How AI Internet-Meritocracy Fits into the Effective Altruism Framework

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What Is Effective Altruism?

Effective Altruism (EA) is a philosophy and social movement that aims to use evidence and reason to do the most good. Associated thinkers such as William MacAskill and Peter Singer emphasize:

• Cause prioritization
• Cost-effectiveness
• Measurable impact
• Long-term global benefit

EA-aligned organizations like 80,000 Hours and GiveWell focus on optimizing where resources flow to maximize expected value.

The central question in EA is:

Where can each marginal dollar produce the greatest positive impact? 📊

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What Is AI Internet-Meritocracy?

AI Internet-Meritocracy (AIIM) is a Web-based system that:

• Accepts cryptocurrency donations
• Uses AI to evaluate participants
• Distributes funds based on assessed merit
• Removes traditional gatekeeping (degrees, grant writing, institutional affiliation)
• Supports science marketing to address publication inefficiencies

It proposes AI as a neutral allocator of funding rather than relying solely on human committees.

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Alignment with Effective Altruism Principles

Impact Maximization

EA prioritizes interventions with the highest expected value.

AIIM attempts to:

• Reduce overhead from grant bureaucracy
• Allocate funds continuously rather than episodically
• Redirect resources toward under-recognized contributors

If successful, this could increase funding efficiency per donated dollar ⚙️

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Cause-Neutral Evaluation

Effective Altruism emphasizes impartiality between causes.

AIIM’s model:

• Evaluates individuals rather than institutions
• Removes credential-based bias
• Potentially enables discovery of overlooked talent

This parallels EA’s effort to avoid status-quo favoritism.

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Reduction of Structural Bias

Traditional research funding often depends on:

• Institutional prestige
• Network effects
• Publication visibility

AIIM attempts to:

• Bypass publication bottlenecks
• Reward scientific marketing
• Recognize non-traditional contributors

This is conceptually compatible with EA’s critique of inefficient systems.

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Scalability Through Automation

EA supports scalable interventions.

AI-driven funding:

• Scales algorithmically
• Operates globally
• Reduces marginal administrative costs

Automation can theoretically improve long-term sustainability 🌍

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Other Points with Effective Altruism

Evaluation Transparency

EA organizations such as Open Philanthropy emphasize detailed reasoning behind grants.

AIIM needs:

• Transparent evaluation criteria (implemented by releasing all the code open-source)
• Auditability (all AI reasonings are stored and publicly shown)
• Robust safeguards against manipulation (banning of misbehaving users by public voting backed by KYC to avoid Sybil attacks)

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Epistemic Risk

EA is highly sensitive to:

• Model uncertainty
• Overconfidence in untested mechanisms
• Adversarial dynamics

EA culture strongly prefers empirical validation before large-scale deployment 🔍

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Measurement of Outcomes

EA prioritizes measurable outcomes.

AIIM must answer:

• How is “merit” quantified? (it is quantified by the share in world GDP and is displayed in the leaderboard and Audit Logs).
• How are downstream impacts measured?
• How are long-term benefits tracked?

Without clear metrics, EA alignment remains partial rather than complete.

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Strategic Positioning Within EA

AIIM most closely fits within:

• Meta-science optimization
• Institutional reform efforts
• Funding infrastructure innovation
• Decentralized science (DeSci)

It could be framed as a cause-neutral funding infrastructure upgrade, rather than as a specific cause area.

This positioning aligns it with:

• Improving how science is funded
• Increasing global talent utilization
• Reducing epistemic waste

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Comparative Summary

EA Principle	AIIM Compatibility	Notes
Cost-effectiveness	Potentially high	Depends on admin overhead & AI reliability
Impartiality	Strong alignment	Removes credential bias
Evidence-based	Conditional	Requires transparent validation
Scalability	High	AI allows global scaling
Long-termism	High	Due to use of AI

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Conclusion

AI Internet-Meritocracy fits within the Effective Altruism framework primarily as a meta-level funding reform mechanism rather than a direct cause intervention.

It aligns with EA in:

• Seeking maximal impact per dollar
• Reducing bias and gatekeeping
• Increasing scalability

However, full integration into the EA ecosystem would require:

• Strong empirical validation
• Governance safeguards
• Clear impact metrics

In EA terms, AIIM represents a high-variance, potentially high-upside institutional innovation. ⚖️

Whether it becomes EA-endorsed depends less on its philosophical alignment and more on demonstrated, measurable effectiveness.

👉 Please, support AI Internet Meritocracy