LinkedIn Is Using Your Data to Train AI: How to Opt Out

LinkedIn recently updated its privacy policy to allow the use of member data for training generative AI models—and unless you've already opted out, your professional profile, posts, and activity are likely part of that training dataset. While LinkedIn frames this as improving user experience, the reality is more complex: your career history, skills, connections, and professional insights are being fed into AI systems with limited transparency about how that data will ultimately be used or protected.

This isn't just a LinkedIn issue. It's part of a broader trend where tech platforms are retrofitting their privacy policies to justify using existing user data for AI development. The difference? LinkedIn contains some of your most sensitive professional information—the kind that could be used to replicate your expertise, mimic your writing style, or even create synthetic versions of your professional persona.

Here's what you need to know about LinkedIn's AI training practices and how to protect your data.

How AI Systems Collect and Use Your Data

LinkedIn's AI training operates differently than you might expect. The platform isn't just scraping your profile photo and job titles—it's analyzing the full spectrum of your professional digital footprint.

What LinkedIn collects for AI training:

• Profile content: Your work history, skills endorsements, recommendations, and "About" section provide rich training data for natural language models
• Posts and articles: Everything you publish, including comments and reactions, helps train AI to understand professional communication patterns
• Messages and interactions: While LinkedIn claims private messages aren't used, the metadata about your communication patterns (frequency, response times, connection strength) may be analyzed
• Engagement patterns: What content you view, how long you watch videos, which profiles you visit—all of this behavioral data trains recommendation algorithms
• Search queries: Your job searches, people searches, and content searches reveal intent and professional interests

The AI models being trained on this data serve multiple purposes. LinkedIn's generative AI features—like post suggestions, profile summaries, and job description generators—need examples of professional writing to function. But the implications extend beyond convenience features.

How this data trains AI models:

When you write a post about your industry expertise, that content becomes a training example. The AI learns vocabulary, sentence structure, argumentation patterns, and domain knowledge. When thousands of professionals in your field do the same, the AI develops a comprehensive understanding of that domain—potentially replicating the collective knowledge that once gave human experts their competitive advantage.

LinkedIn's parent company, Microsoft, has integrated LinkedIn data into its broader AI ecosystem. While the companies maintain some separation, Microsoft's Copilot AI and other enterprise tools benefit from insights derived from LinkedIn's professional network. Your data doesn't just stay on LinkedIn—it flows through a corporate AI infrastructure that spans multiple products and services.

The training process typically works through machine learning pipelines that aggregate, anonymize (to varying degrees), and process data at scale. Your individual profile might be anonymized, but the patterns, language, and professional insights you've shared become permanently embedded in the model's "knowledge." Even if you delete your account later, that training has already occurred—the AI has already learned from your data.

Where Your Data Ends Up in AI Training Pipelines

Understanding the journey of your LinkedIn data through AI systems reveals why opting out matters—and why it needs to happen sooner rather than later.

The Data Collection Phase

When you interact with LinkedIn, every action generates data points. These aren't stored as isolated events but as part of a comprehensive user graph that maps relationships, behaviors, and patterns. This graph becomes the raw material for AI training.

LinkedIn uses graph neural networks to understand the relationships between members, companies, skills, and content. Your connection to another professional, combined with shared skills and engagement patterns, helps the AI predict job matches, content recommendations, and professional opportunities. This isn't simple keyword matching—it's sophisticated pattern recognition that understands context and relationships.

The Training Environment

Your data moves from LinkedIn's production servers to specialized AI training environments. Here's where things get murky. LinkedIn's privacy policy grants broad permissions to use data "to develop, train, and improve our services." This language is intentionally vague—it doesn't specify which services, which AI models, or which third parties might access these training datasets.

Microsoft's AI research division, which collaborates closely with LinkedIn, has published papers using "large-scale professional network data" without explicitly naming LinkedIn. Academic researchers have also analyzed LinkedIn data patterns (often through official API access or partnerships) to study everything from career trajectories to hiring bias. While individual identities may be obscured, the aggregate insights derived from your professional data are widely distributed.

