Recovering from failures

When AI systems fail, clear communication decides if users stay or leave. Trust can survive errors, but only with the right recovery approach. How you handle failures matters more than avoiding them.

Be specific about what went wrong. Generic apologies frustrate users who took time to report problems. If your recommendation system suggested bad content, say exactly why it happened. Maybe it misread user patterns or lacked enough data. Users respect honesty about real limitations more than vague excuses. Follow up with people who reported problems. Show them their feedback made a difference. When a translation app adds new dialects after complaints, tell those users first. Send messages showing the exact improvements they requested. This turns angry users into partners who help make the AI better.