# Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation

arXiv: https://arxiv.org/abs/2404.01070 · HTML: https://mlsanity.com/paper/2404.01070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://mlsanity.com/paper/1910.13267/) (2019)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://mlsanity.com/paper/2110.08527/) (2021)
- [Ethical Considerations for Machine Translation of Indigenous Languages: Giving a Voice to the Speakers](https://mlsanity.com/paper/2305.19474/) (2023)
- [The moral authority of ChatGPT](https://mlsanity.com/paper/2301.07098/) (2023)
- [On the Context-Free Ambiguity of Emoji](https://mlsanity.com/paper/2201.06302/) (2022)
- [Distributional Data Augmentation Methods for Low Resource Language](https://mlsanity.com/paper/2309.04862/) (2023)
- [Building a Parallel Corpus and Training Translation Models Between Luganda and English](https://mlsanity.com/paper/2301.02773/) (2023)
- [RoboTed: a case study in Ethical Risk Assessment](https://mlsanity.com/paper/2007.15864/) (2020)
- [Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer Models for Classifying Depression Severity in English and Luganda](https://mlsanity.com/paper/2401.14240/) (2024)

## Abstract (arXiv)

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.