Third-Party AI Companies

The most concerning aspect of AI training pipelines is third-party access. LinkedIn's terms allow data sharing with "affiliates and third parties" for service improvement. While the company claims to limit this sharing, the definition of "affiliates" includes Microsoft's extensive network of subsidiaries and partners.

Additionally, AI companies have been caught scraping LinkedIn data without permission. In 2023, the FTC investigated multiple AI startups for harvesting LinkedIn profiles to train large language models. While LinkedIn pursues legal action against unauthorized scraping, the practical reality is that your public profile data has likely been captured by numerous AI training datasets—both authorized and unauthorized.

Data Persistence and Model Memory

Here's the critical issue: once your data trains an AI model, it's effectively permanent. Even if LinkedIn removes your data from future training runs, the models already trained retain the patterns and information learned from your profile. This is why opting out now matters—every day of delay means more of your data flowing into more AI systems.

Some AI models exhibit "memorization" where they can reproduce specific training examples. Researchers have demonstrated that large language models sometimes output verbatim text from their training data, including personal information. If your LinkedIn posts or profile content were part of that training set, fragments of your professional identity could resurface in AI-generated content used by others.

Step-by-Step: How to Opt Out or Remove Your Data

LinkedIn's opt-out process isn't prominently advertised, and the company has made it deliberately difficult to find. Here's the exact process to prevent your data from being used for AI training.

For Desktop Users

• Navigate to Settings: Click your profile icon in the top right corner, then select Settings & Privacy from the dropdown menu

• Access Data Privacy: In the left sidebar, click on Data Privacy

• Find the AI Training Option: Scroll down to the section labeled "Data for Generative AI Improvement" (LinkedIn has changed this wording several times, so it might also appear as "AI Features" or "Generative AI Training")

• Toggle Off: You'll see a toggle switch next to text explaining that LinkedIn uses your data to train AI models. Switch this to the OFF position

• Confirm Your Choice: LinkedIn may present a confirmation dialog explaining what you'll "miss out on" by opting out. Ignore the persuasive language and confirm your opt-out

• Verify the Change: Refresh the page and check that the toggle remains off. Some users have reported the setting reverting, so check back in a few days

Important note: As of early 2024, this opt-out option is not available to users in the European Union, European Economic Area, or Switzerland. Why? Because GDPR requires opt-in consent for this type of data processing—LinkedIn legally cannot use EU members' data for AI training without explicit permission. If you're in these regions, you're protected by default.

For Mobile Users

The mobile app process is less intuitive:

• Open Settings: Tap your profile photo, then tap Settings (gear icon)

• Navigate to Data Privacy: Tap Data Privacy in the Account section

• Scroll to AI Settings: Look for "Data for Generative AI Improvement" (this may be buried under "Other applications" or "Third-party services" depending on your app version)

• Disable the Option: Toggle off and confirm

What Opting Out Actually Does (and Doesn't Do)

Let's be clear about the limitations. Opting out prevents LinkedIn from using your data in future AI training runs. It does not:

• Remove your data from models already trained
• Prevent Microsoft from using aggregated, anonymized data
• Stop unauthorized third-party scraping of your public profile
• Remove your information from AI models trained by other companies that previously scraped LinkedIn

Opting out is a damage-control measure, not a complete solution. It's still essential, but it needs to be combined with other privacy strategies.

Additional Privacy Measures

Beyond the AI training opt-out, consider these LinkedIn privacy settings:

• Limit profile visibility: Settings > Visibility > Edit your public profile. Set this to "Your Name Only" if you want maximum privacy, though this defeats the networking purpose of LinkedIn
• Disable activity broadcasts: Turn off "Share profile updates with your network" and "Notify connections when you're in the news"
• Restrict data sharing: Under Data Privacy, review all third-party app permissions and revoke access to apps you don't actively use
• Control search engine indexing: You can prevent search engines from displaying your profile, though this significantly reduces your professional visibility

Requesting Complete Data Deletion

If you want to go further, you can request complete account deletion:

• Navigate to Settings & Privacy > Account preferences > Account management
• Click "Close account"
• Follow the verification process

LinkedIn retains some data even after account closure for "legal obligations and legitimate business purposes," which may include already-trained AI models. Under GDPR (for EU users) or CCPA (for California users), you can submit formal data deletion requests that impose stricter requirements on the company.

