Author
Listed:
- A. N. Sunami
- A. I. Musaev
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.
Suggested Citation
A. N. Sunami & A. I. Musaev, 2024.
"«The Trouble with Bias» in Neural Networks: Conflict and Ethical Challenges,"
Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 5.
Handle:
RePEc:acf:journl:y:2024:id:2601
DOI: 10.22394/1726-1139-2024-5-150-158
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:acf:journl:y:2024:id:2601. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://sziu.ranepa.ru .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.