Explicit And Implicit Bias Analysis Based On Deep Learning Method

In the field of psychology,prejudice refers to the user’s tendency to view specific things.Accurately identifying and analyzing the biases of individuals or groups is not only of scientific significance in the field of psychology,but also an important basis for downstream research in the field of artificial intelligence,including personalized recommendation,intelligent dialogue,and social computing.Specifically,prejudice can be divided into explicit bias and implicit bias.Among them,explicit prejudice refers to the prejudice that people express directly in public,and implicit prejudice refers to the real prejudice in people’s hearts.Psychological research shows that there can often be differences between the explicit and implicit biases of individuals or groups,which brings great challenges to the identification and analysis of bias,especially the identification and analysis of implicit bias.In the field of psychology,the implicit association test(IAT)is the authoritative method for measuring implicit bias.However,the implicit association test can only be completed in the laboratory through the active cooperation of the subjects,which greatly limits the use of this method in large-scale populations.In recent years,based on artificial intelligence methods,especially natural language processing technology,discovering people’s prejudices from the speech information of individuals or groups has become a hot issue in several research fields,including artificial intelligence ethics.The existing methods mainly rely on the semantic distance between vocabulary-level representation learning and the semantic representation of the vocabulary to try to simulate the implicit association test in the field of psychology,and have made certain progress.However,the current bias recognition and analysis methods based on natural language processing technology still have some shortcomings.First of all,the existing methods essentially do not distinguish between explicit and implicit biases.This article reveals that the biases identified by the existing bias recognition methods based on natural language processing are a mixed result of explicit and implicit biases.Secondly,prejudice expression mainly exists at the sentence level,while current methods mainly apply vocabulary-level representation learning techniques,but fail to tap the potential of sentence-level language representation learning in the analysis of explicit and implicit bias recognition.Although some methods have explored sentence-level bias recognition methods including BERT,they essentially construct sentence semantics around individual words in the sentence,instead of exploring sentences that contain both the object of bias and the attributes of bias,that is,it contains The effect of the expression learning of the sentence of complete prejudice expression on the analysis of prejudice.In order to solve the above problems,the work of this article is mainly reflected in the following four aspects:(1)The relationship between psychological measurement and natural language technology: This article first attempts to establish the relationship between psychological implicit association test elements and language elements from a theoretical level,by proposing concept words and attribute words and their combination and expression in expressions.The stimuli,attributes,and the relationship between semantic association and implicit association distance in the implicit association test provide a theoretical basis for language-based explicit and implicit bias testing.(2)Distinguishing between explicit and implicit biases based on vocabulary-level representation learning: Based on the current bias analysis method based on vocabulary-level representation learning,based on the aforementioned association analysis,this paper proposes an automatic measurement by reasonably dividing the corpus Vocabulary-level measurement methods for explicit and implicit biases.(3)Distinguishing between explicit and implicit biases based on sentence-level representation learning: This paper constructs expression sentences composed of concept words and attribute words through different relationships,and uses cuttingedge sentence-level representation learning methods to model the semantics of expressions,and then achieve sentence-level The method of measuring explicit and implicit biases.(4)Verification of the Measurement Method of Explicit and Implicit Bias Based on Deep Learning: In the experiment,this paper verifies the effectiveness of this method by qualitatively comparing the automatic measurement results with the psychological measurement results.At the same time,this paper observes the time-series evolution law of the measured explicit and implicit biases,and draws a new analytical conclusion with certain scientific significance,that is,implicit biases have higher time-series stability than explicit biases.