Slack has been siphoning user data to train AI models without asking permission

Slack used user data to train AI without explicit consent, causing privacy concerns.

: Slack has been collecting user messages and files for AI model training without clear user consent, leading to privacy uproar. The company offered a response that clarified data usage policies, but inconsistencies and an opt-in-by-default model have intensified criticism. Users now demand transparent opt-out options and better communication about data practices.

Slack has come under fire for using customer data, including messages and files, to train its AI models without explicitly informing users or obtaining their consent. This practice was spotlighted by a TechSpot article after a critical social media post by Corey Quinn, which led to widespread criticism and discussions about privacy and ethical data use in machine learning. Following the public outcry, Slack responded by stating the data helps train only non-generative ML models like channel recommendations and search functionalities, asserting that premium AI offerings do not use customer data.

Despite these assurances, users criticized Slack for not making the data training procedures clear from the beginning and providing a complex opt-out process that requires administrative action. Leading industry experts and competitors commented on the issue, underscoring the rising importance of privacy in tech and the potential reputational damage from such practices. This situation reflects broader challenges and ongoing debates in the tech industry concerning user data privacy, consent, transparency, and the ethics of AI development.

The controversy sheds light on the need for clearer privacy policies and truly informed consent in the use of customer data, especially as AI and machine learning become more integrated into business tools. Slack has admitted that their privacy terms require updating to reflect changes and clarify their data usage policies. This episode prompts a reevaluation of standard industry practices around data privacy, encouraging companies to adopt more user-centric data governance practices that prioritize transparency and user control.

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