Adversarial Attacks on Fairness of Graph Neural Networks
Binchi Zhang, Yushun Dong, et al.
ICLR 2024
In this paper we discuss how AI can contribute to support the documentation and vitalization of Indigenous languages and how that involves a delicate balancing of ensuring social impact, exploring technical opportunities, and dealing with ethical constraints. We start by surveying previous work on using AI and NLP to support critical activities of strengthening Indigenous and endangered languages and discussing key limitations of current technologies. After presenting basic ethical constraints of working with Indigenous languages and communities, we propose that creating and deploying language technology ethically with and for Indigenous communities forces AI researchers and engineers to address some of the main shortcomings and criticisms of current technologies. Those ideas are also explored in the discussion of a real case of development of large language models for Brazilian Indigenous languages.
Binchi Zhang, Yushun Dong, et al.
ICLR 2024
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Paulo Cavalin, Luiz S. Oliveira
SIBGRAPI-T 2017
Claudio S. Pinhanez, Aaron F. Bobick
Personal and Ubiquitous Computing