A Study of Cognitive Justice Issues in Big Data Mining Behaviour

Abstract

随着大数据时代的降临，算法技术的正当性问题引发人们关注。区别于算法正义和数据正义，认知正义以认知主体为视角并具有法律意义，要求认知主体的认知水平与认知客体的可信度相匹配。实践中存在对大数据挖掘所获证据的证明力进行天然推定，证明标准难以对法官心证形成控制的现状。这是由于大数据挖掘基于相关关系的思维冲击了人们的认知惯性，将解释权交给了算法，降低了认知水平，算法黑箱的不透明性和难以检验性导致认知客体本不应当赋予过高的可信度，算法歧视导致难以对二者匹配的过程进行矫正。需要将内心确信符合信念标准、偏见性算法信息应仅发挥补强性作用、将大数据挖掘所获信息在辩护人阅卷中进行披露、破除相互印证的因果逻辑桎梏，从而回归于认知正义，走向法治而非“算法治”。 Abstract: With the emergence of the big data era, questions about the legitimacy of algorithmic technologies have arisen. Distinguished from algorithmic justice and data justice, cognitive justice takes the perspective of the cognitive subject and has legal significance, requiring the cognitive level of the cognitive subject to match the credibility of the cognitive object. In practice, there is a natural presumption of the probative power of evidence obtained by big data mining, and the standard of proof is difficult to control the judge’s mental evidence. This is due to the big data mining based on the correlation of thinking impacts the cognitive inertia of people, the interpretation of the right to the algorithm, reducing the level of cognition, the opacity of the black box of the algorithm and difficult to test leads to the cognitive object should not have been given too much credibility, the algorithmic discrimination leads to the difficulty of the process of correcting the matching of the two. There is a need to return to cognitive justice, towards the rule of law rather than the “rule of law”, by bringing inner certainty in line with the standard of conviction, biased algorithmic information should play only a reinforcing role, disclosure of the information obtained from big data mining in the reading of the defense case file and breaking the shackles of the causal logic of mutual corroboration.