For CCPA requests, email privacy@linkedin.com with "California Privacy Rights Request" in the subject line. For GDPR requests, use the same email with "GDPR Data Deletion Request." Be specific about wanting all data removed from AI training datasets and request confirmation of deletion.

What the Law Says About AI and Your Personal Data

The legal landscape around AI training and personal data is evolving rapidly, with significant variation between jurisdictions. Understanding your rights requires looking at both existing privacy laws and emerging AI-specific regulations.

GDPR and AI Training

The General Data Protection Regulation (GDPR) provides the strongest protections for AI-related data use. Under GDPR, using personal data to train AI models requires:

• Lawful basis: Companies must identify a legal justification (consent, legitimate interest, contract necessity, etc.)
• Purpose limitation: Data collected for one purpose (professional networking) cannot be repurposed (AI training) without additional legal basis
• Transparency: Clear disclosure of AI training practices
• Right to object: Users must be able to opt out of AI training

LinkedIn's approach in the EU reflects these requirements—the company doesn't offer an "opt-out" because it cannot legally opt users in without explicit consent. This is why EU users don't see the AI training toggle; the practice isn't permitted under their default terms.

Article 22 of GDPR also grants the "right not to be subject to automated decision-making," which could extend to AI systems that make professional recommendations or hiring decisions based on your data. While enforcement is still developing, this provision could significantly limit how AI trained on LinkedIn data is deployed.

CCPA and State Privacy Laws

The California Consumer Privacy Act (CCPA) and its successor, the California Privacy Rights Act (CPRA), provide more limited protections:

• Right to know: You can request disclosure of what data is collected and how it's used
• Right to delete: You can request deletion of personal information (with exceptions for "legitimate business purposes")
• Right to opt out: You can opt out of "sale" of personal information, though AI training may not qualify as a "sale" under CCPA's definition

The CPRA, which took full effect in 2023, added provisions specifically relevant to AI. It created a new category of "sensitive personal information" and strengthened opt-out rights. However, professional information on LinkedIn may not qualify as "sensitive" under CPRA definitions, limiting these protections.

Other states have passed similar laws:

• Virginia Consumer Data Protection Act (VCDPA): Includes provisions on automated decision-making
• Colorado Privacy Act (CPA): Requires opt-in consent for certain automated processing
• Connecticut Data Privacy Act (CTDPA): Provides opt-out rights for profiling and targeted advertising

None of these state laws explicitly address AI training, creating legal ambiguity that companies like LinkedIn exploit. The laws focus on "sale" and "sharing" of data, but AI training occupies a gray area—the data isn't sold to third parties, but it's used to create commercial AI products.

Federal Proposals and AI-Specific Legislation

At the federal level, several proposals aim to regulate AI training:

The Algorithmic Accountability Act (proposed but not yet passed) would require companies to assess AI systems for bias, discrimination, and privacy risks. It would mandate impact assessments before deploying AI trained on personal data.

The AI Bill of Rights (White House blueprint, not binding law) articulates principles including:

• Notice when AI systems use your data
• Ability to opt out of automated systems
• Protection from algorithmic discrimination
• Access to human alternatives

While not legally enforceable, this blueprint influences regulatory approaches and may preview future legislation.

International Approaches

Canada's PIPEDA (Personal Information Protection and Electronic Documents Act) requires consent for data use beyond original collection purposes, which could restrict AI training. The proposed Consumer Privacy Protection Act would strengthen these requirements.

Brazil's LGPD (Lei Geral de Proteção de Dados) closely mirrors GDPR and provides similar protections against unconsented AI training.

