Hiring decisions often rely on self-reported skills and one-on-one interviews, which can be unreliable due to cognitive biases. Candidates may overestimate their abilities (Dunning-Kruger effect) or downplay them (imposter syndrome). Meanwhile, peer insights—which could reveal collaboration skills, cultural fit, and real-world competence—are missing from the process. This gap leads to costly hiring mismatches.
One way to address this is by creating a platform where recruiters can access structured peer feedback about candidates. Peers—such as coworkers, collaborators, or mentors—would answer standardized questions (e.g., "How does this person handle conflict?"). Candidates control which feedback is shared, ensuring privacy while providing recruiters with deeper insights. The system could integrate with LinkedIn or resume profiles, adding verified social proof beyond superficial endorsements.
To work, the system must encourage honest participation while protecting all parties. Peers could earn reputation points or reciprocal feedback opportunities to motivate engagement. Anonymized responses might ease concerns about giving critical feedback. Candidates could preview feedback before sharing it, addressing privacy hesitations. For recruiters, aggregated scores and standout qualitative insights would streamline evaluation.
An MVP might focus on a simple web app where candidates request peer feedback, compiling responses into shareable reports. Early testing could partner with companies hiring for teamwork-heavy roles (e.g., project managers). If validated, integration with HR tools and expansion to internal promotions could follow. Compared to LinkedIn's binary endorsements, this would offer nuanced insights; unlike internal peer-review tools like Bridgewater's "Baseball Cards," it could scale across organizations.
By complementing traditional hiring methods with peer-verified data, this approach could help match candidates to roles where they’re genuinely likely to thrive.
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Digital Product