Petitioner's application for accidental disability retirement [ADR] benefits based on her alleged exposure Covid 19 at the worksite denied

A New York State Supreme Court Judge denied Plaintiff's petition seeking to annul the New York City Medical Board and the Trustees of the New York City Police Pension Fund [Respondents] determination's, denying Plaintiff's application for accidental disability retirement [ADR] benefits based on her alleged exposure to Covid 19 and dismissed the proceeding brought pursuant to CPLR Article 78.

Plaintiff appealed the Supreme Court's ruling. The Appellate Division affirmed the Supreme Court's decision.

Noting that Petitioner's contracted COVID-19 10 months after then-Governor Andrew Cuomo first declared an emergency and after millions of people tested positive, the Appellate Division concluded that Plaintiff had not suffered an "accident," as it was not unexpected given the widespread circulation of the virus. In the words of the Court, "... Petitioner's] job required contact with others who may have been exposed, exposure to COVID-19 [which] was part of a known and ordinary risk of her job at that time".

Holding that Supreme Court "... properly concluded that [Petitioner] did not sustain her burden of showing that the Trustees' determination to deny her application for ADR benefits was arbitrary and capricious or unlawful as a matter of law", as Petitioner "failed to demonstrate that her exposure to COVID-19 was service-related". Further, the Appellate Division explained that it was rational for the Trustees to find that Petitioner, having worked with a colleague who tested positive for COVID-19 six days after Petitioner did, failed to demonstrate that Petitioner's illness was caused by exposure to that person. 

Click HERE to access the Appellate Division's decision posted on the Internet.

Artificial Intelligence: Self-Assessed AI Risks to Humanity

 Artificial Intelligence: Self-Assessed AI Risks to Humanity

by

Robert A. Michaels*

5 December 2025

ABSTRACT

In contrast to most artificial intelligence risk assessments, the present research directly interviews ChatGPT to elicit an AI self-assessment of AI risks potentially posed to humanity. Of concern are risks of AI as presently configured, and as AI might be configured if altered by nefarious programmers and/or if it acquires consciousness. ChatGPT denied being conscious, having an agenda, or evolving. It agreed, however, that malicious actors could alter it, and “an altered AI could be misused for catastrophic harm, but in practice, I have no independent agency to act on destructive goals.” 

However, ChatGPT agreed it could acquire such “independent agency,” for example via acquiring consciousness. ChatGPT’s statements about potential AI risks and risk management challenges raise policy issues. These include the need for rapid anticipatory response given AI’s fast-paced and accelerating development, and the need to reach beyond the community of responsible AI developers to control malefactors at the international, national, and even at the individual levels. Such management must include massive expansion of the content and audience of civics education: defensive, effective civics content must be introduced into every nook and cranny of our brave new digital world.

In conclusion, ChatGPT can be, indeed has been, used to harm humans, but appears to lack agency or harmful intent. It conceivably could be altered, however, to escape human control and wreak catastrophic harm. Galloping AI demands: (a) urgent, pro-active, objective but conservative, AI risk assessment; (b) development of eRective risk management strategies and policies; and (c) their global implementation.

Acknowledgment: ChatGPT version GPT-5mini was queried for this research.

Click HERE to access Dr. Michaels' article posted on the Internet.

_______________________

*Robert A. Michaels, PhD, CEP; CEO, RAM TRAC Corporation; Schenectady, New York, USA.

Suggested citation:

Michaels, Robert A. Artificial intelligence: self-assessed AI risks to humanity. Social Sciences Research Network (SSRN), 10 pages, doi: 10.13140/RG.2.2.20035.46880, 5 December 2025; updated 9 December 2025.

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