Understanding Diversity in Human-AI Data: What Cognitive Style Disaggregation Reveals

08/02/2021
by   Andrew Anderson, et al.
0

Artificial Intelligence (AI) is becoming more pervasive through all levels of society, trying to help us be more productive. Research like Amershi et al.'s 18 guidelines for human-AI interaction aim to provide high-level design advice, yet little remains known about how people react to Applications or Violations of the guidelines. This leaves a gap for designers of human-AI systems applying such guidelines, where AI-powered systems might be working better for certain sets of users than for others, inadvertently introducing inclusiveness issues. To address this, we performed a secondary analysis of 1,016 participants across 16 experiments, disaggregating their data by their 5 cognitive problem-solving styles from the Gender-inclusiveness Magnifier (GenderMag) method and illustrate different situations that participants found themselves in. We found that across all 5 cogniive style spectra, although there were instances where applying the guidelines closed inclusiveness issues, there were also stubborn inclusiveness issues and inadvertent introductions of inclusiveness issues. Lastly, we found that participants' cognitive styles not only clustered by their gender, but they also clustered across different age groups.

READ FULL TEXT

page 1

page 3

page 6

page 18

research
10/05/2022

On the Influence of Cognitive Styles on Users' Understanding of Explanations

Artificial intelligence (AI) is becoming increasingly complex, making it...
research
03/25/2023

Is It the End? Guidelines for Cinematic Endings in Data Videos

Data videos are becoming increasingly popular in society and academia. Y...
research
12/10/2020

Combined Intuition and Rationality Increases Software Feature Novelty for Female Software Designers

Overcoming society's complex problems requires novel solutions. Applying...
research
02/06/2022

From `Wow' to `Why': Guidelines for Creating the Opening of a Data Video with Cinematic Styles

Data videos are an increasingly popular storytelling form. The opening o...
research
05/07/2019

Fixing Inclusivity Bugs for Information Processing Styles and Learning Styles

Most software systems today do not support cognitive diversity. Further,...
research
07/19/2023

Mitigating Viewer Impact from Disturbing Imagery using AI Filters: A User-Study

Exposure to disturbing imagery can significantly impact individuals, esp...
research
09/19/2023

Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs

A moderately detailed consideration of interactive LLMs as cognitive sys...

Please sign up or login with your details

Forgot password? Click here to reset