Geo-Cultural Representation and Inclusion in Language Technologies (2024)

Abstract: Training and evaluation of language models are increasingly relying on semi-structured data that is annotated by humans, along with techniques such as RLHF growing in usage across the board. As a result, both the data and the human perspectives involved in this process play a key role in what is taken as ground truth by our models. As annotation tasks are becoming increasingly more subjective and culturally complex, it is unclear how much of their socio-cultural identity annotators use to respond to tasks. We also currently do not have ways to integrate rich and diverse community perspectives into our language technologies. Accounting for such cross-cultural differences in interacting with technology is an increasingly crucial step for evaluating AI harms holistically. Without this, the state of the art of the AI models being deployed is at risk of causing unprecedented biases at a global scale. In this tutorial, we will take an interactive approach by utilizing some different types of annotation tasks to investigate together how our different socio-cultural perspectives and lived experiences influence what we consider as appropriate representations of global concepts.

## introduction increasingly, researchers and engineers are relying on human annotation to train, develop, and shape language models. however, as language models are being integrated into global systems of social and cultural importance such as search, education, and even creativity, the annotation tasks veer into increasingly culturally subjective questions of evaluating representation, toxicity, abusive language, stereotyping, and more. measuring such representational quality of generated content requires significant culturally situated expertise and nuanced judgment on specific signifiers and social connotations of language @xcite . who you ask and how you ask them also changes the content of such subjective evaluations @xcite . to highlight this contingency of our existing evaluation methods, in this tutorial we will work through the following questions together: ## tutorial structure we have structured the tutorial into the following parts. each part will be interactive and we will encourage questions throughout the tutorial. we will also keep aside at least the last 7 minutes of each of the following sessions to be just for q/a. part 1 -context and motivation [45 mins] we will begin with a short, introductory talk by the presenters where we will motivate the problem setup and give examples of how cultural subjectivity and expertise can shape evaluation outcomes. we will also demonstrate how these differences impact what is treated as 'ground truth' by our ai pipelines. specifically, in tasks that check for model safety and beneficence, these discrepancies can lead to representational as well as quality of service harms. part 2: live rater annotation [45 mins] this segment of the tutorial will be extremely hands on, and aimed at investigating together how our experiences shape the way we annotate presence or absence of certain features in text or image data points. the task will be shared through a web link during the tutorial. the total time for this segment will be split in the following way: ## target audience the target audience for this could be nlp researchers, engineers, and practitioners at any career stage. they could be actively using annotated data to rain or evaluate models, or creating the datasets for these purposes. with the discussions and exercises at the tutorial, they will collectively reflect on the range of impacts each rater assumption and rating task structure choice has. prerequisite knowledge: no specific prerequisite knowledge is needed. however, a general knowledge of data annotations and/or evaluation tasks in nlp could be helpful. equipment needed: venue with wifi so participants can engage with material. attendees are recommended to bring their laptops for better experience. ## diversity statement the topic of the tutorial is very tightly linked with the mission of diverse representation of people in nlp. the tutorial highlights how differing lived experiences across the globe impact what is 'ground truth' in data annotations for different people. unilateral decisions or tasks only considering majority over categorical ratings do not do justice to the subjective tasks that llms built on these datasets perform. through this tutorial we will elaborate the importance of global inclusion into nlp technologies for equitable model development and deployment. 6 other information the presenters have experience introducing and leading discussions on cultural considerations in ai pipelines. some other venues where we have coorganized and conducted tutorials and workshops with a similar goals include facct 2023 (tutorial on cross cultural considerations in ai; 50 attendees), eacl 2023 (cross cultural considerations in nlp workshop; 75 attendees), neurips 2022 (cultures in ai workshop; 50 attendees), cvpr 2023 (ethical considerations in creative applications of computer vision). with this track record of successful events on this theme at multiple venues, we expect a similar range of attendees at coling. we will also be advertising the tutorial through multiple channels including social media, and mailing lists. ## ethics statement this workshop will help draw attention towards the ethics of globally deploying models which incorporate world views of only few parts of the world, both in its training and evaluations. it will urge deeper reflections of how each data instance that we use to build or evaluate a model can have different interpretations by different people and communities globally. by doing so, this tutorial will be actively fighting against further marginalizations or erasure of people from different communities and cultures.