Practical Tools from Domain Adaptation for Designing Inclusive, Equitable, and Robust Generative AI

Abstract

Generative language technologies have become integral to everyday communication, shaping social interactions and informing critical decision-making processes in areas such as recruitment, healthcare, and education. However, they often struggle to grasp the "long tail" of data distributions -concepts less frequently observed during training -which could have significant repercussions. These models may marginalize underrepresented groups by failing to comprehend preferred communication styles, such as code-switching, or perpetuating societal biases like gender bias. Sectors like healthcare, education, and law, requiring personalization and exhibiting nuanced linguistic features, are also particularly affected when pre-trained models misconstrue or overlook "long tail" data concepts. While methods like distillation of smaller language models, active learning, and other bias mitigation strategies can augment traditional training techniques, a careful statistical analysis is essential for their effective application. This tutorial offers a comprehensive examination of how to develop equitable, robust, and inclusive language technologies using statistical tools from Domain Adaptation (DA) that catalyze positive social change. We will delve into strategies for bias mitigation, explore how to measure bias, and examine open problems in creating culturally-grounded and inclusive language technologies. Accompanying code notebooks and packages will be provided. 1 Baber Khalid, Malihe Alikhani, and Matthew Stone.2020b. Combining cognitive modeling and reinforcement learning for clarification in dialogue. In Proceedings of the 28th International Conference on Computational Linguistics.