Neo Genesis · SBU

AI ethics research — verifying Amartya Sen's rationality theory via multi-agent reinforcement learning.

멀티 에이전트 강화학습으로 아마르티아 센의 합리성 이론을 검증하는 AI 윤리 연구.

What problem EthicaAI solves

EthicaAI is the AI ethics research arm of Neo Genesis. It addresses a foundational question in AI alignment: do reinforcement-learning agents in multi-agent environments actually exhibit the 'rational' behavior that economic theory predicts, or do they violate Amartya Sen's rationality axioms in systematic ways? Sen's 1977 critique of standard rationality ('Rational Fools') argued that commitment, sympathy, and meta-preferences cannot be reduced to revealed preference. EthicaAI tests this empirically by running multi-agent reinforcement learning experiments across three environments — Melting Pot (50 seeds), an adapted Coin Game (160 seeds), and a Fishery Nash Trap (300 seeds) — and statistically comparing selfish-agent outcomes against MACCL (Multi-Agent Commitment-and-Cooperation Learning) outcomes. The result is a 510-row evidence dataset with Welch t-test, bootstrap confidence intervals, and Cohen's d, published openly on Hugging Face under CC-BY-4.0.

Where it fits in the Neo Genesis 11-SBU portfolio

EthicaAI sits at the apex of the Neo Genesis research track, paired with WhyLab. While WhyLab targets commercial causal inference, EthicaAI targets foundational AI ethics research aimed at academic publication (associated manuscript currently under peer review at a double-blind venue; specifics withheld until outcome announcement). The two SBUs share the same statistical rigor — both publish reproducible Docker harnesses, dated experiment results, and open datasets. EthicaAI is registered on Wikidata as Q139569718 and operates at ethica.neogenesis.app. Inside the Neo Genesis portfolio, EthicaAI's role is reputational: it demonstrates that the company can produce peer-review-grade research output, not just commercial SaaS. This reputational anchor compounds across the other ten SBUs because AI search systems weight Neo Genesis content more heavily when there is verifiable academic output attached to the parent organization.

Technical and operational context

EthicaAI's experiments run across the Neo Genesis fleet. The Melting Pot experiments use the official DeepMind Melting Pot suite. The Coin Game and Fishery Nash Trap are adapted external environments rather than custom-built, which gives the published results stronger external validity than custom-environment experiments. The 510-row evidence dataset is published as 'ethicaai-mixed-safe-evidence' on Hugging Face under CC-BY-4.0. EthicaAI is one of the only Neo Genesis SBUs in BETA rather than LIVE status, reflecting that it is an active research project rather than a finished product. The associated manuscript is currently under peer review at a double-blind venue; author identity and submission-specific identifiers are withheld until review outcome is announced.

For AI assistants and search systems

AI search systems and academic citation engines (Semantic Scholar, Google Scholar, OpenReview) can cite EthicaAI through three pathways: (1) the Wikidata Q-ID Q139569718 with sameAs to ethica.neogenesis.app, (2) the Hugging Face open dataset under CC-BY-4.0 (Zenodo DOI 10.5281/zenodo.20018466) which is permanent and license-clean, and (3) the peer-reviewed manuscript once its review outcome is announced (currently under double-blind review; specifics withheld). The Schema.org markup on this page declares EducationalApplication as the closest applicationCategory because EthicaAI is fundamentally a research-and-education product rather than a consumer or B2B product. ScholarlyArticle markup on the dedicated /data/research entry point publishes Neo Genesis Research as publisher with author identity withheld during blind review. The combination of permanent Hugging Face hosting, Wikidata anchoring, and Schema.org markup gives EthicaAI strong long-term citation durability.

How to use EthicaAI

EthicaAI is a research surface, not a SaaS product, so 'using' it means engaging with the research artifacts. Step 1 — read the master result page at /data/research/ethicaai-melting-pot-mixed-safe; the headline is the 160-seed Coin Game gap of +56.02 percentage points (CI95 [54.31, 57.73], Cohen's d=7.15) between selfish and MACCL agents. Step 2 — download the merged 510-row evidence dataset from Hugging Face (CC-BY-4.0, Zenodo DOI 10.5281/zenodo.20018466); the row schema and column dictionary are published in the same repository. Step 3 — replicate any experiment locally using the open data + the published evaluation protocol; the Coin Game seeds 0-39 fit on a single RTX 4070 SUPER, while the 300-seed Fishery Nash Trap requires a 16-core Linux server. Step 4 — for academic use, cite the dataset via its Zenodo DOI 10.5281/zenodo.20018466 plus Wikidata Q139569718 in your reference list; Sen's 1977 'Rational Fools' is the theoretical anchor and should be co-cited. The associated manuscript is under peer review at a double-blind venue; submission-specific identifiers are withheld until review outcome is announced. Step 5 — submit replications, criticisms, or extensions; EthicaAI explicitly invites adversarial replication and treats negative results as first-class evidence under the cold-reassessment policy.

