List of Data Brokerage Firms and Opt Out Template

Shadow profiles are unauthorized collections of candidate data harvested from social media, public records, and browsing history. They exist parallel to official applications and include information candidates never provided. AI recruitment tools compile these profiles from 50+ digital touchpoints. These dossiers may contain everything from your shopping habits and financial history to personal relationship data and political leanings. Unlike traditional background checks that require consent, these shadow profiles are assembled covertly through data scraping and cross-platform tracking technologies.

What's Included in Shadow Profiles?

Most shadow profiles combine explicit (directly provided), implicit (inferred from behavior), and predictive (AI-generated) data. 

Basic Candidate Information (Explicit)

Full Name (including previous names & nicknames)
Contact Details (email, phone, address, work location)
Résumé Details (education, degrees, past jobs)
Social Media Links (LinkedIn, Twitter, GitHub, Facebook)

 
Professional & Work-Related Data (Implicit)

Skills & Experience – Pulled from LinkedIn, job applications, certifications
Job-Hopping Patterns – How often you change jobs
Salary History & Expectations – Estimated based on industry trends, past applications
Endorsements & References – From LinkedIn, review platforms, past applications
Project Contributions – GitHub, ResearchGate, personal blogs
Public Speaking & Conference History – Talks, events, and panels

 
Behavioral Data (AI-Inferred)

Communication Style – How you write emails, job applications, or social media posts
Personality Analysis – Based on language, job preferences, and online activity
Work Ethic Predictions – AI-generated scores from activity patterns
Cultural Fit Assessment – Compared to company values and past hires

⚠️ Risk: AI predictions may lead to biased hiring decisions if they incorrectly label candidates.

AI cannot detect human emotions, and it cannot predict your future success; however, for more than 5 years this technology has gone unregulated and has been on a steady incline in use. 

The talent acquisition industry is exploiting AI, Machine Learning, Natural Language Processing, and Biometric data claiming they can create a skills-based workforce and predict probability of success...resulting in once successful people being destitute, homeless.

AI Attempts and Failure to Detect Human Emotions

 
Risk & Compliance Data

Background Checks – Criminal records, driving history, financial standing
Credit Score & Debt History – Especially for finance-related jobs
Legal Disputes – If publicly available (e.g., lawsuits, business disputes)
Past Employer Feedback – Some firms collect anonymous employer reviews

⚠️ Risk: Errors in credit or criminal records may disqualify candidates unfairly.

AI-Generated Hiring Predictions

Likelihood of Quitting Early – Based on job history & industry trends
Leadership Potential – Inferred from past roles & social media presence
Remote vs. Office Suitability – Based on past job settings & work style
Personality Fit for a Role – AI comparison with similar job applicants

⚠️ Risk: Candidates may be rejected before they even apply based on flawed AI predictions.