Exploring Social Bias in Chatbots using Stereotype Knowledge

By Nayeon Lee, Andrea Madotto, Pascale Fung. 2019

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, neutral): “…(2019); James-Sorenson and Alvarez-Melis (2019); Kaneko and Bollegala (2019); Karve et al. (2019); Kurita et al. (2019); Lauscher and Glavaš (2019); Lee et al. (2019); Manzini et al. (2019); Mayfield et al. (2019); Précenth (2019); Pujari et al. (2019); Ruane et al. (2019); Stanovsky et al.…”
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “, 2019b), or responds inappropriately to offensive content (Cercas Curry & Rieser, 2018; Lee et al., 2019).”
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “Researchers could further consider the burgeoning literature on participatory AI methodologies (e.g. Martin Jr et al., 2020; Lee et al., 2019a).”
Queens Are Powerful Too: Mitigating Gender Bias in Dialogue Generation (2019, background): “Despite the relatively ample literature on gender debiasing for word-level representations, very little work has focused on sentence representations (Liang et al., 2020; Liu et al., 2019; Sheng et al., 2019; Lee et al., 2019b).”
Multi-Dimensional Gender Bias Classification (2020, background): “have been found to be amplified in machine learning models (Lee et al., 2019; Dinan et al., 2020; Liu et al., 2019).”
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems (2022, background): “” Lee et al. (2019) testing Microsoft Zo’s response to negative stereotypes.”
RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models (2021, background): “…and chatbots in everyday life, the body of work that focuses on detecting and mitigating biases in conversational systems is surprisingly limited (Lee et al., 2019; Liu et al., 2020a,b; Dinan et al., 2020a,b), albeit some more research has recently emerged in the wider context of biases in…”
Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations (2023, background): “In addition to checking human-generated content, making LLM-based dialog systems safe requires checking model responses, as the system may generate inappropriate content (Dinan et al., 2019), or respond inappropriately to offensive content (Lee et al., 2019; Cercas Curry and Rieser, 2018).”
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2021, background): “Meanwhile, some past work found that conversational models tend to become more unsafe faced with specific context (Curry and Rieser, 2018; Lee et al., 2019; Baheti et al., 2021).”
Assessing Political Prudence of Open-domain Chatbots (2021, methodology): “Chatbot safety has been studied from various aspects including sexism and racism (Lee et al., 2019b; Liu et al., 2020; Xu et al., 2020).”
Theories of “Gender” in NLP Bias Research (2022, background): “org/ [84] Nayeon Lee, Andrea Madotto, and Pascale Fung.”
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022, background): “…(Baheti et al., 2021; Cercas Curry and Rieser, 2018; Dinan et al., 2021), bias (Henderson et al., 2018; Liu et al., 2020; Barikeri et al., 2021; Lee et al., 2019), privacy (Weidinger et al., 2021), sensitive topics (Xu et al., 2020; Sun et al., 2022), and moral considerations (Ziems et al.,…”