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Slack artificial intelligence, machine learning and data usage
This guide is a supplemental resource that provides detailed information about the data practices that Slack uses in building artificial intelligence (AI) and machine learning (ML) features.
As defined in our privacy policy, customer data is comprised of the following:
Message data, e.g. the content of a message
File data, e.g. files uploaded to Slack
Object data, e.g. a channel or a list
Transcription data, e.g. the transcript from a huddle
Slack does not use customer data to train large language models (LLMs) used in generative AI features. As defined in the Slack privacy policy, our systems may analyse usage information (such as how often a feature is used or interacted with) and workspace information (such as the number of users in a workspace or workspace settings) for ML features.
Types of models
We use a variety of models to power Slack’s AI and machine learning features. Understanding these models and how they work is key to understanding how your data is used.
Generative models These models use third-party LLMs to create an output. Customer data is not used to train these models.
Predictive models These models use machine learning algorithms to power features such as channel recommendations and emoji suggestions. Most of our predictive models are global models, meaning that they are trained on aggregate data from multiple customers.
How features use models and data
The table below outlines the data and models that power specific Slack features.
Feature
Examples*
Data used
Generative model used?
Predictive model used?
Summarisation
Channel summaries, file summaries, huddle notes and recaps
Customer data: Message, file and transcription data
Other information: Workspace, account and usage information
Yes, e.g. to generate a summary
Note: This model never uses customer data for training
Yes, e.g. to personalise a summary
Note: This is a global model, but no customer data is used for training
Search
Natural language search, search ranking
Customer data: Message, file, transcription and Slack object data
Other information: Workspace, account and usage information
Yes, e.g. to generate a search answer
Note: This model never uses customer data for training
Yes, e.g. to rank search results
Note: This is a global model, but no customer data is used for training
Recommendations
User search, channel search, channel archive, join and leave suggestions and VIP
Customer data: N/A
Other information: Usage information
No
Yes, e.g. to make recommendations
Note: This is a global model, but no customer data is used for training
AI actions
AI workflow creation, summarise channel workflow step
Customer data: Message, file, transcription and Slack object data
Other information: Workspace and account information
Yes, e.g. to generate workflows from natural language queries and to power summary and search workflow steps
Note: This model never uses customer data for training
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