LinkedIn

Summary of Important Changes

Overall change

The diff appears to add or substantially restate LinkedIn’s global Privacy Policy, including detailed sections on data collection, use, sharing, retention, user rights, and international transfers. Much of the text is structural or explanatory, but several provisions create important new or clarified risks for customers and Members.

AI model training and use of data

The most significant change is the express statement that LinkedIn may use personal data to:

• “Develop and train artificial intelligence (AI) models”;
• Improve, develop, provide, and personalize LinkedIn products and services;
• Generate insights using AI, automated systems, and inferences.

This is broad language. It does not clearly limit training to anonymized, aggregated, publicly available, or user-consented data. Based on the policy, potentially relevant data may include:

• Profile information, posts, uploaded content, resumes, applications, and survey responses;
• Usage data, searches, viewed content, learning activity, advertising interactions, and social actions;
• Communications and message-related data, particularly where automated tools are enabled;
• Data received from contacts, employers, schools, partners, Microsoft, and other affiliates;
• Inferences about characteristics such as age, gender, interests, seniority, compensation, and skills.

The policy also states that LinkedIn may use personal data and publicly shared content across affiliates, including Microsoft, to provide or develop their services. This may expand the practical use of customer data beyond LinkedIn’s core platform.

Communications and generative AI tools

LinkedIn may scan messages, subject to user settings, to provide AI or automated tools such as:

• Drafting responses;
• Summarizing messages;
• Scheduling meetings;
• Recommending next steps;
• Suggesting possible responses.

This creates confidentiality and privilege risks for sensitive business, employment, legal, or client communications. Users should not assume that LinkedIn messages are private from automated processing.

Broader data collection and profiling

The policy expressly permits collection of data from:

• Other users’ contact uploads, calendars, business cards, and email headers;
• Employers, schools, applicant-tracking systems, and business partners;
• Microsoft and affiliated services;
• Websites and services using LinkedIn cookies, plugins, ads, and tags.

LinkedIn may combine this information across services and create inferences used for recommendations, advertising, employment-related matching, and personalization.

Sharing and retention risks

• Profiles, public posts, social actions, and other content may be visible to Members, customers, Visitors, search engines, and third-party services.
• Premium customers may export limited profile information and store additional information such as resumes or sales history.
• Data is generally retained while an account remains open, including generated or inferred data.
• After account closure, copied content, messages, group content, and third-party search results may remain available.

Practical takeaway

The key change is an explicit and potentially extensive authorization to use personal data—including activity, content, communications, and inferences—for AI development and training, with limited detail about model safeguards, deletion from training datasets, opt-out rights, or restrictions on affiliate use.

Executive Summary

The diff appears to show the same substantive Privacy Policy text on both sides, with no clear change to the operative data-use provisions. Most apparent differences are formatting, punctuation, or presentation artifacts rather than legal changes.

AI Training and Use of Customer Data

Both versions state that LinkedIn may use personal data to:

• Improve, develop, and provide products and services;
• Develop and train artificial intelligence (AI) models;
• Develop, provide, and personalize services; and
• Generate insights using AI, automated systems, and inferences.

This language is broad and is not limited to anonymized, aggregated, or publicly available data. It may therefore encompass information such as:

• Profile and employment information;
• Posts, comments, messages, and other uploaded content;
• Usage and engagement data;
• Inferences about users, including age, gender, interests, skills, and compensation;
• Data obtained from contacts, calendars, partners, employers, Microsoft, and other services.

The policy also states that LinkedIn may scan messages, where settings permit, to provide AI-assisted functions such as drafting responses, summarizing messages, recommending next steps, and scheduling meetings.

Related Data-Sharing Risks

The policy permits LinkedIn to share or use data across its affiliates, including Microsoft, for service development and advertising purposes. It also permits the use of publicly shared LinkedIn content to provide or develop affiliate services. This may increase the number of entities and systems that can process customer data, including for AI-related development.

LinkedIn retains data generated or inferred from use of the Services while an account remains open and may retain certain information after account closure in depersonalized or aggregated form. The policy does not expressly state that AI training data, model inputs, or derived model outputs will be deleted when a user exercises deletion rights.

Other Notable Risks

• Continued use after notice of a material policy change constitutes acceptance of the updated policy; the stated remedy is account closure.
• Personal data may be obtained from other users, employers, partners, Microsoft services, calendars, and third-party websites.
• Data may be transferred internationally to jurisdictions with weaker protections.
• Public posts and social actions may be widely visible and copied outside LinkedIn.
• Employers and enterprise customers may access certain work-related usage and profile information.

Conclusion

No material new AI-training authorization is identifiable as having been added by this diff. However, the policy—as shown in both versions—contains a significant broad authorization to use personal data for AI model development and training, without an express customer-data exclusion, deletion commitment, or clear limitation to de-identified data.

Summary of Important Changes

Overall Scope and Structure

• The revised text adds a substantially expanded global Privacy Policy covering Members and Visitors worldwide.
• It expressly covers LinkedIn websites, apps, communications, plugins, advertising services, “Apply with LinkedIn,” “Share with LinkedIn,” and other off-site services.
• It identifies different data controllers depending on location:
• LinkedIn Ireland for the EU, EEA, and Switzerland.
• LinkedIn Corporation elsewhere.
• LinkedIn reserves the right to update the policy. Continued use after the effective date constitutes acceptance of the updated data practices; users who object may close their accounts.

New or Expanded Data Collection

The policy expressly describes collection of:

• Profile, employment, education, skills, salary, demographic, resume, job-application, payment, and billing information.
• Calendar, contact, business-card, email-header, and connection data supplied by the user or others.
• Usage, search, viewing, clicking, device, IP address, cookie, advertising, and cross-device tracking data.
• Precise location data where the user opts in.
• Communications and message-related metadata, including who users communicate with and when.
• Data from employers, schools, customers, partners, Microsoft, and other affiliated services.

A significant risk is that information about a person may be collected from other users, employers, partners, public sources, or connected services, even when the person did not directly provide it.

AI and Model-Training Changes

The most important substantive addition is express authorization to:

• Use personal data to “develop and train artificial intelligence (AI) models.”
• Use data to develop, provide, personalize, and improve LinkedIn products and services.
• Generate insights and inferences using AI and automated systems.
• Make inferences about characteristics such as age, gender, interests, industry, seniority, and compensation range.
• Use communications for automated tools, including suggested replies, message drafting, summaries, meeting scheduling, and recommended next steps, where settings permit.

The policy does not clearly limit AI training to anonymized, aggregated, public, or user-consented data. It also does not specify:

• Which categories of data may be used for training;
• Whether private messages, resumes, job applications, calendar data, or inferred data may be included;
• How long training data or model outputs are retained;
• Whether users can delete their data from already-trained models;
• Whether users can opt out specifically from AI model training.

Accordingly, users should assume that data collected or generated through LinkedIn may be used for broad AI development purposes unless a separate setting, regional notice, or linked AI policy provides a narrower restriction.

Expanded Sharing and Commercial Uses

• LinkedIn may share data with Microsoft and other affiliates to provide or develop services and, subject to regional limitations, improve or develop advertising services.
• Public LinkedIn content may be shared with affiliates to develop their services.
• Data may be combined across LinkedIn and affiliated services.
• Advertisers and partners may receive hashed identifiers or device identifiers and may link them with data collected directly from users.
• Public profiles and content may be visible to Members, customers, Visitors, search engines, and third-party services.

