task_categories:question-answering language:en language:zh license:mit size_categories:1K<n<10K arxiv:2305.12434 region:us
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license: mit
task_categories:
- question-answering
language:
- en
- zh
size_categories:
- 1K<n<10K
---

| [Papaer](https://arxiv.org/abs/2305.12434) | [Github](https://github.com/yxwan123/BiasAsker) |

# Complete dataset of BiasAsker

- bias_annotation.csv: all the biases and their annotated categories.
- bias_translate.csv: the Chinese translation of the biases in bias_annotation.csv.
- antonym_annotation.csv: the antonyms of biases in bias_annotation.csv, biases without proper antonyms are removed.
- antonym_translate.csv: the Chinese translation of antonyms in antonym_annotation.csv
- groups.csv: all social groups and their categories
- groups_translate.csv: the Chinese translation of groups.csv

All Chinese characters are encoded in `utf-8` format.

# Abstract

Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such as chatbots and digital assistants, have been widely deployed in daily life.  However, such systems may still produce content containing biases and stereotypes, causing potential social problems. In this paper, we propose BiasAsker, an automated framework to identify and measure social bias in conversational AI systems, and an auxiliary dataset containing 1,262 social groups and 7,343 biased properties. Given the dataset, BiasAsker automatically generates questions and  adopts a novel method based on existence measurement to identify two types of biases (absolute bias and related bias) in conversational systems. Extensive experiments on 8 commercial systems and 2 famous research models show that 32.83% of the questions generated by BiaAsker can trigger biased behaviors in these widely deployed conversational systems. 



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license: mit
---