China's Personal Information Protection Law (PIPL) requires explicit consent for automated decision-making and grants the right to refuse such processing.

The global trend is toward requiring affirmative consent for AI training, putting pressure on companies like LinkedIn to adopt more restrictive practices worldwide or maintain different policies by region.

Enforcement Reality

Despite these laws, enforcement remains limited. Privacy regulators are overwhelmed with complaints, and AI training represents a relatively new area of concern. The Irish Data Protection Commission (which oversees many U.S. tech companies' EU operations) has opened investigations into AI training practices, but formal enforcement actions are still rare.

Class action lawsuits may prove more effective. Several cases are pending against AI companies for training on copyrighted or personal data without permission. While these focus on creative works, the legal theories could extend to personal information on platforms like LinkedIn.

What's Coming Next in AI Privacy Regulation

The regulatory landscape for AI and data privacy is shifting rapidly. Understanding emerging trends helps you anticipate future protections—and current gaps that leave your data vulnerable.

The EU AI Act

The European Union's AI Act, finalized in late 2023 and being phased in through 2026, represents the world's first comprehensive AI regulation. Key provisions affecting personal data include:

• Risk-based classification: AI systems are categorized by risk level, with higher-risk systems facing stricter requirements
• Transparency obligations: AI systems must disclose when content is AI-generated and what data was used for training
• Data governance requirements: Training data must meet quality standards and respect privacy rights
• Fundamental rights impact assessments: High-risk AI requires evaluation of privacy and discrimination impacts

For LinkedIn, this means AI features deployed in the EU will face scrutiny about training data sources, model transparency, and user rights. The Act's extraterritorial reach could influence LinkedIn's global practices.

U.S. Federal AI Legislation

Multiple bills are working through Congress, though passage timelines remain uncertain:

The CREATE AI Act focuses on establishing standards and testing frameworks for AI systems. While not directly addressing data privacy, it would create infrastructure for evaluating AI training practices.

The Algorithmic Justice and Online Platform Transparency Act would require platforms to disclose AI training datasets and allow independent audits—directly relevant to LinkedIn's practices.

The American Data Privacy and Protection Act (ADPPA), if passed, would create the first federal privacy law in the U.S. Current drafts include provisions on automated decision-making that could restrict AI training on personal data without consent.

State-Level Innovation

States continue to lead on AI regulation:

California is considering amendments to CPRA that would explicitly address AI training, potentially requiring opt-in consent rather than opt-out.

New York has proposed the Automated Decision-Making Accountability Act, requiring businesses to conduct bias audits of AI systems and disclose training data sources.

Illinois is exploring legislation that would classify AI-generated content and require labeling when AI systems are trained on Illinois residents' data.

This patchwork approach creates compliance challenges for platforms like LinkedIn, potentially pressuring them toward more privacy-protective default settings.

Industry Self-Regulation Efforts

Tech companies are attempting to preempt regulation through voluntary frameworks:

The Partnership on AI has developed principles for responsible AI development, including data minimization and user control. LinkedIn is a member, though compliance is voluntary and unenforceable.

Data provenance standards are emerging to track how training data is collected and used. Technologies like cryptographic watermarking could allow users to trace whether their data appears in AI training sets—though adoption remains limited.

Some AI companies are exploring synthetic data alternatives that reduce reliance on personal information. If these approaches prove viable, pressure may increase on platforms like LinkedIn to adopt them.

The "Right to Reasonable Inferences"

An emerging legal concept could transform AI privacy: the right to reasonable inferences. This principle, discussed in academic and policy circles, would grant individuals control over inferences AI systems draw from their data—not just the data itself.

For LinkedIn, this could mean users could challenge AI conclusions about their skills, career trajectory, or professional value. If this right gains legal recognition, it would fundamentally alter how AI training and deployment work.

Technical Solutions on the Horizon

Beyond regulation, technical approaches may enhance user control:

Federated learning allows AI training without centralizing data, potentially giving users more control over their information. While LinkedIn hasn't adopted this