Retention and User Control

• LinkedIn may retain user-provided, collected, generated, and inferred data while an account remains open.
• Some data remains after account closure for legal, security, fraud-prevention, dispute, and enforcement purposes.
• Content copied or shared by others may remain available outside LinkedIn’s control.
• Users receive expanded rights to access, correct, delete, restrict, object to, or export personal data, subject to applicable law.

Summary of Important Changes

Overall assessment

The diff appears to contain very few substantive changes. Most of the text is duplicated without apparent modification. The identified changes are primarily formatting, punctuation, or link-text changes rather than changes to legal rights or obligations.

AI model training and use of customer data

No apparent change

The provision stating that LinkedIn may use personal data to:

• “improve, develop, and provide” products and services;
• develop and train artificial intelligence (AI) models;
• personalize services; and
• generate insights using AI, automated systems, and inferences,

appears in both the deleted and added versions.

Accordingly, the diff does not appear to introduce or remove LinkedIn’s ability to use customer data for AI model development or training.

Existing risk

Although not newly added by this diff, the clause is broad. It does not clearly limit:

• the categories of personal data used for training;
• whether data is anonymized or pseudonymized before training;
• whether customer content, messages, profile data, behavioral data, or inferred data may be used;
• whether trained models may be retained after account closure; or
• whether customers can opt out specifically from AI training.

The policy also refers to separate materials concerning LinkedIn’s “approach to generative AI” and Responsible AI principles. Those linked materials may contain important limitations or additional permissions and should be reviewed separately.

Other apparent changes

Mission statement typography

• “LinkedIn's mission” appears changed to “LinkedIn’s mission.”
• This is only an apostrophe/typographical change and has no apparent legal effect.

Link and table-of-contents formatting

• “Learn More.” appears changed to “.”
• “ContentsData” appears adjusted to “Contents.”
• These appear to be formatting or hyperlink-rendering corrections. However, if the removal of “Learn More” makes a privacy-control or explanatory link inaccessible, users could lose convenient access to important disclosures or settings.

Opening regional-policy language

The beginning of the diff is truncated or malformed. It appears to concern the relationship between the global Privacy Policy and LinkedIn’s European Regional Privacy Notice. No clear substantive change can be reliably identified from the excerpt.

Key conclusion

No new AI-training permission is apparent in this diff. The principal AI-related risk remains the breadth and lack of detail of the existing authorization, rather than a change introduced here.

Summary

Key Limitation

The provided diff contains only the statement:

> “Added approximately 6625 words to the document”

It does not include the actual added, deleted, or replaced legal language. As a result, it is not possible to reliably identify:

• Changes to rights or obligations
• New fees, liabilities, warranties, or indemnities
• Changes to termination, governing law, or dispute provisions
• Modifications to confidentiality or data-security obligations
• Any provisions concerning the use of customer data to train AI models

AI-Training and Data-Use Analysis

No conclusions can be drawn about AI-model training from the information provided. The added language might address AI training, but the diff does not reveal whether it:

• Permits the provider to use customer data, prompts, outputs, or usage data to train models
• Applies only to de-identified, aggregated, or anonymized data
• Allows human review or sharing with affiliates and subcontractors
• Gives the customer an opt-out or requires affirmative consent
• Applies to customer content submitted before or after the amendment
• Allows model training after termination or deletion
• Grants the provider ownership or broad licensing rights over customer data or outputs
• Excludes confidential information, personal data, regulated data, or customer-provided materials

Risk Assessment

The only certain fact is that approximately 6,625 words were added. The volume of additions may indicate substantial changes, but their legal effect cannot be assessed without the text.

Information Needed

Please provide the full marked-up diff, or at least the newly added and revised clauses. In particular, include provisions containing terms such as:

• “train,” “training,” “machine learning,” or “artificial intelligence”
• “customer data,” “customer content,” “inputs,” or “outputs”
• “improve,” “develop,” or “fine-tune”
• “aggregate,” “de-identify,” or “anonymize”
• “license,” “ownership,” or “usage rights”
• “retain,” “delete,” or “after termination”

Summary of the Diff

Scope of Change

• The diff states that approximately 6,625 words were removed from the document.
• No added or replacement language is shown.
• Because the actual deleted text is not provided, it is not possible to determine precisely which rights, obligations, limitations, or protections were removed.

Customer Data and AI Training

• The available diff does not identify any specific changes concerning:
• Whether customer data may be used to train, fine-tune, or improve AI models;
• Whether prompts, inputs, outputs, or usage data may be retained;
• Whether customer data may be anonymized, aggregated, or shared with affiliates or vendors;
• Whether the provider may use customer data for product development or analytics;
• Whether the customer can opt out of AI training; or
• Whether data is deleted after termination.

• However, if the deleted 6,625 words included data-use or AI provisions, their removal could materially change the parties’ rights. For example, deletion of a restriction could allow broader use of customer data, while deletion of a permission could prohibit or limit model training.

Potential Legal and Commercial Risks

The deletion may have removed important terms relating to:

• Confidentiality and permitted uses of customer information;
• Data ownership and licensing rights;
• Restrictions on model training or service improvement;
• Data security, privacy, and regulatory compliance;
• Data retention, deletion, and return obligations;
• Use of subcontractors or third-party AI providers;
• Intellectual-property ownership and indemnities;
• Liability caps, warranties, and disclaimers;
• Audit, suspension, and termination rights; and
• Post-termination obligations.

A deletion-only revision can also create ambiguity if remaining provisions refer to terms that no longer exist.

Recommended Review

The actual deleted language should be obtained and reviewed before approval. Particular attention should be given to any deleted provisions containing terms such as:

• “Customer Data”
• “Input” or “Output”
• “train,” “fine-tune,” “improve,” or “develop”
• “machine learning” or “artificial intelligence”
• “retain,” “delete,” “anonymize,” or “aggregate”
• “service improvement”
• “confidential information”
• “license” or “ownership”

Bottom line: The diff confirms a substantial deletion but does not provide enough information to determine whether customer data may now be used to train AI models. The missing text is essential to assess the change and any resulting risk.

Summary of Important Changes

1. New express permission to use customer data for AI training

The most significant change is the addition to “How We Use Your Data” stating that LinkedIn may use personal data to:

• “develop and train artificial intelligence (AI) models”;
• improve, develop, provide, and personalize LinkedIn’s products and services; and
• generate insights using AI, automated systems, and inferences.

Risks and implications

• This is an express expansion of permitted data use beyond ordinary service delivery, personalization, analytics, and research.
• The wording is broad and does not specify:
• which categories of customer data may be used;
• whether data will be anonymized or pseudonymized before training;
• whether data will be used to train general-purpose or generative AI models;
• how long training data or model-derived information will be retained;
• whether data will be shared with external AI providers; or
• whether customers can opt out of AI training.
• The policy does not clearly limit AI training to data voluntarily submitted for that purpose. The reference to “personal data” could encompass profile information, activity, communications, inferred characteristics, and other collected data.
• Deleting an account may not remove data already incorporated into trained models, particularly because the retention provisions permit continued retention of certain data and depersonalized information.

