vieses

What problem does it solve? Product teams often run interviews, analyze data, and make roadmap decisions while unconsciously affected by confirmation bias, anchoring, optimism bias, and other cognitive distortions, leading to flawed research and poor prioritization. ## Core Features & Use Cases - Stage-Specific Bias Checklists: Loads a cognitive bias checklist matched to the current product stage (L0-L5) and assesses which biases are active, with mitigations for each. - Agent Self-Check: Audits the AI assistant itself for sycophancy, recency bias, pattern matching, and completionism before proceeding. - System-Level Diagnosis: Applies a five-phase systemic diagnostic (awareness, motivation, ability, reinforcement, sustainability) to determine whether apparent bias is actually a rational response to a badly designed system. - Use Case: Before running a round of user interviews for a new feature, run the bias check to surface confirmation bias risks, define mitigations, and produce a structured bias briefing with a proceed/pause recommendation. ## Quick Start Run the bias check before my upcoming user interviews and list the active bias risks with mitigations.