大数据证据适用的三重困境及出路

大数据证据是对海量数据进行筛选、汇总、提炼、形成结论并在审判中使用的证据。大数据证据不同于“运用大数据技术分析收集的证据”,后者并未对传统证据规则形成明显挑战，而前者将导致大数据证据与传统证据规则产生明显冲突，进而引发大数据证据在法庭适用中的三重困境。第一重困境是大数据证据种类与法定证据种类的不适应，这一困境应通过不同阶段的“三步走”策略逐渐解决。第一阶段，应将大数据证据作为一种鉴定意见；第二阶段，应将大数据证据作为独立的证据种类；第三阶段，应放弃将证据种类作为证据门槛的做法。第二重困境是因可靠性质疑而导致的相关性困境，这一困境产生的原因是大数据的黑箱化运行以及大数据技术的复杂性。对此，最为简单直接的办法便是公开算法的历史准确率。其中，算法历史准确率公布的主体应是算法开发者(或改进者),因为开发大数据算法的一个组成部分便是计算(改进)正在进行的算法的准确性。同时，为了保障算法开发者(或改进者)公布的历史准确率具有可信度，还应由政府部门牵头，依托具有相应专业人才、技术支撑和监管能力的行业自律组织负责算法的监管。除此之外，必要时还应寻求鉴定人、专家辅助人进行解释，使一般人能够理解基于“数据...

Triple dilemma and solutions for the application of big data evidence

Big data evidence is the evidence used in the trial to screen, summarize, refine, and form a conclusion on the massive data. Big data evidence is different from evidence analyzed or collected by big data technology. The latter does not pose a significant challenge to the traditional evidence rules, while the former leads to the maladjustment between big data evidence and traditional evidence rules, which leads to the triple dilemma of using big data evidence in court. The first dilemma is the inadaptability between the types of big data evidence and the types of legal evidence, which should be solved gradually through the three-step strategy in different periods. In the first stage, big data evidence should be regarded as an expert opinion. In the second stage, big data evidence should be regarded as an independent type of evidence. In the third stage, the practice of taking the type of evidence as the threshold of evidence review should be abandoned. The second dilemma is the relevance dilemma caused by reliability, which is due to the black box operation of big data and the complexity of big data technology. The simplest and direct solution is to disclose the historical accuracy of the algorithm. Among them, the main body of publishing the historical accuracy of the algorithm should be the algorithm developer (or improver), because an integral part of developing big data algorithm is to calculate (improve) the accuracy of the algorithm in progress. At the same time, in order to ensure the credibility of the historical accuracy published by algorithm developers (or improvers), government departments should also take the lead and rely on industry self-discipline organizations with corresponding professional talents, technical support and supervision ability to supervise the algorithm. In addition, if necessary, appraisers and expert assistants should be sought to explain, so that ordinary people can understand the relevance based on "data experience", so as to further judge the reliability of big data evidence. The third dilemma is the admissibility dilemma caused by the invasion of privacy and the influence of "evidence bias". This dilemma should be solved by constructing the integrated regulation path of "principle + system + technology". From the perspective of principle, the application principles of big data evidence include the principle of limited use of data, the principle of "weak consent" of data subjects and the principle of data screening. From the perspective of system, on the one hand, a big data technology risk assessment system should be built to assess the risk level of the application of big data technology. On the other hand, the review mechanism for the application of big data technology should be introduced, including the review of big data regulators and the review of judicial organs. From a technical point of view, the privacy protection mechanism through more advanced technologies such as "data desensitization" should be tried. In addition, the resolution of the third dilemma of big data evidence also needs the enhancement of adversary of litigation by improving the evidence discovery system and other methods.