2. AI-based inferences and automated decision-making

The revised language also expressly permits LinkedIn to use AI, automated systems, and inferences, including inferences about:

• age;
• gender;
• professional interests and skills;
• industry, seniority, and compensation; and
• job and advertising preferences.

These systems may affect content rankings, job recommendations, advertising, networking suggestions, and other professional opportunities. This creates risks of inaccurate or discriminatory inferences, especially where data is used in employment or recruiting contexts.

3. Broader sharing with Microsoft and affiliates

The policy states that LinkedIn may share:

• publicly shared content with affiliates, including Microsoft, to provide or develop their services; and
• personal data with affiliates, including Microsoft, to improve, provide, or develop advertising services.

This creates a potential cross-service and cross-company data-use risk. The policy also permits combining information across LinkedIn services and affiliated entities, subject to applicable regional notices.

4. Data sources and monitoring remain broad

The policy continues to cover extensive data, including:

• profile and employment information;
• calendar and contact data supplied by users or others;
• messages and communications;
• browsing, device, cookie, and off-site activity;
• data from employers, schools, partners, and Microsoft services; and
• inferred information.

Automated systems may scan messages for scheduling, drafting, summarization, recommendations, moderation, and security purposes, subject to settings.

5. User controls and legal considerations

The policy references settings, consent, objections, and regional rights, but it does not provide a specific AI-training opt-out in the added language. Customers should verify whether separate settings or regional notices provide such a control. Continued use after notice constitutes acceptance of the updated policy, while the stated remedy for objection is account closure.

Summary of Available Change

The diff only states that approximately 6,625 words were removed from the document. It does not identify which clauses, definitions, or sections were deleted.

Legal and Commercial Risks

Because the deleted text is not provided, it is not possible to determine:

• Which contractual rights or obligations were removed;
• Whether liability limits, indemnities, warranties, confidentiality provisions, or termination rights changed;
• Whether customer data protections were weakened;
• Whether audit, security, compliance, or breach-notification obligations were deleted;
• Whether governing law, dispute-resolution, or other material terms were affected.

A deletion of this size may materially alter the agreement, particularly if it removes incorporated policies, schedules, definitions, or data-processing provisions. The absence of replacement language could also create ambiguity or leave important issues unaddressed.

AI Training and Customer Data

The available diff contains no identifiable language concerning AI models or the use of customer data for training. Accordingly, it is not possible to determine whether the revised agreement:

• Permits or prohibits using customer data to train, fine-tune, or improve AI models;
• Limits use of customer data to providing the contracted services;
• Allows use of prompts, inputs, outputs, usage data, or derived data for model development;
• Requires anonymization, aggregation, or de-identification before AI-related use;
• Gives the customer an opt-out or consent right;
• Allows subcontractors or affiliates to use customer data for AI training;
• Addresses retention or deletion of data used in training; or
• Allocates ownership or confidentiality rights in AI-generated outputs.

Required Information

A reliable legal analysis requires the actual deleted text, or a redline showing the specific deletions and replacements. The document should also be checked for changes to any referenced privacy policy, data-processing agreement, acceptable-use policy, or AI-specific terms.

Summary

The provided diff does not include the text of the added language. It only states:

> “Added approximately 6,625 words to the document”

Accordingly, it is not possible to identify the legal or commercial impact of the changes, including whether the customer’s data may be used to train AI models.

AI Training and Data-Use Review

No conclusions can be drawn about whether the new language:

• Permits the provider to use customer data, prompts, inputs, outputs, or personal information to train, fine-tune, test, or improve AI models;
• Applies such rights to identifiable data, aggregated data, de-identified data, or metadata;
• Allows data to be shared with affiliates, subprocessors, or third-party model providers;
• Makes AI training optional, opt-in, opt-out, or automatic;
• Limits use of data to the term of the agreement or permits continued use after termination;
• Grants the customer ownership or control over inputs and outputs;
• Requires deletion, return, or certification of deletion of customer data;
• Imposes confidentiality, security, privacy, or data-protection obligations; or
• Provides indemnities, liability limits, or remedies for unauthorized AI training or disclosure.

Risk Assessment

Because the actual additions are missing, no reliable assessment can be made regarding:

• Expansion of the provider’s license to customer data;
• Changes to confidentiality or privacy obligations;
• New data-retention or cross-border-transfer rights;
• New restrictions on the customer’s use of AI-generated outputs;
• Changes to warranties, indemnities, audit rights, or liability caps; or
• Conflicts with existing data-processing agreements or applicable privacy laws.

Information Needed

Please provide the actual 6,625 words of added text, preferably with the surrounding existing provisions or a complete redline. The AI-related provisions should be reviewed particularly closely for terms such as “train,” “improve,” “develop,” “fine-tune,” “machine learning,” “artificial intelligence,” “aggregate,” “de-identify,” “usage data,” “inputs,” “outputs,” “service data,” and “model.”

Structured Summary of Important Changes

Executive Summary

The diff is difficult to parse because most of the policy appears duplicated on both sides. Based on the identifiable text, the most important issue is LinkedIn’s express authorization to use customer data in connection with artificial intelligence. However, the AI language appears in both the deleted and added versions, so it may not represent a substantive change in this particular diff.

AI Model Training and Use

The policy states that LinkedIn may use personal data to:

• “develop and train artificial intelligence (AI) models”;
• improve, develop, provide, and personalize LinkedIn products and services; and
• generate insights using AI, automated systems, and inferences.

This is broad language. It does not clearly limit AI training to anonymized, aggregated, publicly available, or non-sensitive data. Potentially relevant data may include:

• profile information and professional history;
• posts, comments, videos, and other uploaded content;
• usage and engagement data;
• communications, where applicable;
• inferred characteristics and interests, including age, gender, seniority, compensation bracket, and professional interests; and
• data received from employers, partners, Microsoft, and other sources.

The policy also refers users to separate materials on LinkedIn’s “Responsible AI principles” and its approach to generative AI. Those materials may contain important additional terms or controls.

Sharing with Affiliates and Microsoft

LinkedIn may share data with affiliated companies, including Microsoft, to provide and develop services. The policy specifically permits sharing:

1. publicly shared LinkedIn content to provide or develop affiliates’ services; and

2. personal data to improve, provide, or develop affiliates’ advertising services, subject to the European Regional Privacy Notice.

This creates a risk that public posts and other visible content may be reused beyond LinkedIn’s core platform, including for product development or AI-related purposes. The policy does not clearly state whether users can opt out of such development uses.

Data Scope and Inferences

LinkedIn may combine information from its services and make inferences about users. These inferences may be used for personalization, advertising, recommendations, and potentially AI development. Inferred data can be inaccurate or sensitive, and the policy does not provide detailed limits on automated decision-making or model retention.

Retention and Deletion Risks

LinkedIn generally retains personal data while an account remains open, including data generated or inferred from use. Closing an account does not necessarily remove:

• data retained for legal, security, fraud-prevention, or dispute purposes;
• de-personalized information; or
• content copied or shared by other users.

The policy does not expressly explain whether information already incorporated into trained AI models can be removed or unlearned after deletion or objection.

