Resources

Test Like Your Company Depends On It

Best For: Creating testing, auditing, and incident response systems that prevent costly failures and build stakeholder confidence. Moving from reactive to proactive AI governance.

Purpose

Accountability transforms AI from a black box into a trusted business system.
Without clear accountability, AI failures become company-ending events - from biased lending decisions triggering lawsuits to hallucinating chatbots destroying brand reputation.
Strong accountability systems prevent these failures while enabling faster innovation through confident deployment. Investors evaluate accountability practices as indicators of operational maturity.
Customers require accountability guarantees before trusting mission-critical processes to AI.
This workstream helps you build testing and auditing systems that catch problems before customers do, create incident response capabilities that minimize damage, and establish governance structures that enable responsible scaling.

Method

Baseline Audit Controls
Early Stage | Implement accountability controls (e.g., storing results of evals, dataset versioning and logs of changes to new models, etc) that help you move faster by anticipating consequences—catching issues before they become blockers. Create simple logs of who accesses AI systems and what decisions they make. For foundational model companies, document all training data sources and major model decisions. Use version control for prompts and model configurations. Create basic test suites that run before each deployment. Document known failure modes and edge cases. These early practices become invaluable IP documentation for due diligence.
Systematic Testing Infrastructure
Growth Stage | Build comprehensive testing and documentation systems. Document all key AI system decisions with rationale and alternatives considered. Create AI incident response plans with clear escalation paths. For app companies, implement adversarial testing (Red Teaming) for customer-facing AI. See our 1 page guide (coming soon!) to getting started with Red Teaming. For tooling/app companies, conduct supply chain audits of all AI components. Implement full logging of AI actions and potentially harmful outputs. For foundational companies, deeply understand training data composition, limitations, and biases. Create automated test suites that evaluate performance across relevant customer segments and characteristics.
Enterprise Accountability Platform
Scaling Stage | Implement institutional accountability systems. Build comprehensive risk mitigation and harm reduction processes. Create 24/7 incident management capabilities with defined SLAs. Achieve full audit compliance with relevant standards (SOC2 Type II for AI, ISO 42001). Implement automated bias detection and correction systems. Create detailed forensic capabilities for investigating AI decisions. For agent-based systems, implement comprehensive human oversight and intervention capabilities. Consider third-party audits and certifications that unlock enterprise deals.

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Trap Doors

Weak accountability systems create existential risks. These common failures have destroyed promising AI companies.

Risk Theater
Superficial testing that misses real risks: Many startups test only happy paths, missing edge cases that cause production failures. Real-world inputs are messier, more adversarial, and more creative than test data. Implement adversarial testing, use production data (with privacy protection) for testing, and specifically test for failure modes. One production incident can destroy years of trust-building.
Blame Diffusion
"The AI Did It" Accountability Gaps: When AI fails, someone must be accountable. "The model made the decision" doesn't satisfy regulators, customers, or courts. Assign clear ownership for AI decisions - product managers own feature behavior, engineers own technical performance, and executives own systemic risks. Document decision chains and escalation paths before incidents occur.

Forensic Blindness
Inability to explain what went wrong: Post-incident investigations often fail because systems lack adequate logging and interpretability. Implement comprehensive audit trails, decision logging, and model interpretability tools from the start. You must be able to explain not just what the AI did, but why. Regulators increasingly require algorithmic explainability for investigation.

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Founders + Operators

Participate in workshops and learning experiences that help you survive and grow. Connect with values-aligned peers. Shape how startups impact society.

Founders + Operators

Participate in workshops and learning experiences that help you survive and grow. Connect with values-aligned peers. Shape how startups impact society.

Your first major AI incident is not a matter of if, but when.

Companies with strong accountability systems recover faster and grow stronger. Those without them often don't survive.

Cases

Spotify's Algorithmic Responsibility framework shows how systematic testing prevents recommendation algorithm failures. Key lesson: Embed accountability into your development process.

Duolingo's AI testing infrastructure reveals how educational AI maintains accountability across diverse global users. Their continuous testing across languages and cultures ensures equitable learning outcomes.

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Key lesson: Test for your most vulnerable users.

Grammarly's responsible AI practices demonstrate how writing assistance AI can maintain accountability at scale. Their comprehensive bias testing and human-in-the-loop systems prevent harmful content generation while processing billions of suggestions.

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Key lesson: Accountability enables scale.