Advancing AI with Integrity: Ethical Challenges and Solutions in Neural
  Machine Translation
Richard Kimera Yun-Seon Kim Heeyoul Choi
This paper addresses the ethical challenges of Artificial Intelligence in
Neural Machine Translation (NMT) systems, emphasizing the imperative for
developers to ensure fairness and cultural sensitivity. We investigate the
ethical competence of AI models in NMT, examining the Ethical considerations at
each stage of NMT development, including data handling, privacy, data
ownership, and consent. We identify and address ethical issues through
empirical studies. These include employing Transformer models for
Luganda-English translations and enhancing efficiency with sentence
mini-batching. And complementary studies that refine data labeling techniques
and fine-tune BERT and Longformer models for analyzing Luganda and English
social media content. Our second approach is a literature review from databases
such as Google Scholar and platforms like GitHub. Additionally, the paper
probes the distribution of responsibility between AI systems and humans,
underscoring the essential role of human oversight in upholding NMT ethical
standards. Incorporating a biblical perspective, we discuss the societal impact
of NMT and the broader ethical responsibilities of developers, positing them as
stewards accountable for the societal repercussions of their creations.