7 Ways to Implement Explainable AI in Practice

• 1. Assess risk early

  At project kickoff, identify decisions with financial, safety, or regulatory impact and determine where explanations are essential. Prioritize traceability for the riskiest areas to guide governance and validation.

• 2. Design explainability into design choices

  Embed explainability in model selection, data handling, and evaluation metrics. Align success criteria with clear, user-relevant explanations to improve adoption and accountability.

• 3. Governance, auditing, and documentation

  Establish versioned explanations, audit trails, and accessible docs for regulators and stakeholders. Record inputs, the rationale shown to users, and any actions taken to support oversight.

• 4. Monitor in production and protect privacy

  Continuously check explanation quality, detect drift, and guard against misuse. Apply privacy-preserving techniques and strong security to maintain trust and compliance.

• 5. Integrate explanations into decision workflows

  Surface rationale at the point of choice and route high-risk cases to human review when needed. Provide a concise reason and suggested next steps to preserve speed and accountability.

• 6. Choose the right explanation approach

  Balance model-agnostic and model-specific explanations based on risk, speed, and context. Consider hybrid strategies to keep explanations consistent and actionable across diverse systems.

• 7. Communicate explanations with visuals and actionable guidance

  Pair explanations with plain-language summaries and visuals: What happened, key drivers, and next steps. Use simple visuals and color cues to guide quick decisions without technical noise.