Publication Date

6-25-2025

Document Type

Conference Proceeding

Publication Title

Cods Comad 2024 Proceedings of the 8th Jpint International Conference on Data Science and Management of Data

DOI

10.1145/3703323.3704279

First Page

353

Last Page

357

Abstract

Artificial Intelligence (AI) has attained human-level performance in tasks like text summarization, machine translation, and code generation while handling multimodal data. Despite these advances, AI poses challenges such as generating false or biased outputs, causing discrimination, and exhibiting opaque decision-making processes that hinder bias mitigation. Additionally, training large language models (LLMs) for AI consumes substantial energy and can enable autonomous warfare, posing a threat to eco-justice, and raising sustainability concerns. Their ability to create realistic content poses the risk of misuse and perpetuating misinformation. This article addresses these ethical issues and presents solutions through regulation and technology, with examples of global legislation tackling algorithmic discrimination and consumer rights. It emphasizes the need for a workforce skilled in navigating an AI-driven world. Future research directions touch upon mechanistic interpretability to understand AI outputs, new approaches to natural language processing and multimodal data understanding, and developing more interpretable neural network architectures with fewer parameters. This article summarizes the tutorial on the topic that includes some of the author’s work in related areas.

Keywords

Environment, Generative AI, Sustainability

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Applied Data Science

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