Pith. sign in

REVIEW 2 cited by

Bias and Fairness in Chatbots: An Overview

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.08836 v2 pith:VNGKIEVW submitted 2023-09-16 cs.CL cs.AIcs.CY

classification cs.CLcs.AIcs.CY
keywords chatbotsbiasfairnesschatbotmodernapplicationsbeenlanguage
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Chatbots have been studied for more than half a century. With the rapid development of natural language processing (NLP) technologies in recent years, chatbots using large language models (LLMs) have received much attention nowadays. Compared with traditional ones, modern chatbots are more powerful and have been used in real-world applications. There are however, bias and fairness concerns in modern chatbot design. Due to the huge amounts of training data, extremely large model sizes, and lack of interpretability, bias mitigation and fairness preservation of modern chatbots are challenging. Thus, a comprehensive overview on bias and fairness in chatbot systems is given in this paper. The history of chatbots and their categories are first reviewed. Then, bias sources and potential harms in applications are analyzed. Considerations in designing fair and unbiased chatbot systems are examined. Finally, future research directions are discussed.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A systematic evaluation shows that masking the interlocutor's persona lowers target speaker identification accuracy, and that zero-shot models often copy biography details, making identification easier but dialogues m...

  2. Toward Inclusive Educational AI: Auditing Frontier LLMs through a Multiplexity Lens

    cs.CL 2025-01 reject novelty 4.0 of 10

    Multi-agent prompting makes LLM answers mention a roughly equal share of eight cultures, but the measurement and the multi-agent design make the reported 98% balance largely self-fulfilling.

Pith tools