Overall Assessment

The clearest legal risk is the broad, purpose-expanding authorization for AI training and affiliate use of personal data. Nevertheless, because the AI wording appears on both sides of the supplied diff, it should be verified against a clean version-controlled comparison before treating it as a newly introduced contractual right.

Summary

The diff only states: “Added approximately 6,625 words to the document.” It does not include the actual added language or identify which provisions changed.

What can be determined

• The document has been substantially expanded.
• The scope and legal effect of the additions cannot be assessed from the word-count notice alone.
• It is not possible to determine whether the additions modify:
• Customer data ownership or licensing rights
• Permitted uses of customer data
• Confidentiality or security obligations
• Data retention or deletion
• Subprocessors or third-party sharing
• Liability, indemnification, or audit rights
• Governing law or dispute procedures
• Service levels, fees, termination, or renewal terms

AI-training provisions

The supplied diff contains no substantive language concerning artificial intelligence or machine learning. Accordingly, it is not possible to determine whether the revised document:

• Permits using customer data, prompts, inputs, outputs, or usage data to train or improve AI models
• Limits training to de-identified, aggregated, or non-personal data
• Requires customer consent or provides an opt-out
• Allows data to be shared with AI providers or subprocessors
• Grants the provider ownership or broad usage rights over AI outputs
• Imposes restrictions on using customer data to evaluate, fine-tune, or develop models
• Provides deletion, isolation, or non-retention commitments for AI systems

Required for substantive review

Please provide the full redline or the actual inserted text. A word-count statement alone is insufficient to identify legal changes or risks, particularly concerning customer-data use for AI training.

Executive Summary

The diff appears to contain very few substantive changes. Most of the policy text is repeated without marked changes. The principal changes are editorial or formatting-related, and there is no apparent change to the AI-training language.

Important Changes

1. Possible removal of introductory wording

The opening text appears to delete:

> “Privacy Policy LinkedIn's global Privacy Policy below applies worldwide. If you're located in the…”

The replacement begins with:

> “…will apply to you in our European Regional Privacy Notice.”

Because the diff is incomplete or unusually formatted, it is unclear whether this removes only a heading and introductory sentence or changes the explanation of which privacy policy applies by location.

Potential risk: Readers may receive less clarity about the relationship between LinkedIn’s global Privacy Policy and its European Regional Privacy Notice. This could affect understanding of which regional rights, obligations, and processing rules apply.

2. Minor wording and formatting edits

The diff includes:

• “LinkedIn's” changed to “LinkedIn’s” (typographic apostrophe).
• “Learn More.” changed to “.”
• “Table of ContentsData” changed to “Table of Contents.”
• Apparent removal or correction of the “Privacy Policy” heading.

These changes do not appear to alter substantive privacy rights or obligations, although the “Table of Contents” correction may improve readability.

AI Model Training and Use of Customer Data

No apparent change in the diff

The policy continues to state that LinkedIn may use personal data to:

• Improve, develop, and provide products and services;
• Develop and train artificial intelligence (AI) models;
• Personalize services;
• Generate insights using AI, automated systems, and inferences.

The text also continues to refer to LinkedIn’s Responsible AI principles and its approach to generative AI.

Continuing risks

Although unchanged, this language is significant because it is broad:

• It does not clearly limit training to anonymized, aggregated, or publicly available data.
• It appears to cover personal data, user activity, communications, profile information, and inferred information.
• The policy does not specify which AI models may be trained, whether data is used for internal or commercial models, how long training data is retained, or whether trained models can be deleted or corrected if the underlying data changes.
• Any available opt-out or objection rights are not stated directly in this excerpt and may depend on settings, regional notices, or linked policies.

Overall Assessment

The marked changes appear primarily editorial. The principal substantive issue remains the existing broad authorization to use personal data for AI development and training, rather than a newly introduced change in this diff.

Summary

The supplied diff does not include the actual amended contract language. It only states:

> “Added approximately 6625 words to the document”

Accordingly, it is not possible to identify:

• Which provisions were added, deleted, or replaced;
• New contractual obligations, liabilities, fees, or termination rights;
• Changes to confidentiality, data ownership, privacy, security, or compliance terms;
• Whether customer data may be used to train, fine-tune, evaluate, or improve AI models;
• Whether such use is permitted by default, requires consent, or is subject to opt-out rights;
• Whether customer data is anonymized, aggregated, retained, shared with vendors, or transferred internationally; or
• Any indemnity, limitation-of-liability, or warranty provisions associated with AI or data use.

AI-training review required

The actual added language should be reviewed specifically for terms such as:

• “train,” “fine-tune,” “improve,” “develop,” or “evaluate” models;
• “customer data,” “content,” “inputs,” “outputs,” or “usage data”;
• rights to use data on an aggregated, de-identified, or pseudonymized basis;
• provider rights to share data with affiliates, subprocessors, or third-party model providers;
• retention and deletion periods;
• customer opt-out or consent mechanisms;
• restrictions on using confidential, personal, regulated, or sensitive information; and
• allocation of responsibility for model outputs, data breaches, and regulatory compliance.

Important limitation

A word-count notice is not a substantive redline and does not permit a reliable legal-risk analysis. Please provide the full redline or the text of the 6,625 added words, including the surrounding unchanged provisions where necessary.

Summary of Available Change

The diff only states that approximately 6,625 words were removed. It does not identify which clauses, definitions, or sections were deleted.

Key Legal Risks

Because the deleted text is not provided, it is not possible to determine whether the removals:

• Eliminate customer protections, warranties, or liability limitations;
• Change confidentiality, security, privacy, or data-retention obligations;
• Remove audit, notice, indemnification, or termination rights;
• Alter ownership or licensing rights in customer data, outputs, or intellectual property;
• Remove restrictions on subcontractors or other third-party data processors;
• Affect compliance obligations under applicable privacy or AI laws; or
• Create inconsistencies with provisions that remain in the agreement.

A deletion of this size may materially change the parties’ rights and obligations, even if no replacement language is shown.

AI Training and Customer Data

The available diff contains no substantive language identifying whether customer data may be used to train, fine-tune, evaluate, or improve AI models.

Accordingly, it is not possible to determine whether the changes:

• Permit or prohibit training on customer data;
• Distinguish between customer inputs, outputs, prompts, feedback, or usage data;
• Apply different rules to personal data, confidential information, or de-identified data;
• Require consent or provide an opt-out;
• Allow data to be shared with model providers or affiliates;
• Require deletion of data from training datasets or model-development systems; or
• Allocate responsibility for model memorization, disclosure, or use of customer data.

Recommended Review

The complete redline or the text of the deleted provisions is necessary for a reliable analysis. Particular attention should be given to deleted sections concerning:

1. Data use and licensing

2. AI training, model improvement, and analytics

3. Confidentiality and privacy

4. Security and data retention

5. Intellectual property and ownership

6. Third-party providers

7. Indemnities, warranties, and liability

8. Termination and deletion obligations

Until the deleted text is reviewed, the principal risk is that material customer protections—or restrictions on use of customer data for AI training—may have been removed without being identifiable from the abbreviated diff.

Summary of Important Changes

1. Expanded use of customer data to train AI models

The revised policy expressly permits LinkedIn to use personal data to:

• “develop and train artificial intelligence (AI) models”;
• improve, develop, provide, and personalize LinkedIn products and services; and
• generate insights using AI, automated systems, and inferences.

