Moral uncertainty—the challenge of making decisions when confidence in any single moral theory is lacking—creates dilemmas in ethics, policy-making, and personal choices. Existing approaches often rely on intuition or favor one theory without justification, leaving a gap for structured methods to weigh competing perspectives fairly.
One way to address this could be a tool that simulates decision-making under moral uncertainty by treating competing theories as "parties" in a virtual parliament. The size of each party would reflect the user’s confidence in that theory, and the tool would aggregate these perspectives to suggest decisions. For example:
Initial testing might use clear-cut scenarios (e.g., trolley problems) to compare outcomes, with later expansion to real-world cases like healthcare rationing. Users could adjust weightings or exclude theories they find irrelevant, and qualitative explanations would accompany results to avoid oversimplification.
This tool could serve ethicists exploring theoretical debates, policy-makers balancing conflicting values, or individuals navigating personal dilemmas (e.g., altruism vs. family obligations). Incentives align well: researchers might collaborate to refine effective altruism tools, while institutions could adopt it to legitimize contested decisions. A freemium model or grants could support development, with basic features free and advanced customization as paid options.
Unlike crowdsourced tools like MIT’s Moral Machine (which documents public opinion) or risk-focused frameworks like EthicalOS, this project would offer prescriptive guidance for uncertain scenarios. It generalizes beyond niche applications (e.g., philanthropy prioritization) by integrating multiple theories into a single decision-making process. Early testing with ethicists could validate whether the "parliament" metaphor meaningfully represents moral uncertainty.
By bridging abstract philosophy and practical choices, this approach could provide a clearer alternative to biased or ad hoc reasoning—especially in high-stakes decisions where fairness matters.
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Digital Product