Artificial Intelligence (AI) has gained significant attention and is often hailed as a game-changing technology that can revolutionize various sectors, including healthcare, finance, and personal assistant services. However, navigating the nuances of real-world issues such as the COVID-19 pandemic has proven complex even for advanced AI like the Copilot chatbot. The chatbot’s responses reflect a mixture of recognized public sentiments, governmental messages, and popular narratives surrounding the pandemic. In this exploration of an AI’s capabilities, I compared its responses on COVID-related questions to that of the public’s understanding, revealing a broader dialogue about pertinent health concerns and the measures that were taken during the pandemic.
A conversation with the AI chatbot revealed a particular emphasis on life expectancy in America, which it stated was 79.25 years in 2024, slightly up from previous years. This statistic serves as a baseline for discussions about vulnerability, especially in the context of COVID-19. When asked about the average age of Americans who succumbed to the virus, Copilot indicated it was approximately 81.5 years. This is a crucial statistic that sheds light on the demographic most affected by the pandemic, primarily older adults with underlying health concerns. The AI communicated the importance of protecting these vulnerable populations, a sentiment echoed widely throughout the pandemic.
Delving deeper, I posed more challenging questions to the AI concerning the nature of mortality attributed to COVID-19, particularly focusing on whether the death of individuals aged 81.5 with pre-existing health conditions could be considered “premature.” The response revealed an attempt to convey sensitivity toward the complexities surrounding individual health circumstances, illustrating how subjective terms like “premature death” can be when discussing those who were likely on the decline due to existing medical issues. Although the AI attempted to balance different perspectives, the underlying implication was that the pandemic highlighted existing societal health challenges rather than standing as an independent crisis.
The chatbot was unable to pinpoint a specific percentage of healthy individuals under 70 years old who died from COVID-19, instead indicating that the overall risk is lower for those without underlying health issues. This gap in data is notable because it reflects a larger trend in public health discussions during the pandemic, which often polarized opinions on school closures and social restrictions. The lack of clear statistics can foster misinformation, which complicates discussions about public health measures. Further examining whether it was prudent to restrict the lives of young people during the height of the pandemic led to an exploration of the lasting impacts on education and mental health, particularly with measures like school closures.
The conversation also prompted the acknowledgment that while AI can simulate a conversational partner and provide information promptly, it lacks the personal experience and relationships that enrich human understanding. The Copilot AI, for instance, could relay the fact that some reasonably healthy individuals under 85 died from COVID-19, but it could not draw from any personal experiences to contextualize this information meaningfully. In contrast, my experiences indicated a much different narrative—one where I had not known anyone in good health who died from the virus, suggesting that anecdotes concerning the impact of COVID-19 can be deeply subjective and vary widely between individuals and communities.
Ultimately, the interaction with the AI underscores the challenges inherent in distilling public health issues through a binary lens. The portrayal of COVID-19, its victims, and the measures taken—or not taken—during the pandemic often reflects a broader societal narrative influenced by fear, politics, and media portrayals. The conversation illustrated the limitations of AI when addressing complex societal issues, particularly where personal experiences and anecdotal evidence clash with broader trends. As we continue to navigate the evolving landscape of health information, the discourse must go beyond raw data and statistics to encapsulate the nuanced experiences and reality of human lives affected by crises like the COVID-19 pandemic.
