Algorithmic accountability scholarship has focused heavily on explanation, helping affected parties understand why decisions were made. We argue this focus is insufficient. Explanation without evidentiary access does not enable meaningful contestation. A person told "your risk score was 0.73" understands the decision but cannot verify the score, test alternatives, or produce counter-evidence. We introduce a taxonomy of contestation failures, showing that most accountability interventions address only one failure mode (opacity) while leaving four others unaddressed. Drawing on analysis of 168 legal cases spanning algorithmic decision-making contexts, we find that contestation faces a two-gate structure: a procedural gate (evidentiary access) and a doctrinal gate (substantive liability rules). Among litigated cases, those without evidence access almost never succeed (9%); those with access succeed at rates approaching 97% in domains without liability shields. Where doctrinal immunities apply (e.g., Section 230), even full evidentiary scrutiny produces no liability. This association almost certainly reflects selection effects; our empirical contribution is diagnostic rather than causal. The data identify where contestation fails among observable cases, not whether providing access would change outcomes for currently-excluded cases. We propose evidentiary rights as the missing procedural component, and develop counterfactual interrogation rights that allow affected parties to probe decision systems with modified inputs and observe whether outcomes change, without requiring disclosure of model internals. This reframes algorithmic accountability from a transparency problem to a procedural rights problem.

翻译：算法问责相关研究长期聚焦于解释，旨在帮助受影响方理解决策缘由。我们认为这种关注存在不足——若缺乏证据获取渠道，解释无法支撑有意义的质疑。当被告知"您的风险评分为0.73"时，当事人虽能理解该决策结果，却无法验证评分准确性、测试替代方案或提供反证。本文提出质疑失败的分类体系，揭示大多数问责措施仅解决一种失败模式（不透明性），而其他四种模式尚未被应对。通过对涵盖算法决策情境的168件法律案例进行分析，我们发现质疑面临双重屏障：程序屏障（证据获取）与法理屏障（实质性责任规则）。在诉讼案例中，无证据获取渠道的案件几乎全部败诉（9%胜诉率）；而在无责任豁免保护的领域，享有证据获取权的案件胜诉率接近97%。当适用法律豁免条款（如《美国通信规范法》第230条）时，即使进行充分证据审查也无法追究责任。这种关联性几乎必然反映选择效应；我们的实证贡献属于诊断性而非因果性。这些数据揭示了可观测案例中质疑失败的环节，而非证明提供证据渠道会改变当前被排除案例的结果。我们提出将证据权作为缺失的程序组件，并发展反事实质询权：允许受影响方通过修改后的输入参数探测决策系统，观察输出结果是否变化，而无需获知模型内部机制。这将算法问责从透明度问题重新定义为程序权利问题。