A field study of the impact of gender and user's technical experience on the performance of voice-activated medical tracking application

By James A. Rodger, Parag C. Pendharkar. 2004

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jennifer Wortman Vaughan
2. Jiliang Tang
3. Haochen Liu
4. Hanna M. Wallach
5. Zitao Liu
6. Hui Liu
7. Solon Barocas
8. Ece Kamar

In Information Systems and Management

1. Hanna M. Wallach
2. Meredith Ringel Morris
3. Solon Barocas
4. Adrian Weller

In Human-Computer Interaction

1. Meredith Ringel Morris
2. Ece Kamar
3. Hanna M. Wallach

Citations in the corpus

• Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs (2021, background): “However, several pieces of work have demonstrated that AI systems can perform differently for different groups of people, often exhibiting especially poor performance for already-disadvantaged groups (e.g., Nicol, Casey, and MacFarlane 2002; Rodger and Pendharkar 2004; Tatman 2017; Buolamwini and Ge”
• Does Gender Matter? Towards Fairness in Dialogue Systems (2019, background): “However, they have been proven to be unfair in many real-world applications such as computer vision [1], audio processing [2] and recommendations [3].”
• Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning (2020, background): “A variety of AI applications have demonstrated common prejudices towards particular groups of people (Rodger and Pendharkar, 2004; Howard and Borenstein, 2018; Rose, 2010; Yao and Huang, 2017; Tolan et al., 2019).”
• The Ugly Truth About Ourselves and Our Robot Creations: The Problem of Bias and Social Inequity (2017, background): “According to a study by Rodger and Pendharkar (2004), medical voice-dictation software was found to more accurately recognize voice input from a man versus a woman.”
• Designing an Online Infrastructure for Collecting AI Data From People With Disabilities (2021, background): “Such studies have investigated how one’s gender [10, 34, 42, 47, 50], race [10, 34], and socioeconomic status [1] could negatively afect the performance of an AI system such as facial recognition or natural language processing systems.”