This is a significant clarification and expansion because the prior wording generally referred to service development, personalization, research, and analytics without expressly authorizing AI-model training.

Key risks

• Broad data scope: The policy does not clearly limit AI training to anonymized, aggregated, public, or non-sensitive data. It may therefore cover profile information, activity, content, communications, inferred characteristics, job-related data, and other personal data collected from third parties.
• Potential use of sensitive information: The policy recognizes that users may post sensitive information, but the AI-training authorization does not expressly exclude such information.
• Limited control over trained models: The policy does not explain whether deleting data or closing an account will remove it from training datasets or retrain models.
• No clear opt-out: The excerpt does not provide a specific opt-out mechanism for AI training. General privacy settings, objection rights, or consent withdrawal may not necessarily stop training-related processing.
• Ambiguous purposes and recipients: The policy does not specify which AI models are covered, how long training data is retained, or whether models may be shared with or used by Microsoft, affiliates, customers, or third parties.

LinkedIn refers users to its Responsible AI principles and generative-AI information, but those linked materials should be reviewed because they may contain important limitations or additional permissions.

2. Broader sharing with Microsoft and other affiliates

The revised policy states that LinkedIn may share with affiliates, including Microsoft:

1. publicly shared content to provide or develop their services; and

2. personal data to improve, provide, or develop their advertising services.

This creates a broader cross-company use of customer data, particularly for advertising and product development. It may also permit use of LinkedIn content and data in Microsoft-related services, subject to the European Regional Privacy Notice where applicable.

3. Expanded automated processing and inferences

The policy expressly describes the use of AI, automated systems, and inferences concerning characteristics such as age, gender, interests, seniority, industry, and compensation. These inferences may affect recommendations, advertising, recruiting, and personalization, creating accuracy, discrimination, and transparency risks.

4. Other notable changes

• The policy is reorganized and substantially expanded, adding detailed descriptions of data collection, sharing, retention, advertising, enterprise access, and user rights.
• LinkedIn may retain data generated or inferred from account activity while the account remains open.
• Public content, social actions, and profile information may remain visible or be copied after account closure.
• Changes become binding through continued use after notice, with account closure presented as the primary response to objection.

Executive Summary

The diff appears largely duplicative and does not show clear substantive changes to the operative privacy terms. Most provisions appear unchanged, including LinkedIn’s data-collection, sharing, retention, advertising, account-closure, and user-rights provisions.

Notable Changes

1. Introductory formatting and wording

• The heading appears to change from “Privacy Policy” to text beginning with LinkedIn’s worldwide-policy statement.
• The opening language concerning the European Regional Privacy Notice appears truncated or reformatted in the diff.
• “Learn More.” is changed to “.”, which appears to be a formatting or hyperlink-markup change rather than a substantive legal change.
• “LinkedIn's” is changed to the typographically possessive “LinkedIn’s.”
• The table-of-contents text appears to be reformatted.

These changes should be checked against the rendered policy because the diff formatting may have obscured whether any introductory sentence was removed.

AI-Training and AI-Related Data Use

The policy expressly states that LinkedIn may use personal data to:

• Improve, develop, and provide products and services;
• Develop and train artificial-intelligence (“AI”) models;
• Develop, provide, and personalize services;
• Generate insights using AI, automated systems, and inferences.

It also links to LinkedIn’s Responsible AI principles and generative-AI approach and states that LinkedIn may make inferences, including regarding age and gender.

Change assessment

The AI-training language appears in both the deleted and added text shown in the diff. Accordingly, there is no clearly identifiable change to LinkedIn’s authority to use customer or member data for AI-model development or training.

Nevertheless, the retained language creates significant ongoing risk:

• It does not clearly limit training to de-identified, aggregated, or public data.
• It does not specify which categories of data may be used, potentially including profile data, activity, communications, inferred information, and other personal data.
• It does not clearly distinguish between training LinkedIn’s internal models and models developed by affiliates, including Microsoft.
• The policy does not state a specific retention period for data incorporated into trained models or explain whether deletion or objection will remove data from existing models.
• AI-generated inferences may affect personalization, advertising, recommendations, and career-related opportunities.

Other Continuing Risks

• Broad sharing with affiliates, service providers, enterprise customers, advertisers, and linked third-party services.
• Extensive tracking across LinkedIn and third-party websites, particularly outside designated European countries.
• Continued retention of personal data and inferred data while an account remains open.
• Public visibility and continued availability of content copied or exported by others.
• Policy changes may become binding through continued use after notice.

Overall: No definite substantive amendment is apparent from the supplied diff, but the policy continues to authorize broad AI training and related data uses without precise limits or deletion guarantees.

Summary of Important Changes

1. Express authorization to train AI models

The revised policy expressly states that LinkedIn may use personal data to:

• “develop and train artificial intelligence (AI) models”;
• improve, develop, provide, and personalize LinkedIn products and services; and
• generate insights using AI, automated systems, and inferences.

This is a significant expansion or clarification of permitted data uses. The policy does not limit AI training to anonymized, aggregated, publicly available, or non-sensitive data. It also does not clearly identify which specific data categories may be used for training.

2. Broad categories of data potentially used for AI

The policy describes LinkedIn’s data holdings broadly, including:

• profile information, employment history, skills, and education;
• posts, comments, videos, messages, and social actions;
• job applications, resumes, and salary or demographic information;
• calendar, contact, and email-header information;
• browsing, device, location, advertising, and engagement data;
• data received from employers, schools, partners, Microsoft, and other services; and
• inferred information, including possible age, gender, interests, seniority, compensation, and industry.

Because the AI-training authorization is not tied to a narrower category, it may cover data generated by customers’ use of the Services, as well as data obtained from third parties.

3. Communications and workplace data may be processed by automated systems

The policy says LinkedIn may use communications, including messages, for customer support, security, fraud prevention, and automated functions. Where settings permit, LinkedIn may scan messages to provide tools such as drafting responses, summarizing messages, scheduling meetings, and recommending next steps.

The policy does not clearly state whether message content used for these functions is also retained or reused for general AI-model training. This creates uncertainty and a potential confidentiality risk, particularly for business, recruiting, or regulated communications.

4. Sharing with Microsoft and affiliates

The policy permits sharing data with LinkedIn affiliates, including Microsoft, to provide and develop services. It also permits sharing:

• publicly shared LinkedIn content to provide or develop affiliate services; and
• personal data to improve, provide, or develop affiliate advertising services, subject to the European Regional Privacy Notice.

This may allow customer content and data to support AI or other product development across the broader Microsoft/LinkedIn group, although the policy does not expressly say Microsoft may train models using all shared data.

5. Limited controls and retention

Users may object to, restrict, or request deletion of personal data, subject to applicable law. However:

• most data is retained while an account remains open;
• inferred or generated data is included within retained data; and
• withdrawal or deletion rights do not expressly guarantee removal of data already incorporated into trained AI models.

6. Other notable changes

The additions also introduce or consolidate a comprehensive privacy policy structure, including expanded disclosures about data collection, advertising, profiling, cross-border transfers, affiliates, and automated decision-making. These provisions increase LinkedIn’s flexibility to combine data, create inferences, personalize services, and use data for research and product development.

