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Found 21 papers, 8 papers with code
Conversational Assistants and Gender Stereotypes: Public Perceptions and Desiderata for Voice Personas
We then outline a multi-disciplinary project of how we plan to address the complex question of gender and stereotyping in digital assistants.
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Detection in Conversational AI
We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems.
Think Like a Person Before Responding: A Multi-Faceted Evaluation of Persona-Guided LLMs for Countering Hate
Automated counter-narratives (CN) offer a promising strategy for mitigating online hate speech, yet concerns about their affective tone, accessibility, and ethical risks remain.
The AI Gap: How Socioeconomic Status Affects Language Technology Interactions
Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to exacerbate the digital divide.
Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models
We ascribe these to cultural bias in LLMs and the scarcity of NLP literature on religion.
Language Model Council: Democratically Benchmarking Foundation Models on Highly Subjective Tasks
However, using a single LLM judge is prone to intra-model bias, and many tasks - such as those related to emotional intelligence, creative writing, and persuasiveness - may be too subjective for a single model to judge fairly.
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.
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
We then analyze the emotions generated by the models in relation to the gender-event pairs.
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.
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions
We then discuss four lacunae: (1) the absence of demographic and cultural aspects does not account for the variation in how emotions are perceived, but instead assumes they are universally experienced in the same manner; (2) the poor fit of emotion categories from the two main emotion theories to the task; (3) the lack of standardized EA terminology hinders gap identification, comparison, and future goals; and (4) the absence of interdisciplinary research isolates EA from insights in other fields.
Know Your Audience: Do LLMs Adapt to Different Age and Education Levels?
We find large variations in the readability of the answers by different LLMs.
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.
Computer says "No": The Case Against Empathetic Conversational AI
Emotions are an integral part of human cognition and they guide not only our understanding of the world but also our actions within it.
ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI
We find that the distribution of abuse is vastly different compared to other commonly used datasets, with more sexually tinted aggression towards the virtual persona of these systems.
Alexa, Google, Siri: What are Your Pronouns? Gender and Anthropomorphism in the Design and Perception of Conversational Assistants
Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems.
A Crowd-based Evaluation of Abuse Response Strategies in Conversational Agents
How should conversational agents respond to verbal abuse through the user?
An Ensemble Model with Ranking for Social Dialogue
Open-domain social dialogue is one of the long-standing goals of Artificial Intelligence.
A Review of Evaluation Techniques for Social Dialogue Systems
In contrast with goal-oriented dialogue, social dialogue has no clear measure of task success.
Why We Need New Evaluation Metrics for NLG
The majority of NLG evaluation relies on automatic metrics, such as BLEU .
