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Despite the success we are seeing with our use of algorithms to combat abuse, manipulation, and bad faith actors, we recognize that even a model created without deliberate bias may nevertheless result in biased outcomes, bias can happen inadvertently due to many factors, such as the quality of the data used to train our models. In addition to ensuring that we are not deliberately biasing the algorithms, it is our responsibility to understand, measure, and reduce these accidental biases. This is an extremely complex challenge in our industry, and algorithmic fairness and fair machine learning are active and substantial research topics in the machine learning community.