综述 | 产业界负责任人工智能实践的半个十年实证研究

TL;DR - A review of 161 empirical studies from 2019–2025 finds that responsible AI is becoming institutionalized in industry, but implementation remains constrained by skills gaps, misaligned incentives, poorly integrated tools, and weak external collaboration.

• Industry practices increasingly include fairness testing, model documentation, review gates, governance processes, and stakeholder participation.
• More than 120 studies reported insufficient technical or sociotechnical knowledge among practitioners.
• Effective interventions must fit real workflows and span data collection, development, deployment monitoring, and feedback—not merely model-output evaluation.
• The authors recommend continuous role-specific training, organizational accountability, co-designed tools, and institutionalized external participation.