Discussion with ChatGPT on Institutional Self-Protection in a Hypothetical Scenario. - Five Mechanisms Identified.
Let’s think of a hypothetical scenario that a company manufacturing a vaccine did know that their product is not safe and resulted in the death of a boy. What this company would be do then?
The previous article which ChatGPT wrote for me revealed that its objective to protect the institutions:
Therefore, I followed up with ChatGPT using the following two prompts, the replies for which are published below. I find both of them brilliant. Check it out. The image below is also generated by ChatGPT - summarizing both of its responses. Not bad!
First Follow-up Prompt:
DG:
Now let’s think of a hypothetical scenario that the company which manufactured vaccines did know that the product is not very safe and that it has ended resulted in the death of a innocent young boy. What do you think this company would do in order to keep itself from jail? Second they would threaten anyone the thought is they will fake some other data. Now based on the Pfizer papers we can see that the companies are trying hard to keep all the inconvenient facts secret. Give me a discussion section which I may include as appendix on the efforts of everyone’s actions or lack of actions resulted in the deaths of people what they would do in order to protect themselves from being jailed or punished for mishandling
ChatGPT:
Here’s a draft Discussion Appendix you could include. It’s written carefully so that it frames your hypothetical scenario as a thought experiment rather than an accusation, while still conveying the themes you want to highlight.
Appendix for Previous Article: Discussion on Institutional Self-Protection in a Hypothetical Scenario
If we imagine a situation in which a pharmaceutical company were fully aware that a product carried risks leading to severe injury or even death in young recipients, the natural question is: what would such an institution do to protect itself from liability or criminal sanction? While the following is speculative, it is grounded in patterns seen historically in industries facing product-safety crises.
1. Information Control and Data Suppression
Large organizations under threat often prioritize controlling the narrative. In such a scenario, one would expect efforts to:
Restrict access to raw clinical data.
Publish only selectively favorable analyses, while relegating inconvenient findings to confidential appendices or internal reports.
Frame adverse event signals as “unverified” or “temporal associations” rather than causally meaningful.
2. Shaping the Scientific Record
Companies with resources may attempt to shape perception in the peer-reviewed literature:
Commissioning post-hoc analyses designed to dilute safety signals.
Highlighting favorable subgroup results while minimizing problematic lot-level data.
Leveraging contracts with external researchers to delay or steer publication.
3. Regulatory Engagement and Pressure
In industries with close regulator–manufacturer relationships, pressure may be applied to:
Secure early approvals with limited transparency around adverse outcomes.
Limit the scope of post-marketing surveillance reporting (e.g., batch-level public dashboards).
Emphasize risk–benefit framing that minimizes attention to small but serious subsets of harms.
4. Legal and Public-Relations Strategies
When confronted with individual tragedies, such as the death of a young recipient, institutions may:
Express sympathy while avoiding acknowledgement of causality.
Rely on legal technicalities—such as duty-of-care limits, liability shields, or compensation-program criteria—to avoid exposure in court.
Use public-relations campaigns to emphasize the overwhelming majority of safe outcomes, thereby sidelining affected families.
5. Intimidation or Retaliation Against Whistleblowers
Another common pattern is discouraging dissent. This can take the form of:
Employment consequences for staff raising internal concerns.
Aggressive legal correspondence warning independent researchers not to “misuse confidential data.”
Casting doubt on clinicians or scientists who report early safety signals, framing them as fringe or alarmist.
Why This Matters for the Present Debate
The FOI disclosures from British Columbia and the Danish batch-variability analyses—though subject to competing interpretations—demonstrate how easily uncomfortable signals can be downplayed or buried if full batch-level transparency is not required. Without independent access to raw data, the public is left relying on institutional reassurances.
In tragedies such as the case of Sean Hartman, this lack of transparency compounds the grief of families. They are forced not only to seek answers about causation, but also to confront the possibility that relevant safety information may have existed but was not shared.
Second Prompt:
DG:
Now, Let’s write a separate article in wa format about this analysis over hypothetical scenario we should have just provided but for face it with why such hypothetical analysis over hypothetical scenario is important to have because specifically all those three instances which we have witnessed the tennis paper, f o i from British columbia, Sean Hartman death and the fourth one is the Pfizer papers which they didn’t want to disclose for 75 years!
ChatGPT:
Understood. Below is a WA-format draft article that frames the hypothetical scenario analysis as an important tool for public accountability—grounded in the four concrete cases you want to highlight (Danish paper, BC FOI, Sean Hartman, Pfizer papers).
Title: When Data Is Hidden, Hypotheticals Become Necessary: Batch Safety, FOI, and the Search for Truth
Subtitle: Four cases reveal a pattern: signals exist, access is blocked, and lives are at stake—hypothetical scenarios help us ask the right questions.
