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Using machine learning to solve Trust and Safety problems has gained popularity recently. However, there are several challenges associated with it.
One of the primary challenges is dealing with data imbalance. In many cases, fraud is rare, leading to an imbalanced dataset. To address this, various techniques can be employed. Downsampling can be used for the negative labels (non-fraud cases) to reduce their representation, while upsampling can be applied to increase the positive cases (fraud cases). Another approach is assigning different weights to different classes, so that errors on positive cases are penalized more.
Data labeling is another significant challenge. Different methods can be utilized for data labeling. Human agents can be employed and trained using historical fraud cases. User reports and feedback provide a cost-effective way to obtain data, and self-supervised or unsupervised learning can be used, where cases are labelled based on heuristic rules.
Data quality poses another challenge. Skill levels of human agents can vary, leading to mislabeling of fraud and non-fraud cases. Also some fraud cases might not be enqueued to agents for labeling because they are not detected by heuristics or reported by user to enqueue , Ensuring high-quality training data is essential to develop accurate machine learning models.
Infrastructure is a crucial aspect of trust and safety or fraud defense. Managing large volumes of data from various microservices within a microservices architecture requires effective event processing. Real-time detection of fraudulent behaviors is necessary, which can be expensive to set up due to the volume and accuracy requirements. Event processing engines like Kafka, along with online and offline job processing, can be used. Storage systems like Redis can store both online and offline data, with a feature store aiding in retrieving data quickly for machine learning model predictions.
In summary, the challenges of using machine learning for Trust and Safety problems include data imbalance, data quality, and system infrastructure. Addressing these challenges requires employing suitable techniques and approaches while considering the specific context and requirements of the problem at hand.