缩小人工智能信任差距：可信人工智能独立认证的案例

中文摘要 AI 辅助

在过去十年中，负责任的人工智能（RAI）已产生大量实践，用于识别和减轻人工智能在高风险环境中带来的风险。然而，这项工作尚未产生一个奖励可信度的市场。认真投资于安全、公平和监督的公司无法向消费者、监管机构和股东持续证明其系统超越了最低合规标准。社会缺少一种认可或比较差异的方式，导致了信任差距。我们认为，这种差距部分是由于关注负责任的人工智能（内部流程问题）而非可信的人工智能（可独立验证的现实世界结果问题），并且由于三个复合失败而持续存在：（1）市场无法区分可信系统与其模仿品；（2）评估针对模型和输出而非部署的社会技术系统及其结果；（3）测量生态系统旨在避免伤害而非证明益处。通过审查现有人工智能治理工具并将其与医疗保健、可持续性和安全领域的认证制度进行比较，我们表明没有一个在单一框架中整合治理基线、独立验证的积极结果证据和市场信号。我们提出以结果为导向的独立认证作为可以缩小信任差距的连接层，通过使可信度可衡量、可比较和获得商业回报来补充监管和内部治理。

英文摘要

Over the past decade, responsible AI (RAI) has produced a substantial body of practice for identifying and mitigating the risks AI poses in high-stakes settings. Yet this work has not produced a market that rewards trustworthiness. Firms that invest seriously in safety, fairness, and oversight cannot consistently prove to consumers, regulators, and shareholders that their systems go beyond the bare minimum of compliance. What is missing is a way for society to recognize or compare the difference. The result is a trust gap: a structural condition in which responsible development efforts happen inside organizations but produce no external, independently recognized and verifiable signal of trustworthy outcomes. We argue this gap is sustained in part because of a focus on responsible AI (a matter of internal process) as opposed to trustworthy AI (a matter of independently verifiable real-world outcomes), and that it persists because of three compounding failures: (1) the market cannot distinguish trustworthy systems from their imitations; (2) evaluation targets models and outputs rather than deployed sociotechnical systems and their outcomes; (3) the measurement ecosystem is oriented toward avoiding harm rather than demonstrating benefit. Reviewing existing AI governance instruments and comparing them to certification regimes in healthcare, sustainability, and security, we show that none integrate a governance baseline, independently verified positive-outcome evidence, and market signaling in a single framework. We propose independent, outcome-oriented certification as the connective layer that can close the trust gap, complementing regulation and internal governance by making trustworthiness measurable, comparable, and commercially rewarded.

发表机构

• University College London（伦敦大学学院）
• AI Ethics Lab（人工智能伦理实验室）
• University of Cambridge（剑桥大学）
• Columbia University（哥伦比亚大学）
• MATS
• Tech with Intention（技术与意图）
• University of Southern California（南加州大学）
• Kyushu University（九州大学）
• University of Chile（智利大学）
• BehSci Meets AI（行为科学与人工智能）
• Digital Trust Council（数字信任委员会）

机构由 AI 辅助整理，请以论文原文为准。