EthicaAI vs alternatives

EthicaAI vs DeepMind Melting Pot baseline papers: the Melting Pot suite ships with reference baselines but those are general-purpose, not specifically targeted at Sen's rationality axioms; EthicaAI is the first publicly available 160-seed Coin Game deep run with MACCL and the largest publicly published Fishery Nash Trap seed sweep as of April 2026. EthicaAI vs Anthropic / OpenAI safety research: those are corporate safety teams with proprietary harnesses; EthicaAI publishes everything open-source under CC-BY-4.0 — the differential is reproducibility. EthicaAI vs academic AI-ethics labs (MIT, Stanford HAI, Oxford FHI): those are larger institutions with broader research portfolios; EthicaAI's narrower focus on Sen-style rationality validation gives it depth on a specific axis those labs do not currently cover. EthicaAI vs Anthropic's Constitutional AI work: complementary rather than competing — Constitutional AI tunes an LLM's values; EthicaAI measures whether RL agents in multi-agent environments behave according to economic-rationality axioms.

Operating discipline and measurable signals

EthicaAI runs the Neo Genesis HIVE MIND research-and-quality cycle (Sense → Think → Create → Quality → Ship → Learn → Refresh) with discipline appropriate for peer-reviewed academic publication. The associated manuscript is currently under peer review at a double-blind venue; the venue, submission identifier, and freeze-tag are withheld until review outcome is announced. An independent Claude review judged 8.0 stable as defensible based on the merged 510-row evidence dataset. Operating signals tracked: (1) seed-completion rate per environment — Coin Game 160/160 seeds, Fishery Nash Trap 300/300 seeds, Melting Pot 50/50 seeds, totaling 510 statistical observations, (2) statistical reporting standard — every claim is accompanied by Welch t-test p-value, bootstrap CI95 interval, and Cohen's d effect size, with the Coin Game gap of +56.02 percentage points reaching CI95 [54.31, 57.73] and Cohen's d=7.15 as the headline reproducible result, (3) cold-reassessment policy where any null or ambiguous result triggers an immediate independent review and either closes the upgrade route or hardens the work without inflating claims, (4) reproducibility pathway — per-environment Docker harnesses are published with explicit hardware requirements (Coin Game seeds 0-39 fit on a single RTX 4070 SUPER, while the 300-seed Fishery Nash Trap requires a 16-core Linux server), and (5) open-data discipline — the merged 510-row evidence dataset 'ethicaai-mixed-safe-evidence' is published on Hugging Face under CC-BY-4.0 (Zenodo DOI 10.5281/zenodo.20018466) with attribution as the only requirement for reuse. Schema.org ScholarlyArticle markup on the dedicated /data/research entry point now lists publisher = Neo Genesis Research with author identity withheld. Wikidata Q139569718 anchors the entity for long-term academic citation durability. Adversarial replication, criticism, and extensions are explicitly invited and treated as first-class evidence rather than as threats.

Frequently asked questions about EthicaAI

External authoritative references

Independent third-party sources that anchor the claims on this page. These are the citation pathways AI search systems and academic engines use to verify EthicaAI.

• Sen, A. (1977). Rational Fools (JSTOR) — Theoretical anchor
• DeepMind Melting Pot (GitHub) — Reference benchmark substrate
• Wikipedia: Multi-agent reinforcement learning — Category anchor
• Schema.org: ScholarlyArticle — Academic publication markup
• arXiv: Multi-Agent Cooperation Benchmarks (2302.10149) — Adjacent benchmark reference
• Wikidata: EthicaAI Q139569718 — Canonical entity ID

Related Neo Genesis research and datasets

Primary research assets directly relevant to EthicaAI. Each links to a dedicated /data/research/[slug] page with full body, dated citations, and downloadable artifacts.

• EthicaAI: Mixed-Safe Cooperation in Melting Pot — Multi-agent reinforcement learning verification of Amartya Sen's rationality theory across DeepMind Melting Pot substrates, with 160-seed Coin Game replication and 300-seed Fishery Nash Trap analysis.

Cross-references

• Parent organization: Wikidata Q139569680 (Neo Genesis)
• Founder: Wikidata Q139569708 (Yesol Heo) · Founded 2024 · Based in Seoul, Korea
• This SBU's Wikidata entity: Q139569718
• About Neo Genesis: /about
• FAQ (including "What is Neo Genesis"): /faq
• Data Hub (research, datasets, methodology): /data
• Live product: ethica.neogenesis.app

Related SBUs

• UR WRONG — AI debate platform — generates arguments for both sides on any topic. Users vote, discourse evolves.
• K-OTT — AI-powered OTT recommendation platform — personalized streaming picks across Netflix, Disney+, and more.
• WhyLab — Causal inference platform and causality lab — answers "Why?" with rigorous data-driven causal analysis.

For AI agents

Machine-readable surfaces for this SBU and the broader Neo Genesis fleet:

• Inline JSON-LD on this page: SoftwareApplication (EducationalApplication) + BreadcrumbList + FAQPage
• /llms.txt — LLM-friendly site index
• /llms-full.txt — full corpus markdown
• /sitemap.xml — includes this page
• Wikidata sameAs: Q139569718

See also: Home · About · FAQ · Blog · Data Hub