"The Trouble with Bias" in Neural Networks: Conflict and Ethical Challenges

Abstract

This article is devoted to the analysis of ethical and conflict challenges related to the trouble with bias in neural networks. The necessity of a correct, scientifically based explication of the phenomenon of bias is postulated in order to build models correcting this problem as a necessary element of the software development process based on artificial intelligence algorithms. The history of the development of neural networks is considered from the origin of the idea of a mechanical organism to the construction of modern models of an artificial neuron. The most significant characteristics of modern neural networks are highlighted: architecture, weights and offsets, activation functions, inferences, and learning methods.A detailed description of natural language as a neural network learning resource is given, and programming in natural language is analyzed. The specificity of the natural language of the neural network as a set of linguistic practices reflecting the entire digitized experience of mankind, including stereotypes, inequalities, hate speech and other phenomena, ultimately producing the trouble with bias, is emphasized.Considerable attention is paid to the analysis of the phenomena of “politics classification”, “power discourse”, “cultural violence” in the context of the search for methodological foundations of natural language filtering and censorship strategies in the process of constructing a neural network.Separately, it is emphasized how the errors in neural networks are reflected in the existing ethical and conflict studies debates around the problem of artificial intelligence. It is concluded that the current assessment of the moral aspects of the problem does not imply granting neural networks the status of a moral agent and places the ethical expertise of the product on its developers. It is particularly noted that the conflict aspect of the trouble with bias lies in its recognition exclusively in relation to groups that have now acquired the “sensitive” status of discriminated against as a result of social conflicts.In conclusion, the paper substantiates the urgent need to optimize artificial intelligence in order to reduce the destructive potential of the trouble with bias, which necessarily implies the modification of social relations in the broader context of the struggle of excluded groups for the right to be recognized as discriminated against.