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Paper Type
ERF
Abstract
Developing natural language applications such as chatbots and intelligent assistants like Alexa, Siri and Cortana is currently a significant undertaking of many organizations who are seeking greater customer engagement. These applications rely on natural language that learns from human content which is often subject to systematic and universal biases such as race and gender stereotypes. These biases can be transferred to natural language processing applications which have been found to behave erratically in some instances. This unpredictability is linked to the increasing reliance humans place on the recommendations provided by these applications. There is a risk of humans circulating false information, which can mislead or amplify biases. This research proposes to investigate the impact of machine learning on human biases, such as gender and racism that have been systematically present since the emergence of the human corpus using design science research.
Recommended Citation
Manseau, Jasmin and Mbuko, Ikenna, "A Design Science Research to Correct Inherent Biases in Natural Language Applications" (2020). AMCIS 2020 Proceedings. 17.
https://aisel.aisnet.org/amcis2020/ai_semantic_for_intelligent_info_systems/ai_semantic_for_intelligent_info_systems/17
A Design Science Research to Correct Inherent Biases in Natural Language Applications
Developing natural language applications such as chatbots and intelligent assistants like Alexa, Siri and Cortana is currently a significant undertaking of many organizations who are seeking greater customer engagement. These applications rely on natural language that learns from human content which is often subject to systematic and universal biases such as race and gender stereotypes. These biases can be transferred to natural language processing applications which have been found to behave erratically in some instances. This unpredictability is linked to the increasing reliance humans place on the recommendations provided by these applications. There is a risk of humans circulating false information, which can mislead or amplify biases. This research proposes to investigate the impact of machine learning on human biases, such as gender and racism that have been systematically present since the emergence of the human corpus using design science research.
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