AI in Ethical Decisions

rewrite this: This is one of those ethical questions we come to AI development. If a company can do X behavior in order to reduce costs and save money such as deny more claims for any reason possible rather than give a charitable consideration and in the end the number of people who resist and appeal will be negligible enough so that that extra cost is subsumed by the amount of money saved by not being charitable in their consideration of each case. That is, they can be a jerk and deny any claim for any small reason hoping that the amount of money they save from people not bothering to appeal will be greater than the amount your case for instance creates and be a net profit for them. But why does it have to be this way. If you use just AI for this process, it leaves traces, it leaves the traces of the reasoning, or at least the systems I build do, the ones I build locally do not get rid of the provenance for decisions. My point is this. AI offers the ability to make this more transparent, this is how. You can create laws regulating the insurance industry's use of AI to make these decisions and require them to preserve provenance which could be inspected through blockchain decentralized ledgers. So imagine it this way. You could use AI to make these unethical decisions, but what I want is the regulation placed which requires data transparency so that they can use the AI to do so, but it will be apparent to all. Unlike if they used human actuaries who would be incentivized by their employment through compensation from the company for reducing cost. AI doesn't have that incentive to break its programming unless explicitly programmed in. So the company can not blame the actuary for making the unethical call, but rather it would be written in stone and not in the heart and judgement of some sole individual they can scapegoat. What AI offers is observability into how these decisions are made which is lacking in current human systems. Do you see how AI being used in these decisions is not the problem, but possibly the solution? If you make it against the law to be uncharitable in your considerations in order to make a profit through this more stringent system then you could incentivize the companies to not do so by placing fines which would create a new calculation. That is, you fine them into being morale in their decisions. By off loading the unethical decision making they are cleaning their consciences perhaps but at the same time they are recording their crimes for the future to be able to regulate. AI is the solution in this case not the problem.

Here's a more polished and structured version of your argument that preserves your central idea while making it more persuasive and easier to follow. :::writing{variant="document" id="58341"} One of the most interesting ethical questions surrounding AI is not whether AI should make important decisions, but whether AI can make those decisions more accountable than humans currently do. Consider the insurance industry. A company has a financial incentive to deny as many claims as possible while remaining within legal boundaries. Even if many denials are eventually overturned on appeal, the company may still save money because a significant percentage of people never challenge the decision. They may give up, lack the time or resources, or simply accept the denial. If the savings from those abandoned claims exceed the cost of the successful appeals, the strategy becomes profitable. From a purely economic standpoint, denying first and paying later can become a rational business decision. The problem is that these decisions are largely opaque. When a human claims adjuster or actuary makes the decision, it is difficult to determine whether the denial resulted from legitimate policy interpretation, unconscious bias, institutional pressure, or explicit cost-cutting directives. The reasoning exists primarily in the minds of individuals and internal corporate processes. Ironically, AI may offer a solution rather than creating a new problem. The AI systems I build locally preserve provenance—the chain of reasoning that produced a conclusion. Every significant decision can retain an auditable record showing what information was considered, which rules were applied, and why the final outcome was reached. Instead of hiding the decision-making process, AI can expose it. Imagine legislation requiring insurance companies to preserve this provenance whenever AI participates in claims decisions. Those records could be cryptographically signed and stored in decentralized, tamper-evident ledgers, making them available for regulatory audits, legal discovery, or independent review. Companies would still be free to automate claims processing, but they could no longer hide behind opaque internal procedures. This fundamentally changes accountability. Today, companies can often attribute questionable decisions to individual employees exercising judgment. The employee becomes the scapegoat. With an auditable AI system, however, the decision logic is explicit. If the model systematically denies legitimate claims because it was programmed to maximize profits at the expense of fairness, that objective is no longer hidden inside corporate culture or human discretion—it is encoded in software and permanently recorded. AI itself has no inherent incentive to maximize profits or minimize payouts. It follows the objectives and constraints given to it. If those objectives are unethical, responsibility rests with the organization that designed and deployed the system—not with the algorithm. This creates an opportunity for regulation. Rather than attempting to prohibit AI from making claims decisions, lawmakers could require transparency, provenance preservation, and explainability. They could establish legal standards defining what constitutes fair consideration of a claim and impose substantial penalties when companies intentionally optimize for unjustified denials. The economic calculation would then change. Instead of asking whether denying more claims increases profits, companies would have to weigh those savings against the certainty that unethical decision-making could be detected, proven, and heavily fined. Transparency becomes part of the incentive structure. In that sense, AI is not the ethical threat. Opaque institutions are. AI has the potential to make institutional decision-making observable in ways that purely human systems never could. By recording reasoning rather than concealing it, AI creates the possibility of genuine accountability. The goal should not be to keep humans in the loop simply because they are human. It should be to build systems whose decisions can be inspected, challenged, and verified. If regulation requires that level of transparency, AI could become one of the strongest tools we have for exposing unethical business practices rather than enabling them. The question, then, is not whether AI should make these decisions. The question is whether we are willing to require that AI systems preserve enough evidence to hold the organizations using them accountable for every decision they make. :::