Public Health Credibility 9-7-25

During the COVID-19 pandemic, public health credibility was undermined by a combination of evolving scientific guidance, unprecedented misinformation, and intentional disinformation. While not yet a major driver of misinformation in 2020, AI began to play a significant role in fueling these credibility problems soon after by generating and spreading false content.  

Challenges to public health credibility in 2020

Public health agencies faced numerous challenges to their credibility throughout 2020, many of which created an environment ripe for AI-driven issues to emerge later:

·       Rapidly changing guidance: As the scientific community learned more about the novel coronavirus, recommendations on issues like mask-wearing and social distancing changed. Public health authorities struggled to communicate this evolving understanding transparently, which created confusion and eroded public trust.

·       The "infodemic" of misinformation: The World Health Organization (WHO) coined the term "infodemic" to describe the overabundance of false and misleading information that spread during the pandemic. Misinformation was rampant on social media, promoted ineffective or dangerous treatments, and discouraged people from following public health measures.

·       Lack of clear, consistent communication: Many public health campaigns failed to deliver simple, culturally congruent messages delivered by trusted local messengers. Instead, they relied on top-down, academic-style communication that did not resonate with many communities.

·       Political polarization: Health-related messaging became intensely politicized, particularly in the United States. During 2020, this led to partisan-influenced interpretations of the virus's threat and differing adherence to public health recommendations.

·       Pre-existing distrust: Decades of distrust in health institutions, often rooted in historical bias and inequitable care for marginalized populations, deepened public skepticism during the pandemic. For these communities, misinformation spread more easily. 

The emerging role of AI in 2020 and its future impact

In 2020, advanced generative AI was not yet widely accessible to the public. However, AI was already at work behind the scenes in ways that affected public health information and laid the groundwork for future credibility problems. 

How AI systems contributed to the 2020 infodemic:

·       Social media algorithms: AI-powered algorithms on social media platforms played a key role in the pandemic's "infodemic." By promoting sensational or engaging content—regardless of its factual basis—AI accelerated the spread of misinformation.

·       Malicious bots and accounts: State-owned or financially motivated actors used bots and automated accounts to generate millions of false social media posts. This targeted campaign used AI to create and amplify misinformation, though the full scope of AI's role was often unclear at the time.

·       AI-driven disinformation tactics: Even in 2020, malicious actors were using AI tools to quickly create and translate false stories to spread disinformation globally. This demonstrated how AI could scale the creation of harmful narratives. 

AI issues that surfaced after 2020 and originated in that era:

·       Reinforcing existing biases: Many AI models developed to aid the pandemic response were trained on flawed, incomplete, or biased data from 2020. Key demographic information like race and ethnicity was often missing from these datasets. This lack of data transparency created the risk that these AI tools could perpetuate existing health inequities, a problem that came to light later.

·       Over-reliance and false confirmation: The pandemic accelerated the use of AI in healthcare decision-making, such as predicting case loads. A risk called "false confirmation" can occur when an AI reinforces an incorrect decision made by a human. This is especially dangerous when dealing with new pathogens like SARS-CoV-2 and highlights the risks of over-reliance on emerging technologies.

·       Generative AI's future disinformation role: The massive wave of COVID-19 misinformation in 2020 provided a perfect training ground for malicious actors to weaponize new AI tools later on. Post-2020, generative AI made it possible to produce convincing and sophisticated misinformation on a vast scale, including "hallucinated" medical details and deepfake audio and video. 

During the COVID-19 pandemic, public health credibility was undermined by the rapid spread of misinformation, the politicization of science, and outdated technological infrastructure. Though still in its early stages, Artificial Intelligence (AI) emerged as both a contributor to and potential solution for these credibility problems. 

Factors challenging public health credibility

Public health faced unprecedented challenges during the pandemic that eroded public trust. 

·       The "infodemic": This "overabundance of information—some accurate and some not" was driven primarily by social media. The prevalence of health misinformation was staggering, with one study showing that 0.2% to 28.8% of COVID-19-related social media posts contained misleading or false information. Examples included conspiracy theories about the virus's origins, false remedies, and unproven treatments like hydroxychloroquine.

·       Politicization and mixed messaging: In 2020, political leaders frequently contradicted or downplayed public health guidance, creating mass confusion. This led to a loss of public trust in federal agencies and public health officials, who were often subjected to harassment and attacks.

·       Systemic inequities and mistrust: The pandemic exposed and worsened long-standing health disparities. Historically marginalized communities experienced higher rates of infection, hospitalization, and mortality but lacked trust in a public health system that had perpetuated injustices.

·       Outdated technology: Many public health institutions relied on obsolete information systems that could not handle the scale of the pandemic. Data sharing was inconsistent between different levels of government, creating a fragmented response. Examples include data glitches and backlogs of hundreds of thousands of test results in 2020. 

AI's role in the public health landscape of 2020

In 2020, AI's role was two-fold. While still an emerging tool in public health, it both exacerbated the credibility crisis and began to offer potential solutions. 

How AI exacerbated public health credibility problems:

·       Fueling the infodemic: AI's foundational role in social media algorithms amplified the spread of misinformation. These algorithms prioritize engaging content, and studies show that misleading or emotionally charged content often spreads faster than factual information.

·       Bias in data and algorithms: Even well-intentioned AI applications could be biased. If trained on non-representative or historically flawed health data, AI models could replicate and even worsen existing health inequities. An algorithm used in a study from that period assigned the same risk level to Black and white patients, even though the white patients were less sick—leading to inadequate care for Black patients. 

