AI Transparency in Hiring Solutions → Hire

AI Explainability of Hire Products

Learn more about how our Hire AI products function, comply with regulations, and manage data privacy below.

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LinkedIn Hiring
AI Products

As AI plays a growing role in hiring, responsible use is essential to maintaining trust in the hiring ecosystem. At LinkedIn, our principles guide how we design, deploy, and govern AI with transparency, accountability, and a human-centered approach.

These responsible AI principles guide our work at LinkedIn →

Hiring Assistant
RSC+
AI-Assisted Messages
AI-Assisted Search

Hiring Assistant Spotlight

Hiring Assistant is the only AI agent for recruiters powered by the world’s most dynamic talent network. Built on trusted, real-time data from 1+ billion members, Hiring Assistant delivers high-quality shortlists and uncovers candidates you’d otherwise miss.

It learns from recruiter actions – like which candidates are viewed, contacted, or advanced – so recommendations become more relevant with use. And because it’s built with LinkedIn’s responsible AI principles and verified data, recruiters stay in control with technology they can trust.

How do recruiters use Hiring Assistant?

Recruiters choose the roles they want to use Hiring Assistant for and specify the qualifications they’re seeking in candidates. Hiring Assistant aids by refining those qualifications into better search queries and retrieving and displaying candidates that match the recruiter’s chosen qualifications.

Human-centered decision making, not autonomous

Hiring Assistant does not make autonomous or automatic decisions. Hiring Assistant ensures human involvement remains central to the hiring process, mitigating excessive reliance on automation.

Compliant with AI Regulations

LinkedIn complies with applicable law in the jurisdictions where our products are offered and we always seek to enable our customers to comply with their legal obligations when using our products.

Training Hiring Assistant’s models

Hiring Assistant's learnings from user actions are confined to the user and the particular role for which they are hiring; it does not feed into or modify underlying OpenAI models nor does it negatively impact the candidate’s normal LinkedIn seeker experience.

No Processing of Sensitive Personal Data

Hiring Assistant in Recruiter does not require the processing of sensitive personal data provided by customers. Additionally, it does not evaluate based on characteristics such as but not limited to name, age, gender identity, race, religion, sex, and disability.

Mitigating Bias and In-Product Safeguards

LinkedIn conducts fairness and bias reviews on a per-model level. When potential biases are identified, the team investigates and, as appropriate, retrains the model to mitigate the biases (or deramps the model). Additionally, Any user inputs that prompt Hiring Assistant to return sensitive personal data are not supported and the user will be blocked from proceeding.

Ranking, Sourcing, Evaluating candidates based on qualifications

Hiring Assistant transforms the end user’s hiring qualifications into several search queries, evaluates candidates' profiles to access how well they align with the role’s qualifications, then retrieves & ranks candidates using LinkedIn Recruiter’s standard ranking mechanisms.

RSC+ and Connected Projects

RSC+ is the latest enhancement to LinkedIn’s ATS integration, Recruiter System Connect (RSC), and unlocks Connected Projects* for Hiring Assistant customers. Connected Projects streamline applicant management and evaluation by allowing Hiring Assistant to access applicants and insights from qualifying Applicant Tracking Systems (ATSs).

Connected Projects centralizes applicants from LinkedIn and the ATS in one view and uses up-to-date profile information from both platforms to power smarter Hiring Assistant evaluation. Unlock LinkedIn’s most advanced integration to-date through Recruiter System Connect by toggling on “RSC+” in product settings.

*Connected Projects requires a Hiring Assistant license.

How do recruiters use Connected Projects?

Once the RSC+ enhancement is activated, Recruiters can run Hiring Assistant evaluations across all applicants, no matter where they applied, by connecting Projects in LinkedIn Recruiter directly to job requisitions in the ATS. Assuming Hiring Assistant has been enabled on the project, it will work in the background as applications come in to identify and surface top talent using both candidate profiles and ATS resume insights.

How does LinkedIn store ATS data?

All customer data is stored in LinkedIn owned and operated data centers in the United States. LinkedIn stores this data within a Distributed Data Store, in which all customer data is programmatically segmented and secured. For more details, check the RSC data security guide here.

What type of data is shared between LinkedIn and your ATS?

