In a recent update to its privacy policy, LinkedIn announced that it will begin using members’ public data to train its artificial intelligence models by default starting in early November 2025. The decision places the Microsoft-owned platform among a growing number of tech companies that are leveraging vast volumes of user-generated content to fuel the development of generative AI systems. This change, while legally permissible in many jurisdictions, has sparked a wide-ranging discussion about user consent, data repurposing, and the boundaries of acceptable AI training practices in professional online environments.
According to LinkedIn’s updated policy, information users share publicly on their profiles; including job titles, work history, skills, endorsements, posts, comments, and interactions; may be used to improve the company’s AI tools and services unless users explicitly opt out. The change does not include private messages, financial data, or sensitive identity information, and LinkedIn has stated that it remains committed to maintaining a secure and transparent platform. However, the default nature of the opt-in and the professional context in which the data was originally provided have raised questions about expectations of privacy, informed consent, and the broader implications for trust in AI-driven digital platforms.
What Kind of Data Will Be Used
LinkedIn’s update primarily concerns data that users have already made public through their profiles or activity on the platform. This includes public posts, article contributions, profile descriptions, job titles, listed skills, and user-generated content visible to other members. The company has emphasized that data used for AI training will be aggregated and anonymized where possible to reduce the risk of identifiable personal information being misused.
Importantly, LinkedIn has clarified that private communications, account credentials, and personally sensitive data such as addresses, birthdates, or payment information are excluded from AI training data. The company also reassures users that all data handling is governed by its existing privacy framework and in compliance with relevant data protection laws such as the General Data Protection Regulation (GDPR) in the European Union and other local regulations where applicable.
Despite these assurances, the fact that such data will be included in training models by default; rather than through an opt-in mechanism; has drawn concern from digital rights groups and some members of the professional community. Many users may not be aware that their activity on LinkedIn could contribute to machine learning models, especially if they are not closely following privacy updates or product announcements.
Why LinkedIn Is Making This Move
LinkedIn’s decision is part of a broader shift within the technology industry, where companies are increasingly investing in AI-driven solutions to enhance product offerings, automate services, and personalize user experiences. By using real-world data from its platform, LinkedIn aims to develop more effective AI tools that can power features such as improved job recommendations, more relevant content discovery, smart career advice, and intelligent profile suggestions.
In essence, the company is leveraging the immense volume of professional data it hosts as a strategic asset to build proprietary AI capabilities that can maintain its competitive edge. Generative AI and large language models (LLMs) require massive amounts of diverse, high-quality data to improve accuracy and contextual understanding. LinkedIn’s platform offers a particularly valuable dataset in the professional and employment domain, making it a natural candidate for training systems designed to understand workplace trends, industry skills, and career trajectories.
From a business standpoint, this move may allow LinkedIn to build more sophisticated models that not only enhance the user experience but also open the door to new AI-powered services. These could include tools for automated résumé writing, recruiter recommendations, interview preparation assistance, and career development analytics. Such features may provide value to users, especially those actively seeking job opportunities or career guidance.
The Broader Trend of AI Data Collection
LinkedIn’s approach is not unique in the tech industry. Other major platforms including Google, Meta, and X (formerly Twitter) have similarly begun using publicly available user data to train AI models. In many cases, these updates have been implemented quietly or buried within broader terms of service changes, drawing criticism from advocates for digital transparency and data ethics.
The central tension in this trend revolves around the concept of “data repurposing”; using information originally shared for one purpose in entirely new contexts, such as AI training. While legal frameworks like GDPR require companies to specify and limit the purposes for which personal data is processed, some interpretations of existing consent models allow for such repurposing provided that notice is given and users are offered a way to opt out.
This legal gray area has led to inconsistent implementations across platforms and jurisdictions. In countries with strong data protection laws, such as Germany or South Korea, companies face stricter limitations on what user data can be used for AI training and under what conditions. In contrast, jurisdictions with looser privacy frameworks often see broader applications of user data with fewer restrictions.
As companies race to build and improve AI systems, user-generated content has become one of the most accessible and valuable resources available. However, the balance between innovation and individual rights remains a delicate one, especially when the default behavior favors the company’s interests over the user’s control.
