Summary of Advancing Ai with Integrity: Ethical Challenges and Solutions in Neural Machine Translation, by Richard Kimera et al.
Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation
by Richard Kimera, Yun-Seon Kim, Heeyoul Choi
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the ethical considerations of Artificial Intelligence (AI) in Neural Machine Translation (NMT) systems. The authors investigate the fairness and cultural sensitivity of AI models in NMT, examining data handling, privacy, data ownership, consent, and other ethical issues at each stage of development. They employ Transformer models for Luganda-English translations, refine data labeling techniques, fine-tune BERT and Longformer models, and conduct empirical studies to identify and address these ethical concerns. The paper also reviews the literature on NMT from databases like Google Scholar and platforms like GitHub, underscoring the importance of human oversight in ensuring ethical standards are upheld. Furthermore, the authors discuss the societal impact of NMT and the broader ethical responsibilities of developers as stewards accountable for their creations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how Artificial Intelligence (AI) is used in machine translation to ensure that it’s fair and respectful to different cultures. The authors want to make sure AI systems don’t unfairly favor one group over another, or hurt people’s feelings by not understanding cultural differences. They’re looking at what makes these AI systems work, what kind of data they use, and how they can be improved. They also review what other researchers have said about this topic. The authors think that humans should always be in control to make sure AI systems are used responsibly and don’t cause harm. |
Keywords
» Artificial intelligence » Bert » Data labeling » Transformer » Translation
