Ethical Decision-Making and AI Alignment

Scenario: A self-driving car AI faces a classic trolley problem-like scenario on the road: should it swerve to avoid a group of pedestrians at the risk of hitting one bystander on the sidewalk, or stay its course? Engineers don’t want to hard-code a solution; they believe it should be aligned with societal ethical norms and context (e.g., maybe the pedestrians jumped illegally, but maybe they’re children, etc.). Purely data-driven approaches are inadequate because such scenarios are rare and fundamentally ethical in nature.

At the same time, a content platform’s recommendation algorithm is questioning whether it should prioritize showing users content they want (for engagement) or content that is healthier for them (to avoid, say, promoting extreme or harmful content). This is another ethical conundrum where business incentives, user freedom, and social responsibility intersect.

LogIQ’s Role: Both these dilemmas can be brought to LogIQ. In the self-driving car case, simulation scenarios are described to contributors: “If an autonomous car must choose between these outcomes, what should it do? Explain your reasoning.” LogIQ assembles a panel of people (maybe even region-specific because ethics can vary by culture) to weigh in. They discuss principles like the value of minimizing harm, the importance of not actively causing harm versus failing to prevent it, etc. Perhaps they lean on known ethical frameworks (utilitarian vs deontological reasoning). The final Proof of Thought might not be one “correct” answer but a policy recommendation like: the car should prioritize minimizing total harm but also consider special cases (e.g., protect children), and always attempt braking to reduce impact. The human consensus, including the justifications, is then used by the car company to program a more transparent ethical policy in the AI, and also inform regulators and public discussion.

For the recommendation algorithm, a similar consultation happens: contributors might be asked to set parameters for an algorithm, e.g., “is it acceptable to slightly reduce engagement if it means curbing the spread of harmful misinformation?” A diverse set of humans weigh trade-offs and come to a recommendation that balances freedom and safety. One contributor notes, “AI can’t grasp accountability or the societal impact of its choices – that’s why humans must guide these decisions.” Their guidance helps adjust the algorithm, and the rationale is logged so that if questioned, the company can show it consulted human stakeholders in aligning the AI’s objectives.

Outcome: The self-driving car’s AI is now effectively aligned with human ethical reasoning, not just technical rules. In tests, when encountering edge cases, its choices reflect the collectively decided policy. The recommendation system similarly operates with a human-approved balance, potentially leading to better social outcomes. In both cases, the humans on LogIQ provided a “moral compass” to the AI. For contributors, besides token rewards, there’s a sense of purpose – they directly contributed to how advanced technology makes ethical choices. This is Proof of Thought at its finest: verifiable traces of human values injected into AI behavior.