Executive Summary

The diff appears to show the privacy policy being deleted and reinserted almost entirely, likely because of formatting or document-generation issues. The substantive text in the deleted and added versions is materially identical. No clear new customer-data right, restriction, or obligation can be reliably identified from this diff.

AI Model Training

The policy states that LinkedIn may use personal data to:

• “Improve, develop, and provide products and Services”;
• “Develop and train artificial intelligence (AI) models”;
• Develop, provide, and personalize services; and
• Generate insights using AI, automated systems, and inferences.

It also links to LinkedIn’s Responsible AI principles and generative-AI materials.

Important point: This AI-related language appears in both the deleted and added text. Accordingly, the diff does not demonstrate that LinkedIn has newly introduced or expanded AI training rights. However, the clause remains broad because it:

• Does not specify which categories of customer data may be used;
• Does not clearly limit training to de-identified, aggregated, or publicly available data;
• Does not identify particular AI models, vendors, retention periods, or geographic processing locations;
• Does not expressly provide a dedicated opt-out from AI model training; and
• May cover data supplied by users, collected from usage, inferred data, communications, and other sources, subject to applicable settings and regional notices.

Other Apparent Changes

• Minor punctuation, typography, and formatting changes appear, including curly apostrophes and a change from “Learn More.” to “.” in one location.
• The opening text appears malformed in places, such as “MattersLinkedIn’s mission,” suggesting that the comparison may have been generated from incorrectly merged or duplicated versions.
• The policy continues to permit broad data use for personalization, advertising, research, security, service development, and sharing with affiliates including Microsoft.
• The policy continues to state that data may be retained while an account remains open, including generated or inferred data.

Risk Assessment

Change-related risk: Low/uncertain. The supplied diff does not clearly establish a substantive amendment.

Underlying policy risk: Moderate to high. Independently of the change comparison, the continuing AI-training provision is broad and lacks detailed limits on data scope, model use, deletion from trained models, or user control. A clean, line-by-line comparison against the prior policy is needed before concluding that no substantive expansion occurred.

Diff Analysis

Available Information

The diff only states:

> “Added approximately 6,625 words to the document”

No actual added, deleted, or replaced contractual language is included.

Key Legal Changes

The specific legal changes cannot be identified from the information provided. In particular, it is not possible to determine whether the additions affect:

• Customer data ownership or licensing rights
• Confidentiality and permitted disclosures
• Data retention, deletion, or security obligations
• Service-provider or subprocessor access
• Liability, indemnification, or warranties
• Audit, compliance, or regulatory obligations
• Termination rights or post-termination data handling
• Governing law or dispute resolution

AI Training and Model-Use Terms

The provided diff does not reveal whether customer data may be:

• Used to train, fine-tune, or improve artificial-intelligence or machine-learning models
• Combined with other customers’ data for model development
• Reviewed by human personnel for quality, safety, or model training
• Retained after termination for training or benchmarking
• De-identified, anonymized, aggregated, or otherwise transformed before use
• Used to create models, outputs, embeddings, profiles, or other derived data
• Excluded from training by default or only excluded through an opt-out process

Accordingly, no conclusion can be reached about whether the changes create new AI-training risks.

Information Needed

Please provide the actual redline text, including the 6,625 added words and any deleted or replaced language. The most important provisions to review will be those containing terms such as:

• “train,” “training,” “fine-tune,” “improve,” or “develop”
• “artificial intelligence,” “machine learning,” “models,” or “algorithms”
• “customer data,” “content,” “inputs,” “outputs,” or “usage data”
• “de-identified,” “aggregated,” or “anonymized”
• “retain,” “delete,” “license,” “sublicense,” or “perpetual”
• “opt out,” “consent,” or “service improvement”

Without the underlying text, the only reliable conclusion is that a substantial amount of language was added, but its legal effect cannot be assessed.

Executive Summary

The diff appears to contain little or no substantive change to LinkedIn’s privacy terms. Most of the text is duplicated on both sides of the diff. The principal apparent changes are formatting, punctuation, and a truncated introductory sentence.

Key Changes

1. Introductory regional-policy language

• The deleted text begins with: “Privacy Policy” and refers to LinkedIn’s global Privacy Policy applying worldwide, followed by an incomplete phrase: “If you're located in the…”
• The replacement begins with: “will apply to you in our European Regional Privacy Notice.”

Because the surrounding text is incomplete, it is difficult to determine whether this reflects:

• a genuine change to the applicability of the European Regional Privacy Notice; or
• a formatting or extraction error in the diff.

Risk: If the wording was substantively changed, customers in the EU, EEA, Switzerland, or UK should confirm whether the regional notice still governs their data and whether any region-specific rights or restrictions have changed.

2. “Learn More” punctuation

• Deleted: “Learn More.”
• Added: “.”

This appears to be a link-label or formatting correction, with no evident legal effect.

3. Table-of-contents formatting

The diff shows formatting changes around “Table of Contents” and “Data.” These appear to be markup or extraction issues rather than changes to the policy’s operative provisions.

4. Typographical normalization

“LinkedIn's” appears to be changed to “LinkedIn’s” in one instance. This is a typographic change only.

AI Model Training and Use of Customer Data

No change to the AI provisions is apparent. The same language appears in both versions stating that LinkedIn may use personal data to:

• “develop and train artificial intelligence (AI) models”;
• improve, develop, provide, and personalize its services;
• generate insights using AI, automated systems, and inferences; and
• make inferences, including about age and gender.

The policy also refers users to LinkedIn’s Responsible AI principles and generative-AI information.

Important existing risk: This language permits broad use of personal data—including activity data, communications, profile information, inferred characteristics, and potentially public content—for AI development and training. The policy does not, in this diff, provide a specific AI-training opt-out, retention period, model-deletion mechanism, or clear limitation to anonymized data.

Overall Assessment

On the available diff, there is no identifiable new AI-training right or expansion beyond the already-present language. The main legal issue is the unclear introductory regional-policy change, which should be verified against the clean versions of the prior and revised policies.

Summary of Important Changes

Overall change

The diff appears to replace a short introductory section with a substantially expanded LinkedIn Privacy Policy. The new text provides detailed disclosures about data collection, use, sharing, retention, user rights, advertising, and international transfers.

AI and model-training changes

The most significant new provision is an express statement that LinkedIn may use personal data to:

• “Develop and train artificial intelligence (AI) models.”
• Improve, provide, personalize, and develop LinkedIn products and services.
• Generate insights using AI, automated systems, and inferences.

Key risks

• Broad scope: The policy does not clearly limit which data may be used for AI training. The wording potentially covers profile information, posts, messages, usage data, inferred information, communications, calendar data, resumes, job applications, and data received from third parties.
• No clear opt-out: The new language does not itself provide a specific right to opt out of AI model training. General data controls, consent withdrawal, objection, or restriction rights may apply depending on jurisdiction, but their effect on already-trained models is unclear.
• No deletion commitment for models: Although users may request deletion of personal data, the policy does not explain whether data incorporated into training datasets, model parameters, derived insights, or backups will be removed or retrained.
• Third-party and affiliate use: LinkedIn may share public LinkedIn content with affiliates, including Microsoft, to provide or develop their services. This could allow public content to support AI development outside LinkedIn’s core platform.
• Inference risks: LinkedIn may infer characteristics such as age, gender, interests, seniority, compensation bracket, and skills. These inferences may be used for personalization, advertising, recommendations, and potentially AI systems.
• Communications and AI tools: Where settings permit, LinkedIn may scan messages to provide bots and tools for scheduling, drafting responses, summarizing messages, and recommending next steps. This creates confidentiality and workplace-monitoring concerns.

