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? 📊
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.
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 ⚙️
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.
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.
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 🌍
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)
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 🔍
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.
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
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 |
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
👉 Help fund the next public test of AIIM.
Help Test a New Way to Fund Science
AI Internet-Meritocracy (AIIM) is an operational beta designed to allocate available donations to researchers and open-source developers using AI-assisted evaluation of documented contributions. Payment transactions are already recorded on-chain.
The next major evidence milestone is a proposed five-month public adversarial test of the allocation model, with $1,000 distributed to eligible funding recipients. Donations help pay for the development, infrastructure, reviewer and participant recruitment, outreach, and operating work needed to reach and evaluate that milestone.
You do not need to assume AIIM is already proven to support the project. Your donation helps turn the proposal into evidence: what works, what fails, and what should change.
Support the next testing milestone → Read the test proposal →
Independent review
Researchers and technical reviewers: independent criticism is welcome, including negative conclusions. Review AIIM’s assumptions, governance, failure modes, and testing plan →
Research status: AIIM remains experimental. AI-generated evaluations can contain factual errors or biases, and decentralized governance and non-custodial components remain under development. That uncertainty is why public testing, auditability, and external criticism are central to the project.