We only use data customers have approved for us to access based on permissions tied to their login. With the customer's permission, LinkedIn retrieves recruiting-related data from the ATS. This includes:

Candidate data (e.g. name, email, current status, candidate notes or tags)
Application records (e.g. job applied to, source, stage history, application notes, resumes, interview feedback)
Job requisition metadata (e.g. job title, location, ID)
Access control lists for specific entities that might be restricted (e.g. private job requisitions)
Additional data may be made available for future roadmap enhancements. Prospective RSC+ launches on the roadmap include: pre-screen scheduling and feedback, bi-directional stage syncing between Recruiter and your ATS, exporting prospects to your ATS, and more.

How does enabling RSC+ change the existing Recruiter System Connect integration experience?

RSC+ is a LinkedIn Recruiter integration enhancement that continuously pulls data from your ATS via Recruiter System Connect, helping ensure your candidate and job information on LinkedIn is up-to-date and consistent. It augments standard RSC data with additional detail, such as applicant resumes, providing a more complete view of each applicant. This improved data accuracy powers LinkedIn Hiring product features like Hiring Assistant applicant evaluation through Connected Projects, making your hiring workflow smoother while maintaining the data privacy and security from standard RSC.

Who controls the ATS data?

Customers are the controller of their RSC personal data as defined in the LinkedIn Subscription Agreement (LSA) or your governing LSA with LinkedIn.

How will RSC+ for Connected Projects use and access customer data?

For Hiring Assistant customers, RSC+ will allow customers to connect their ATS job requisitions to LinkedIn hiring projects, allowing customers to view and manage applicants from their ATS on LinkedIn. Hiring Assistant will not be automatically enabled on a project or have access to ATS data without customer consent.

In the future, customers using RSC+ will also have the option to move applicants to a new stage in their ATS pipeline or reject applicants from LinkedIn. No applicant stage movements or disposition actions will be automated unless a customer explicitly enables automation settings.

AI Explainability: AI- Assisted Messages

With AI-Assisted Messages, recruiters can compose and send unique, personalized messages to candidates. AI-Assisted Messages draws on profile information provided by the candidate and combines it with your requirements to create a fully personalized message.

Once the message is drafted, users can make edits and customizations to ensure the message accurately reflects their tone, and add any additional information.

How do recruiters use AI-Assisted Messages?

AI-Assisted Messages is part of the message compose flow in Recruiter. When you click the Compose icon associated with a candidate, you'll be able to individually or bulk draft messages with AI.

What data is LinkedIn using to draft GenAI messages?

We leverage the data that our customers and members have chosen to share with us on our platform, and use AI to draft personalized outreach messages. The following data fields are currently used for AI generation (where available):

Recruiter information: Name, title, company name, company information, and custom content
Candidate information: Title, candidate company, open to work, company follower, past applicant, and mutual connections
Job information: Job title, job company, skills, location, workplace type, compensation, and employment type

When can I use AI-Assisted Messages?

Recruiters can choose to use this feature when composing message content to help craft effective, personalized messages.

How do users provide feedback to the AI messages?

Recruiters can provide feedback by clicking the Thumbs up or Thumbs down icons in the message body to help refine the AI model and improve future drafts.

AI Explainability: Advanced AI-Assisted Search

Advanced AI-Assisted Search provides better quality of results by going beyond standard filters and keywords listed on profiles, matching based on qualifications that are unique to the candidate you are looking for – even those critical, hard-to-define skills not explicitly listed on resumes. For example, it can match on qualifications such as “Data-driven decision maker” or “excellent written and communication skills.”

Furthermore, it will identify which qualifications were found in a candidate’s profile and which are missing, helping recruiters quickly understand top matches.

How do recruiters use Advanced AI-Assisted Search?

Recruiters can choose when to use Advanced AI-Assisted Search when searching for candidates on LinkedIn Recruiter. Advanced AI-Assisted Search will analyze what a user wrote and instantly populate the right search filters to match the provided criteria.

Keeps humans in control of decision making

Advanced AI-Assisted Search provides users with search recommendations and results and does not make hiring or sourcing decisions.

Complies with AI regulations

LinkedIn complies with applicable law in the jurisdictions where our products are offered. We strive to provide the resources that enable our customers to assess and ensure their compliance with laws and regulations that apply to them.

Addresses bias, privacy and safety via testing and in-product safeguards

Our AI models were reviewed for equal treatment and harmful content – helping to identify, investigate, and mitigate any detectable biases. Additionally, any user inputs that prompt to return sensitive personal data will result in the user being blocked from proceeding until removed.