From a consumer protection perspective, the FCRA aims to ensure fairness, accuracy, and transparency in how information influences employment decisions. Tools providing subjective measurements (e.g., AI-driven scores, "teamwork potential," or "risk of underperformance" based on non-verifiable inferences) often fall under this umbrella because they inherently assess "personal characteristics" or "mode of living." However, their subjective nature creates significant compliance hurdles and risks for employers, as outlined below.
Accuracy Challenges: An algorithm inferring traits like overall sentiment or personality predictions from social media posts is subjective, and hard to audit for accuracy, failing FCRA's "maximum possible accuracy" mandate and risking false rejections of qualified candidates.
Dispute Resolution Issues: Candidates can't easily verify or dispute opaque scores (e.g., a low "agreeableness" rating from tweet sentiment analysis), complicating FCRA-mandated investigations and exposing companies to lawsuits over unresolved inaccuracies.
Transparency Burdens: Disclosing the full report under FCRA is tough—sharing the algorithm's black-box inputs (e.g., vast training data) without revealing proprietary details violates notice requirements, leading to compliance headaches.
Regulatory Risks: Such tools invite CFPB/FTC scrutiny for potential biases (e.g., cultural misreads of online behavior), with willful FCRA violations carrying fines up to $4,691 per case, plus state-level AI hiring audits.
The following analytics will be restricted to non-FCRA use/cases.
Social Media Score
Post Sentiment
Sentiment over Time
Channel Volumes
OCEAN Personality