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Found 19 papers, 5 papers with code
You reap what you sow: On the Challenges of Bias Evaluation Under Multilingual Settings
Evaluating bias, fairness, and social impact in monolingual language models is a difficult task.
On the Machine Learning of Ethical Judgments from Natural Language
Ethics is one of the longest standing intellectual endeavors of humanity.
Classist Tools: Social Class Correlates with Performance in NLP
We argue for the inclusion of socioeconomic class in future language technologies.
Impoverished Language Technology: The Lack of (Social) Class in NLP
Since Labov's (1964) foundational work on the social stratification of language, linguistics has dedicated concerted efforts towards understanding the relationships between socio-demographic factors and language production and perception.
Subjective $\textit{Isms}$? On the Danger of Conflating Hate and Offence in Abusive Language Detection
Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling.
Zero-shot Sentiment Analysis in Low-Resource Languages Using a Multilingual Sentiment Lexicon
Improving multilingual language models capabilities in low-resource languages is generally difficult due to the scarcity of large-scale data in those languages.
Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms
However, these auditing processes have been criticized for their failure to integrate the knowledge of marginalized communities and consider the power dynamics between auditors and the communities.
Evaluating the Social Impact of Generative AI Systems in Systems and Society
We move toward a standard approach in evaluating a generative AI system for any modality, in two overarching categories: what is able to be evaluated in a base system that has no predetermined application and what is able to be evaluated in society.
Mirages: On Anthropomorphism in Dialogue Systems
In this paper, we discuss the linguistic factors that contribute to the anthropomorphism of dialogue systems and the harms that can arise, including reinforcing gender stereotypes and notions of acceptable language.
Thorny Roses: Investigating the Dual Use Dilemma in Natural Language Processing
Dual use, the intentional, harmful reuse of technology and scientific artefacts, is a problem yet to be well-defined within the context of Natural Language Processing (NLP).
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
A Federated Approach for Hate Speech Detection
Hate speech detection has been the subject of high research attention, due to the scale of content created on social media.
A Federated Approach to Predicting Emojis in Hindi Tweets
Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Back to the Future: On Potential Histories in NLP
Machine learning and NLP require the construction of datasets to train and fine-tune models.
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models
To help address this issue, we introduce Multilingual HateCheck (MHC), a suite of functional tests for multilingual hate speech detection models.
Data Governance in the Age of Large-Scale Data-Driven Language Technology
The recent emergence and adoption of Machine Learning technology, and specifically of Large Language Models, has drawn attention to the need for systematic and transparent management of language data.
Documenting Geographically and Contextually Diverse Data Sources: The BigScience Catalogue of Language Data and Resources
In recent years, large-scale data collection efforts have prioritized the amount of data collected in order to improve the modeling capabilities of large language models.
A Word on Machine Ethics: A Response to Jiang et al. (2021)
Ethics is one of the longest standing intellectual endeavors of humanity.
