photos and videos to flag content our team should review. This is still very early in development, but we have started to have it look at some content, and it already generates about one-third of all reports to the team that reviews content for our community.”
It is incredible what deep learning has accomplished in the last few years. And although we have been able to see near perfect recall in finding pornography with our current work there is an explosion of new topics we are training on. Further, the subtleties you outline are key.
I look forward to two changes to resolve this:
- I call on networks to trust that their users have resilience. It is not imperative to find everything just the worst. If all content can be sorted by maybe bad to absolutely bad we can then draw a line in the sand and say these cannot be unseen and these the community will find. In so doing we don’t have to wait for technology to reach perfection nor wait for users to report things we already know are bad. Let computers do what they do well and let humans deal with the rest.
- I call on users to be patient. Yes, sometimes in our ambition to prevent harm we may find a Holocaust photo. We know this is terrible but we ask for your patience. Computer vision is like a child still learning. A child that sees that image for the first time is still deeply impacted and is concerned. Join us to report these problems and to help train the system to mature and discern.
However, you are right that many more strides need to happen to get this to where it needs to be. We need to call on the world’s greatest thinkers. Of all the hard problems to solve, our next one is child pornography (CSAM). Some things cannot be unseen. There are things when seen re-victimize over and over again. We are the first to gain access to hundreds of thousands of CSAM material and train deep learning models on them with CEASE.ai. We are pouring millions of dollars and putting the best minds on this topic. It is a problem that must be solved.
And before I move on I want to give a shout out to your incredible team whom I have had the chance to volunteer at hack-a-thons with and who have helped me think through how to get this done. Your company commitment to social good is outstanding and they have helped many other companies and not for profits.
“The guiding principles are that the Community Standards should reflect the cultural norms of our community, that each person should see as little objectionable content as possible, and each person should be able to share what they want while being told they cannot share something as little as possible. The approach is to combine creating a large-scale democratic process to determine standards with AI to help enforce them.”
That is cool. I have got a couple of the main pieces needed for that completed if you need them.
“The idea is to give everyone in the community options for how they would like to set the content policy for themselves. Where is your line on nudity? On violence? On graphic content? On profanity?”
I had the chance to swing by Twitter 18 months ago. I took their sample firehose and have been running it through our system. We label each message across 1.8 million of our signatures, then put together a quick demo of what it would be like if you could turn off the toxicity on Twitter. It shows low, medium, and high-risk. I would not expect to see anything severe on there, as they have recently tried to clean it up.
We are committed to helping you and the Facebook team with your mission to build a safe, supportive, and inclusive community. We are already discussing ways we can help your team, and we are always open to feedback. Good luck on your journey to connect the world, and hope we cross paths next time I am in the valley.
CEO, Two Hat Security
Originally published on Medium