Supercomputers can crunch through data at enormous velocity, enabling them to recognize patterns and warning signals well before a human can. A content moderation algorithm can also be deployed on a number of channels at once, providing consistent analysis and feedback to all. AI models and computers don’t suffer from mental fatigue; their eyes don’t gloss over.
Why Human Content Moderatoration Isn’t Enough
AI-based content moderation solutions are also better at pinpointing patterns that a human might miss. Early pattern recognition is essential to protecting community members. Certain patterns, such as an adult male asking a pre-teen girl what she wore to school that day, can be detected quickly by AI, whereas a human might not identify that relationship as grooming until much later.
While human content moderators can interpret nuance and context of an interaction, they do not approach these as consistently as an AI-based solution. No matter how skilled or well-trained your moderators are; or how clearly your community guidelines have been communicated; moderators still have an overwhelming workload and expectations of productivity that create extreme cognitive stressors. These provide exactly the conditions in which a person’s unconscious bias can float to the surface and affect their instinctive responses.
It is critical in training AI to ensure that unconscious biases don’t affect the initial creation of an algorithm, and that the training data that is used is cleared of bias as well.
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Complexities of Online Content Moderation
Until recently, it has been difficult to train AI algorithms to address the more complex nuance, context, and variables of human interactions. But as AI models become more refined and more training data is available, these tools are becoming better and better at ‘reading the room’ - providing more accurate identification of the nuance and context of complex online interactions.
For example, the use of an emoji can significantly alter the meaning of a message, as shown in this example:
Researchers created a model that was trained using 1.2 million relevant tweets, then tested it - and found the model achieved 75% accuracy in sarcasm detection (no, really!) To verify these results, the same information was given to human moderators, whose interpretations agreed with the model 82.4% of the time. The people in the study only agreed with one another 76.1% of the time.
With the right amount of time and availability of training data, AI-based content moderation solutions can get more and more refined, improving accuracy in identifying harmful behaviors and giving online platforms the ability to respond quickly and consistently to protect users.
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¹ Image provided by: https://www.ofcom.org.uk/__data/assets/pdf_file/0028/157249/cambridge-consultants-ai-content-moderation.pdf