How AI offered potential solutions for public health:

·       Real-time surveillance and forecasting: AI was quickly adapted to analyze massive datasets from social media and electronic health records to track the spread of COVID-19. Tools like HealthMap used AI to forecast the virus's spread in real-time, assisting public health officials with early detection and response planning.

·       Combating misinformation: AI was used by social media companies and other organizations to identify and curb the spread of misinformation. AI-powered chatbots from organizations like the World Health Organization (WHO) helped provide instant, reliable information to the public, guiding them toward credible resources.

·       Operational improvements: AI helped with resource allocation during the vaccination campaigns by analyzing demographic and geographic data to identify the best locations for vaccination sites.

·       Medical breakthroughs: AI accelerated drug discovery and vaccine development. Machine-learning algorithms helped analyze vast viral genomic data to identify potential vaccine targets in a fraction of the time it would have taken human researchers. 

The path forward

By 2020, it was clear that addressing the public health credibility crisis would require a multi-pronged approach that included the strategic and ethical use of technology. This would involve investing in robust data infrastructure, developing clear ethical frameworks for AI, prioritizing health equity and rebuilding public trust through transparent communication. 

https://www.google.com/search?q=public+health+credibility+problems+2020

In early 2020, Dr. Anthony Fauci's public health guidance evolved as scientific understanding of the COVID-19 pandemic progressed. However, these changing recommendations—particularly concerning masking, the virus's origin, and social distancing—created confusion and eroded public trust. 

Timeline of key guidance and criticisms

February 2020: Downplaying the threat

·       Guidance: Fauci described the risk of COVID-19 to the American public as "minuscule".

·       Context: While the full scope of the pandemic was not yet known, some found this public messaging to be misleadingly reassuring. 

Early 2020: Mixed messaging on masks

·       Guidance: Early in the pandemic, Fauci advised against the public wearing masks. He and other officials argued that masks were unnecessary for the general population and needed to be reserved for healthcare workers.

·       Shift: As evidence grew showing asymptomatic and presymptomatic spread, health officials, including Fauci, reversed course and recommended mask-wearing for everyone.

·       Criticism: The initial mixed messaging caused confusion and fueled public distrust regarding the efficacy of masks. 

Throughout 2020: The "6 feet apart" rule

·       Guidance: The Centers for Disease Control and Prevention (CDC) recommended maintaining a 6-foot distance from others. Fauci, during later congressional testimony, stated that this recommendation "sort of just appeared" and was not based on scientific data.

·       Criticism: The revelation that the 6-foot rule was arbitrary sparked controversy. Critics noted the negative impact the rule had on businesses and schools, and accused public health officials of not being transparent with the public. 

2020 and 2021: Downplaying the lab-leak theory

·       Actions: Early in the pandemic, Fauci dismissed the possibility that the virus originated from a lab, including the Wuhan Institute of Virology.

·       Shift: Fauci later acknowledged that the lab-leak theory could not be ruled out.

·       Criticism: Critics accused Fauci of suppressing the lab-leak theory by having a hand in a paper that argued for a natural origin of the virus. 

Summer 2021: Vaccine guidance and "breakthrough infections"

·       Guidance: As vaccines rolled out, Fauci and other health officials initially played down the risk of "breakthrough infections" (cases in fully vaccinated people).

·       Shift: With the emergence of the Delta variant, Fauci acknowledged that even vaccinated people could transmit the virus and called for the reintroduction of some public health measures, including masking.

·       Criticism: This created a new source of confusion, particularly among vaccinated people who believed their shots would fully prevent transmission. 

The consequences of flawed guidance

The shifting nature of public health advice led to several negative consequences:

·       Erosion of trust: Repeated instances of changing recommendations contributed to a significant decline in public trust in health officials and government institutions.

·       Politicization of public health: Inconsistent messaging fueled partisan divides and led to the politicization of mitigation measures like masking and social distancing.

·       Misinformation and polarization: The shifting guidance and lack of clear communication created an environment ripe for misinformation, which deepened skepticism and hindered effective responses to the pandemic. 

https://www.google.com/search?q=dr+fauchi%27s+flawed+guidance+for+covid-19+timeline

Comments

In 2020, when COVID-19 was declared a “ Pandemic”. It was expected to be lethal to patients over age 65 with pre-existing conditions. It was expected to be serious for all ages with pre-existing conditions. It was not expected to be serious for younger people with no pre-existing conditions. We took that to mean that our immune systems would deal with the Virus and provide “herd immunity”.

Our immune systems would produce anti-bodies to resist the virus.

Governor of States were supported by their “Public Health” agencies to over-react. Public Schools were allowed to close and “non-essential” businesses were ordered to close. Masks and 6 foot distances were required for all who came in contact with others. Employees who were young and had no pre-existing conditions were fired for refusing to take the virus.

We were told that people who took the Covid Vaccine would not pass the Virus to others, but this was a lie.

As the Covid Virus mutated, It became less deadly, but more transmissible.

In 2020, I began posting Covid Deaths by Country and Covid Deaths by State and continued these postings through November 2023 when the Pandemic ended. We were expected to have 2% Deaths, but Deaths were held to 1%. I used UN WHO data, because it was the “gold standard” for all activist Public Health Believers who belong to the Global Public Health Community. They were loyal to each other and gleefully shared their data to be reported. The CDC was in no position to provide the data.

The Public Health gospel is shared by Nursing and Social Work Students. It has its own dogma that demands “Funding”. Outcomes are not great.

I agree with the RFK jr. approach to reducing health care costs and improving outcomes. It begins with nutrition and removing harmful chemicals from processed foods. It will result in decreased obesity and require patients to take responsibility for their health.

Norb Leahy, Dunwoody GA Tea Party Leader