| To me it is fallacy of bigtech to misclassify moderation problem as just a typical ML problem. Hence a false belief that ML models, standard approaches that they use for their other ML problems, and cheap annotators can solve it. What, I think, can be done: 1. Don't just hire expensive PhDs and hope that algorithms can correctly classify racism, etc.
Hire an expensive product visionary who can build holistic approach to address moderation problem and knows what is solvable with ML and what needs pivoting to human-guided resolution. 2. Don't hire lots of cheap annotators in "Rural Inida" but hire or train fewer expensive experts and give them powerful tools that can scale their work to handle efficiently all suspicious traffic. 3. Give power to "normal" users to flag inappropriate content or behavior and loop this in a thought-out workflow with your ML and your experts on the other end. 4. Partition users and content so that bad actors and bad content get clustered away and are less easily accessible for others. Why bigtech companies don't do it? Besides fallacy of thinking this is a "typical" ML problem, moderation is hard. It's also hard to make a business case for short-minded bosses and prove revenue increase or expenses cut. Lastly, some bigtech ones benefit a lot from abuse so they can't change immediately to stop it all. |