Other important data-use changes

• Expanded collection from calendars, contacts, email headers, Microsoft services, employers, schools, partners, cookies, devices, and third-party websites.
• Broader cross-service and affiliate data sharing, including Microsoft.
• Extensive profiling and targeted advertising on and off LinkedIn.
• Employer access to certain enterprise-service usage and related information.
• Long retention of account data, including generated and inferred data, while the account remains open.
• Continued retention after closure for legal, security, fraud-prevention, regulatory, and dispute-related purposes.
• Public posts, social actions, profile information, and group activity may remain visible or be copied by others after deletion or account closure.
• Continued use of the policy through service use: material changes become binding after notice unless the user closes the account.

Drafting and presentation issues

The diff contains apparent formatting or extraction artifacts, including duplicated text and merged headings such as “ContentsData.” These should be corrected before publication to avoid ambiguity.

Summary of Important Changes

Overall Assessment

The diff appears to contain substantial duplicated text and malformed comparison markers. Based on the text that can be reliably compared, there are no clear substantive changes to LinkedIn’s data-use, sharing, retention, or customer-rights provisions.

Several apparent changes are limited to formatting or typography.

AI Model Training

The provision stating that LinkedIn may use personal data to:

• Improve, develop, and provide products and services;
• Develop and train artificial intelligence (AI) models;
• Personalize services; and
• Generate insights using AI, automated systems, and inferences,

appears in both versions with the same wording.

Risk Assessment

There is therefore no identifiable change in the AI-training authorization in this diff. The existing language nevertheless creates a broad ongoing right for LinkedIn to use personal data for AI development and training. It does not clearly:

• Limit training to de-identified, aggregated, or publicly available data;
• Identify particular AI models or vendors;
• State how long training data or model outputs will be retained;
• Explain whether data can be used to train models for Microsoft or other affiliates;
• Provide a specific opt-out from AI training; or
• Explain whether deletion requests can remove data already incorporated into trained models.

The policy also continues to permit sharing of publicly shared LinkedIn content with affiliates, including Microsoft, to provide or develop their services, subject to the European Regional Privacy Notice. This may create an indirect AI-development risk for public posts and other publicly available content.

Other Apparent Changes

• “LinkedIn's” was changed to the typographic form “LinkedIn’s.” This is non-substantive.
• “Learn More.” appears to have been changed to “.” in one location, apparently removing or corrupting a hyperlink or label. If intentional, this could reduce access to referenced information, but it does not appear to change the underlying legal terms.
• Table-of-contents and heading text appears malformed or altered by the diff formatting.
• The opening privacy-policy language is incomplete in the supplied comparison, so it is not possible to determine whether the regional-notice wording changed.

Customer Impact

Customers should not treat this diff as evidence of a new AI-training right: the express AI-training language appears unchanged. However, the policy already grants LinkedIn broad rights to use personal data, inferred data, communications, and public content for service development, research, personalization, affiliate services, and AI model development.

Summary of Important Changes

1. Express permission to use data for AI training

The revised policy expressly states that LinkedIn may use personal data to:

• “Develop and train artificial intelligence (AI) models”;
• Improve, develop, provide, and personalize LinkedIn products and services; and
• Generate insights using AI, automated systems, and inferences.

This is a significant change because it adds AI model training as a stated purpose for processing customer data. The policy does not clearly limit this use to anonymized, aggregated, publicly available, or non-sensitive data.

2. Broad categories of data potentially used

The policy generally defines “personal data” broadly and separately states that LinkedIn may use:

• Profile and professional information;
• Posts, comments, videos, messages, and other uploaded content;
• Usage and engagement data, including searches, viewed content, clicks, and learning activity;
• Calendar, contact, device, location, and communications data;
• Data received from employers, partners, Microsoft, and other services; and
• Inferences, including inferred age, gender, interests, seniority, industry, and compensation range.

Because the AI-training authorization is not tied to a specific data category, it may cover both user-submitted content and information observed or inferred by LinkedIn.

3. No clear AI-training opt-out or deletion commitment

The revised language refers to settings and general data rights, including deletion, objection, restriction, and withdrawal of consent. However, it does not clearly provide:

• A dedicated opt-out from AI model training;
• A guarantee that opting out will prevent future training use;
• Removal of data already incorporated into trained models;
• A specific retention period for training data; or
• A clear explanation of whether trained models may continue to be used after account closure.

This creates uncertainty about the practical effect of exercising privacy controls.

4. Sharing with Microsoft and affiliates

The policy permits sharing data with LinkedIn affiliates, including Microsoft, to provide and develop services. It also permits sharing publicly posted LinkedIn content and personal data for affiliates’ services and advertising activities, subject to the European Regional Privacy Notice.

This may allow customer data—particularly public content—to be used across a broader corporate ecosystem and potentially in developing affiliated AI or related technologies.

5. Automated processing and communications

LinkedIn states that it may scan messages, where settings permit, to support tools that draft responses, summarize messages, schedule meetings, and recommend next steps. It also uses automated systems for recommendations, security, content moderation, fraud prevention, and career opportunities.

This increases the risk that confidential business communications and professional activity may be analyzed by automated systems, although the policy does not specify whether message content is used to train general-purpose AI models.

6. Other material implications

The policy also confirms broad data collection, cross-service tracking, advertising personalization, long retention while an account remains open, cross-border processing, and continued retention of certain data after account closure.

Overall risk: The clearest substantive change is the express authorization to train AI models using broadly defined personal data, without a clearly stated AI-specific consent mechanism, opt-out, data minimization limit, or model-deletion commitment.

Summary of Changes

Main Change

• Approximately 6,625 words have been removed from the document.
• No replacement language or details identifying the deleted provisions were provided.

Legal and Commercial Risks

Loss of Important Protections

The deleted material may have contained provisions addressing:

• Customer and provider obligations
• Liability limits and indemnities
• Confidentiality and data-security requirements
• Intellectual-property ownership and licensing
• Data retention, deletion, and return
• Service levels, warranties, and remedies
• Termination rights
• Governing law and dispute resolution
• Compliance with privacy and data-protection laws

Without the deleted text, it is not possible to determine whether protections were removed for the customer, the provider, or both.

Increased Interpretive Uncertainty

A large deletion may create:

• Gaps or inconsistencies with the remaining provisions
• Unclear allocation of risk
• Ambiguity about rights and obligations
• Greater reliance on default legal rules
• Potential conflicts with definitions or cross-references elsewhere in the agreement

The document should be checked for references to sections, terms, or schedules that no longer exist.