Transparency and the Question of Consent
One of the most pressing concerns raised by privacy experts is the issue of informed consent. In LinkedIn’s case, users are automatically included in the data pool for AI training unless they take the time to opt out. Critics argue that this passive consent model places too much burden on users, many of whom may not be aware that such a setting even exists.
Furthermore, the nature of professional platforms like LinkedIn implies a certain level of trust. Users often provide detailed, accurate information about their careers, qualifications, and aspirations with the expectation that this data is being used primarily for networking, recruitment, and professional development. The reuse of this data for AI training introduces a shift in expectations that some users may find unsettling.
LinkedIn has published help articles explaining how users can manage their data preferences and opt out of AI training, but the effectiveness of this transparency effort depends on how clearly the information is communicated and how easily the settings can be accessed. User interface design, notification mechanisms, and platform defaults all play a role in shaping real-world consent.
Some critics argue that true informed consent should require an opt-in process, especially when the data is being used in ways that were not previously contemplated by the average user. Others point out that the practice of collecting consent through dense legal language or obscure settings may meet legal standards but fall short of ethical ones.
Implications for Trust and User Experience
LinkedIn has long positioned itself as a trusted space for professional engagement, career development, and business networking. As the platform begins to integrate AI more deeply into its operations, it will need to manage a complex relationship between innovation and trust. The use of member data to train AI systems is a step that, while technologically sound, may affect how users perceive the platform’s priorities and respect for individual agency.
Some users may welcome more intelligent features, especially if AI-generated insights genuinely help them find better jobs, make connections, or understand market trends. Others may feel uneasy about contributing to systems they have little control over, particularly when the systems benefit the company in ways that are not directly visible or valuable to the individual user.
Trust in digital platforms is built not just on compliance, but on clear communication, transparency of purpose, and respect for user boundaries. LinkedIn’s ability to maintain this trust will depend in part on how it engages with its user community during this transition. Listening to feedback, providing meaningful control options, and ensuring accountability for data use will be critical.
The Role of Regulators and Standards
The increasing reliance on user data for AI development highlights the urgent need for updated regulatory standards that address the unique challenges of generative AI. Many current data protection laws were not designed with modern AI architectures in mind and struggle to account for the nuances of data reuse, model training, and automated inference.
Regulators in the European Union, United Kingdom, and parts of Asia have already begun to issue guidance and inquiries into how AI models are trained, especially when public data is involved. In the United States, where privacy laws are less centralized, the conversation is more fragmented, though states like California and Colorado are pushing forward with their own frameworks.
A key challenge facing regulators is how to define fair use of publicly available data in the context of AI. Is data shared online by a professional simply “fair game” for any computational use? Or should there be stronger requirements for context-aware consent and ethical safeguards? These are questions that go beyond LinkedIn and speak to a broader need for global norms on data governance in the AI era.
What Users Can Do
For LinkedIn users who are concerned about their data being used to train AI systems, the most immediate step is to review the platform’s privacy settings and adjust participation in AI data sharing as desired. LinkedIn provides a setting under its data privacy controls that allows users to opt out of having their public data used in this way.
It is also wise for users to stay informed about privacy policy updates, especially on platforms where they share personal or professional information. Understanding how data is used, stored, and potentially repurposed is essential in the current digital environment.
More broadly, users can advocate for greater transparency and ethical standards in AI development by engaging in public discussions, supporting regulatory initiatives, and holding platforms accountable through feedback and usage choices.
Conclusion
LinkedIn’s decision to use user data by default to train its AI systems reflects a broader shift in how digital platforms approach innovation in the age of artificial intelligence. While the move aligns with industry trends and offers potential benefits in the form of enhanced features and services, it also raises important questions about privacy, consent, and trust.
As AI becomes more deeply embedded in the fabric of online life, companies will need to navigate these tensions carefully. For LinkedIn, success will depend not only on the capabilities of its AI models but on its ability to maintain the confidence of a user base that expects professionalism, respect, and transparency. In this evolving landscape, both platforms and users have a role to play in shaping the future of ethical AI development.