I think it's as clear as it can be, they go into much more detail and provide examples in their bullet points, here are some highlights:
Our model learns from previous suggestions and whether or not a user joins the channel we recommend. We protect privacy while doing so by separating our model from Customer Data. We use external models (not trained on Slack messages) to evaluate topic similarity, outputting numerical scores. Our global model only makes recommendations based on these numerical scores and non-Customer Data.
We do this based on historical search results and previous engagements without learning from the underlying text of the search query, result, or proxy. Simply put, our model can't reconstruct the search query or result. Instead, it learns from team-specific, contextual information like the number of times a message has been clicked in a search or an overlap in the number of words in the query and recommended message.
These suggestions are local and sourced from common public message phrases in the user’s workspace. Our algorithm that picks from potential suggestions is trained globally on previously suggested and accepted completions. We protect data privacy by using rules to score the similarity between the typed text and suggestion in various ways, including only using the numerical scores and counts of past interactions in the algorithm.
To do this while protecting Customer Data, we might use an etrnal model (not trained on Slack messages) to classify the sentiment of the message. Our model would then suggest an emoji only considering the frequency with which a particular emoji has been associated with messages of that sentiment in that workspace.
