Javed Kashif, Li Jianxin
School of Law, Zhengzhou University, Zhengzhou, 450001, Henan, China.
Heliyon. 2024 Apr 26;10(9):e30184. doi: 10.1016/j.heliyon.2024.e30184. eCollection 2024 May 15.
History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.
历史表明，由于认知偏差，人类社会在社会正义方面遭受了苦难。语义偏差往往会放大认知偏差。因此，广泛历史数据中存在的认知偏差可能会导致不道德且涉嫌不人道的预测，因为人工智能系统是基于这些数据进行训练的。人工智能的创新及其在各学科中的迅速整合引发了有关该技术主观性的问题。当前的研究聚焦于法律判决中的语义偏差，以提高训练数据的合法性。通过应用通用人工智能（AI）算法，我们对中国人工智能与法律（CAIL）数据集中存在的语义偏差进行分类和检测。我们的研究结果表明，与结构化专业风险评估工具相比，人工智能模型在由数百个案例组成的CAIL数据集中获得了卓越的预测能力。为在此过程中协助法律从业者，基于人工智能的创新方法可在法律领域内实施。为实现这一目标，我们提出了一种与法律判决中语义偏差的分类和识别相关的语义偏差分类模型。我们提出的模型以法律领域为例，结合CAIL数据集的识别进行分类。这将通过识别司法判决中的语义偏差来实现。我们使用了不同类型的分类器，如支持向量机（SVM）、朴素贝叶斯（NB）、多层感知器（MLP）和K近邻（KNN）来获得最优结果。SVM的准确率为96.90%，NB为88.80%，MLP为86.75%，KNN为85.66%，而SVM与其他模型相比准确率更高。此外，我们证明，仅基于决定案件结果的司法判决中的语义偏差分类来预测结果时，我们能够获得相对较高的分类性能。
