Learning About Public Perceptions of AI Use During the COVID-19 Pandemic
An exploratory research project investigating public perceptions of Artificial Intelligence during the COVID-19 pandemic, uncovering insights into its potential, limitations, and ethical implications to inform user-centered AI applications in healthcare and crisis management. [Link to Full Paper] [PDF]
Introduction
The emergence of COVID-19 led to a global crisis, disrupting lives and straining healthcare systems. Amid this turmoil, Artificial Intelligence (AI) emerged as a potential ally in mitigating the pandemic’s impact through innovations in healthcare. However, despite its potential, the human perception of AI during the crisis remained underexplored.
Motivated by the scarcity of interview-based research in AI literature, this study sought to delve into how people perceived AI's utilization during the pandemic. Using semi-structured interviews, I aimed to bridge the gap between technology and human experience, uncovering valuable insights to inform future AI solutions.
Objectives
To explore participants’ thoughts and experiences regarding AI utilization during the COVID-19 pandemic.
To identify recurring patterns and themes in public perceptions to guide the development of user-centric AI solutions.
Role and Collaboration
As the Lead Researcher, I spearheaded this project, designing and conducting interviews and analyzing qualitative data. I collaborated with a team of experts from the University of Illinois at Urbana-Champaign, integrating healthcare and AI domain expertise into the study framework.
Research Methods
1. Semi-Structured Interviews
Fifteen participants were recruited via social media platforms (Facebook, Instagram, Nextdoor, and Reddit). Interviews were conducted remotely, with audio recorded for 13 participants and transcribed via Otter.ai, while notes were taken for two who declined recording. Participants spanned diverse demographics and professions, such as undergraduate students, data scientists, project managers, and acupuncturists.
Key questions explored:
“What do you think AI actually means?”
“What experiences have you had with AI?”
“How do you think AI can help with the pandemic?”
“Do you have concerns about AI, and what are they?”
2. Thematic Analysis
An inductive thematic analysis approach was used to process the data. The coding was performed collaboratively, ensuring consistency through “intercoder agreement checks to ensure code alignment.” Themes were developed through iterative discussions among team members.
The Process: Insights and Discoveries
1. Setting the Stage
The pandemic created an urgent need to understand how AI could be applied to mitigate healthcare challenges. Participants’ responses ranged from optimistic views about AI's potential to concerns about its feasibility, especially in healthcare.
2. Uncovering Participant Experiences
The interviews revealed varied perceptions. While all 15 participants could articulate how AI might help, the responses ranged from enthusiastic endorsements to serious doubts:
Optimism About AI's Role in Testing and Monitoring: Participants highlighted AI’s potential for reducing human-to-human contact during tests and improving diagnostic efficiency. For example, Participant 6 stated, “It would be beneficial I’m sure if these things can figure it out, even give a test for that or something.” Participant 3 added, “They can somehow do the tests without needing to actually be there and like for very long without having to expose another human to the virus.”
Concerns About AI's Adaptability: Four participants voiced skepticism about AI's ability to manage the complexities of healthcare, such as varying patient conditions and medication regimens. Participant 5 remarked, “I could see some issues with that, just based off the fact that I’m not sure how AI can adjust to patients who are overweight, who have different kinds of conditions…depending on the medications they’re taking.”
Ethical and Privacy Considerations: Participants debated the trade-offs between AI’s utility and privacy concerns. While some supported location tracking to manage the virus's spread, others raised concerns about data misuse. Participant 1 described phone-tracking as a way to “see if you’re outside,” while others supported “AI tracking packages or tollway data” to monitor movement (Participant 13).
Key Themes Identified
Optimism About Efficiency: AI was seen as a tool for “eliminating human-to-human contact” (Participant 2) and accelerating processes such as clinical trials (Participant 8).
Concerns About Accuracy and Feasibility: Participants expressed doubts about AI’s readiness for “serious consideration at international and national policy levels,” particularly in managing diverse healthcare needs.
Mixed Reactions to Ethical Implications: While participants acknowledged the benefits of tracking technologies, they emphasized the need for transparency and regulation.
Conclusion: Reflections on the Journey
This research highlighted the duality of public perception: while participants acknowledged AI’s potential, concerns about feasibility and ethics tempered their enthusiasm. Despite this, “all 15 participants either strongly or somewhat supported AI development in healthcare, even if they felt suspicious of it.”
By providing a user-centric lens, this study underscores the importance of addressing public concerns in AI design. It also offers a foundation for future research, emphasizing that public support for AI hinges on its transparency, adaptability, and ethical application. As Participant 2 noted, “We are set that we’re not putting, you know, the healthcare workers at risk...because we were eliminating that human-to-human contact.”
Through this process, I contributed to bridging the gap between technological advancements and human concerns, advocating for thoughtful AI solutions that resonate with users’ values. This study serves as a starting point for ensuring AI’s integration into healthcare aligns with public expectations and needs.