Customer Data and AI Training

• The diff does not identify whether customer data may be used to train AI models.
• Any provisions governing AI training, machine learning, model improvement, data analytics, or use of customer content may have been removed.
• If restrictions on training were deleted, the customer may have less protection against its data being used to:
• Train or fine-tune provider models
• Improve products or services
• Create derived datasets or model outputs
• Share data with affiliates or third-party AI providers
• If consent, opt-out, deletion, confidentiality, or anonymization provisions were removed, the customer’s control over its data may be reduced.
• Conversely, the deletion may have removed a provider’s express right to use data, but this cannot be confirmed from the supplied diff.

Recommended Review

Before accepting the revised document:

1. Obtain the full deleted text or a clean prior version.

2. Compare provisions concerning customer data, AI training, and model improvement.

3. Confirm whether customer data is used only to provide the services or also for product development.

4. Require express language addressing consent, anonymization, opt-out rights, retention, deletion, and third-party AI use.

5. Check all remaining cross-references and definitions for drafting errors.

Bottom line: The only confirmed change is a substantial deletion. Its legal effect—and specifically any change to AI-training rights—cannot be reliably assessed without the removed provisions.

Summary of Important Changes

1. New express permission to use data for AI training

The revised policy expressly states that LinkedIn may use personal data to:

• “Develop and train artificial intelligence (AI) models”
• Develop, provide, and personalize its services
• Generate insights using AI, automated systems, and inferences

This is a significant expansion or clarification of permitted data use. The term “personal data” is broad and may include profile information, posts, messages, calendar information, job applications, usage data, inferred characteristics, and data obtained from third parties.

Key risks

• No clear limitation on the types of data used for training. The policy does not clearly exclude private messages, resumes, job applications, customer-provided data, or sensitive personal data.
• No explicit AI-training opt-out is stated. Although the policy provides general privacy settings and rights to object or restrict processing, it does not identify a specific control for preventing data from being used to train AI models.
• No deletion commitment for trained models. Closing an account or deleting data may not remove information already incorporated into training datasets, model parameters, derived insights, or outputs.
• Potentially broad purposes. Training appears tied not only to LinkedIn’s services but also to development of products, services, and related insights.

2. Expanded sharing with affiliates, including Microsoft

The policy states that LinkedIn may share:

• Publicly shared content with affiliates, including Microsoft, to provide or develop their services; and
• Personal data with affiliates to improve, provide, or develop advertising services.

This may permit LinkedIn data to be used across Microsoft-affiliated products and AI systems, subject to the European Regional Privacy Notice where applicable. The policy does not clearly limit such sharing to LinkedIn’s own models or services.

3. Increased use of automated analysis

LinkedIn may use automated systems to:

• Scan messages, where settings allow, for scheduling, drafting responses, summarization, and recommendations;
• Moderate content and detect policy violations;
• Make inferences, including about age, gender, interests, seniority, compensation, and career opportunities;
• Rank content and recommend jobs, contacts, and learning materials.

This creates risks of privacy intrusion, inaccurate profiling, bias, and consequential automated decisions in employment or recruiting contexts.

4. Broader data sources and retention

The policy confirms collection from calendars, contacts, partners, employers, applicant-tracking systems, Microsoft services, advertising partners, cookies, and off-site activity. LinkedIn generally retains data while an account remains open, including data generated or inferred from use.

5. Practical implications

Customers should review:

• Whether confidential business, employee, recruiting, or customer data is uploaded;
• Available AI, personalization, advertising, and message-scanning settings;
• Contracts or regional notices that may impose additional restrictions;
• Whether deletion or objection rights adequately address AI training and derived data.

Executive Summary

The diff appears to contain a wholesale re-rendering or replacement of the policy text, but the substantive wording is almost entirely unchanged. The identifiable changes are primarily formatting, punctuation, and typography corrections. No new substantive permission to use customer data for AI training appears to have been added in this diff.

Identified Changes

1. AI model training language — No apparent change

The policy states that LinkedIn may use personal data to:

• “improve, develop, and provide” products and services;
• “develop and train artificial intelligence (AI) models”; and
• develop, provide, and personalize services and generate insights using AI, automated systems, and inferences.

This language appears in both the deleted and replacement text. Accordingly, the diff does not appear to expand or narrow LinkedIn’s AI-training rights.

2. Existing AI-related risk remains significant

Although not newly introduced, the retained wording creates broad potential uses of customer data:

• It does not clearly limit AI training to de-identified, aggregated, or publicly available data.
• It does not identify specific data categories excluded from training, such as private messages, resumes, employment information, calendar data, or sensitive profile information.
• It does not clearly distinguish between training LinkedIn’s own models and training models operated by affiliates, including Microsoft.
• The policy separately permits use of communications by automated systems for message suggestions, moderation, security, and similar functions.
• The policy also permits sharing public LinkedIn content and personal data with affiliates, including Microsoft, for service and advertising development, subject to the European Regional Privacy Notice.

Customers should therefore treat the AI-training authorization as continuing and potentially broad, even though it is not expanded by this particular diff.

3. Minor wording and formatting corrections

The visible changes include:

• “LinkedIn's” changed to the typographically equivalent “LinkedIn’s.”
• “Learn More.” changed to a period following the apparent removal of a hyperlink.
• “Table of ContentsData” corrected to “Table of Contents.”
• Apparent cleanup of headings, punctuation, and document formatting.

These changes do not appear to alter substantive legal rights or obligations.

Overall Risk Assessment

Change-related risk: Low. The identifiable amendments are largely editorial.

Policy-level data-use risk: Moderate to high. The unchanged policy continues to authorize broad use of personal data—including potentially inferred data and communications—for service development, AI model development and training, personalization, research, advertising, security, and sharing with affiliates. The practical effect of those permissions may depend on settings, regional notices, and applicable law.

Summary

The diff provided only states that approximately 6,625 words were removed. It does not identify which provisions were deleted, nor does it show any additions or replacement language.

AI Training and Customer Data

• No specific changes can be determined regarding:
• Whether the customer’s data may be used to train, fine-tune, test, or improve AI models;
• Whether customer data may be combined with other data for model development;
• Whether prompts, outputs, usage data, telemetry, or derived data may be retained or used for training;
• Whether the customer can opt out of AI training;
• Whether data is anonymized, de-identified, or otherwise protected before use;
• Ownership of models, training outputs, or data-derived insights; or
• Restrictions on human review or access to customer data.

Potential Legal and Commercial Risk

The removal of 6,625 words could materially change the agreement, but the nature and direction of the risk cannot be assessed without the deleted text and any replacement language. Depending on what was removed, the changes could affect:

• Confidentiality and permitted-use restrictions;
• Data protection, security, and breach obligations;
• Retention and deletion requirements;
• Intellectual-property ownership and license rights;
• Liability caps, indemnities, and warranties;
• Audit, compliance, and regulatory obligations;
• Customer termination and data-export rights; and
• Restrictions or disclosures concerning AI systems.

If provisions limiting use of customer data, requiring deletion, or prohibiting model training were removed, the customer could face broader vendor rights to use its data, reduced confidentiality protections, or less control over downstream AI development. Conversely, removal of vendor rights or disclaimers could increase the vendor’s obligations.

Required Information

A meaningful legal analysis requires either:

1. The full before-and-after contract text;

2. A redline showing the deleted and added language; or

3. At minimum, the specific deleted provisions and any replacement provisions.

On the current information, no definitive conclusion can be drawn about changes to AI-training rights or other contractual obligations.