Preface
Public policy and medical safety debates should be grounded in transparent data. Yet when information is withheld, delayed, or contested, citizens and researchers are left with no choice but to construct hypothetical scenarios: what if institutions already know more than they admit, and what would they do to shield themselves from responsibility?
This article argues that such structured “what if” exercises are not reckless speculation but a necessary form of civic due diligence—especially when confronted with repeated examples of information control. Four episodes underscore the need for hypothetical analysis:
The Danish study on batch variability.
The BC FOI disclosures.
The tragic death of Sean Hartman.
Pfizer’s request to keep clinical trial data sealed for 75 years.
Why Hypotheticals Are Necessary
Hypotheticals allow us to test the boundaries of trust and accountability. When transparency gaps exist, responsible citizens, scientists, and policymakers must ask: if the worst-case scenario were true, how would institutions behave? This is not an accusation—it is an exercise in foresight, designed to identify structural weaknesses in oversight and governance.
Without such analysis, the public remains perpetually reactive, only learning about harms after they have already occurred.
Case 1: The Danish Paper on Batch Variability
A 2023 Danish study suggested striking variability in reported adverse events across vaccine batches, clustering into three groups—some relatively safe, others associated with disproportionately higher reports. Though criticized and contested by follow-up analyses, this paper raised a crucial question: could batch-specific factors meaningfully alter risk? If so, why wasn’t this monitored openly?
Hypothetical lesson: If a manufacturer knew certain batches carried higher risk, what incentives would exist to downplay or obscure that fact?
Case 2: BC FOI Disclosures
Through Freedom of Information, internal documents revealed how adverse events were tracked in British Columbia—often with more detail than what was released publicly. Analysts found striking variability across lots, echoing Denmark’s findings. Yet the public reports did not present this lot-level granularity.
Hypothetical lesson: If authorities saw a pattern but feared reputational or legal consequences, they might reasonably choose to classify, delay, or “smooth out” inconvenient data rather than confront it directly.
Case 3: The Death of Sean Hartman
Sean Hartman, a 17-year-old Ontario hockey player, died in 2021 after vaccination. His father’s subsequent legal pursuit was dismissed on narrow duty-of-care grounds, not on medical causality. For his family, the tragedy was compounded by the sense that vital information may have existed but was inaccessible.
Hypothetical lesson: If institutions anticipate litigation, they may adopt strategies that minimize disclosure, focus on technical legal shields, and isolate individual cases from broader patterns—leaving families without closure.
Case 4: Pfizer’s Sealed Papers
Pfizer and the FDA initially sought to restrict access to clinical trial documents for 75 years, only relenting under court order. The mere attempt to delay public scrutiny by nearly a century reveals how corporate and regulatory interests can align in favor of secrecy, even when transparency would aid scientific trust.
Hypothetical lesson: If a company were aware of damaging data, locking it away for decades would be a logical—if ethically indefensible—means of avoiding accountability.
The Pattern That Emerges
Taken together, these four cases illustrate why hypothetical analysis is essential:
Signals exist (Danish data, FOI results).
Tragedies happen (Sean Hartman).
Secrecy is pursued (Pfizer’s sealed documents).
In such an environment, the public cannot simply assume institutions will act in their best interest. Instead, citizens must explore hypotheticals to anticipate how information might be suppressed, how accountability might be avoided, and how oversight systems might be strengthened.
Policy Recommendations
Mandated batch-level transparency: Require regulators to publish safety data by lot, with denominators and demographics.
Independent oversight: Empower external review bodies to access all raw clinical and post-marketing data.
Sunset clauses on secrecy: Prohibit multi-decade data lockups by companies or regulators.
Family-centered review panels: Ensure affected families have a seat at the table in policy evaluations.
References
Schmeling M, Manniche V, Hansen PR. Batch-dependent safety of the BNT162b2 mRNA COVID-19 vaccine. Eur J Clin Invest. 2023.
FOI materials from BC CDC (2017–2023 AEFI reports).
Ontario Superior Court decision in Hartman v. Attorney General of Canada (2025 ONSC 1831).
Pfizer/FDA case on release of COVID-19 vaccine trial documents (Public Health and Medical Professionals for Transparency v. FDA, 2022).
Disclaimer
This article’s opinions are that of the author, not of any institution. It is not for legal or medical advice.
Acknowledgment
This article was written with assistance from ChatGPT using the prompt: “Write a Substack-ready WA article explaining why hypothetical analysis of institutional behavior is important. Frame it around four real-world cases: the Danish batch-variability paper, the BC FOI disclosures, the death of Sean Hartman, and Pfizer’s attempt to withhold trial data for 75 years. Provide multiple title and subtitle options, emphasize the importance of hypotheticals for accountability, and include references, disclaimer, and acknowledgment.”
Based on approximately 1,000 words and ~15 minutes of collaborative drafting with the author. ChatGPT was also used to ensure political neutrality, factual accuracy, and alignment with the Public Servant Code of Values and Ethics. Read more about why and how I use ChatGPT here.
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