Ethical Challenges in the Development of Virtual Assistants Powered by Large Language Models

Author:

Piñeiro-Martín Andrés12ORCID,García-Mateo Carmen2ORCID,Docío-Fernández Laura2ORCID,López-Pérez María del Carmen2

Affiliation:

1. Balidea Consulting & Programming S.L., Witland Building, Camiños da Vida Street, 15701 Santiago de Compostela, Spain

2. GTM Research Group, AtlanTTic Research Center, University of Vigo, Maxwell Street, 36310 Vigo, Spain

Abstract

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs), such as ChatGPT, has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.

Funder

Galician Innovation Agency

Consellería de Cultura, Educación, Formación profesional e Universidades of the Xunta de Galicia

Centro singular de investigación de Galicia

Axudas para a consolidación e estructuración de unidades de investigación competitivas do Sistema Universitario de Galicia

European Regional Development Fund-ERDF

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

Reference55 articles.

1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. arXiv.

2. Howard, J., and Ruder, S. (2018). Universal language model fine-tuning for text classification. arXiv.

3. Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2023, July 18). Improving Language Understanding by Generative pre-Training. Available online: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.

4. Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv.

5. Geramifard, A. (2023, July 18). Project CAIRaoke: Building the Assistants of the Future with Breakthroughs in Conversational AI. Available online: https://ai.facebook.com/blog/project-cairaoke/.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3