Here is another way to view the FDD work
Grant Eligibility = Content Moderation
Round execution - Reactive operational execution (All Round Management)
- Two reviewers per grant minimum x 1000 new grants
- Training of new reviewers - facilitating learning and communication during the round
- Manual documentation of flags/disputes
- Judgements on disputes
- Manual documentation of appeals
- Facilitation of conversations around appeals and judgements
Short Term Improvements - Proactively improving for the next round (Mandate Delivery, Data Science)
- Ethelo devops support to bring down per review cost - spend $10k, save $10k per round
- Gather better data from the approvals process
- Training
- Research on Kleros, Celeste and staking ideas (We had this on the roadmap 2 seasons ago, but then the dissolving of dGrants was a surprise and no one included us in the product conversations around building grants 2.0 so we thought we were supposed to maintain course and figure out our own solutions for grants 2.0)
Medium Term Connecting Current Course & Speed to Future State (Trust Builders)
- Using review data from ethelo to run simulations of the decentralized protocol for reviewer reputation which would create stamps in Passport.
- Software - https://github.com/dRewardsSystem/Rewards/commit/6eeaebf3584d21fbbdfcc42e580b870b4e75ea22
- Description of software - GIA Rewards OKR Report
Long Term Vision - Future State = An ethically values aligned and sustainable solution (Trust Builders w/ Sybil Detection DAO & Passport)
- Passport is used by everyone on earth - They have an option to participate in sybil hunting and grant curation for reward at anytime
- The system doesn’t BAN sybils and fraudulent grant creators, it instead only allows them to play with each other
- We avoid the web 2 moderation trap of becoming addicted to lean and inexpensive (but easily corrupted) delegated authority
- We have built a system that offers communities the potential to choose “community curation” which is a decentralized review process aka a system where they can’t do wrong as opposed to shouldn’t do wrong
- Stamps from this system are HIGH quality non-sybil signals
Sybil Defense = User Moderation
Round execution - Reactive operational tasks (Mostly SAD squad & Human Evals)
- Run ASOP algorithm to identify sybil accounts
- Push info to gitcoin backend for sanctioning/squelching (114,000 contributions out of 500k total in GR12)
- Enough human reviews to statistically validate the model is working properly (>1,500 but decreasing returns over 8,000 or around there currently)
- Enough human reviews to identify new behaviors of attackers (More is better as long as they are putting in genuine human subjective answers and not just using a “rubric”)
- Enough human reviews to disperse bias across reviewer geographies, cultures, race, sex, etc.
(rather than programming the bias of the engineers)
Short Term Improvements - Proactively improving for the next round (Data Science & Community Model)
- Identifying the high confidence sybil users (and known not sybil) and analyzing for correlations to Passport stamps
- Turn Passport stamp correlations into features
- Continue work identifying sybil behavior classes and new features
- Analyze human evaluations for inter-reviewer reliability
Medium Term Connecting Current Course & Speed to Future State (Community Model, Mandate Delivery, Data Science)
- Remove all cgrants backend data from algos - Use only non-pii publicly available (On chain) inputs
- Identify long term value patterns that can prove the cost of forgery
- Continue algorithmic sybil defense for communities that need it early. Although gitcoin is building a very long term valuable solution with passport, someone still needs to read the data AND we need to compare the results of our hypothesis that dPoPP will continue to solve the problem and not be gamed. We should not lose the current working system until we have tried to falsify the new hypothesis with the best data available!
Long Term Vision - Future State = An ethical & values aligned sustainable solution (Community Model, Sybil Detection DAO, Trust Builders)
- Sybil Detection DAO decentralized user moderation - NOT by having an ever expanding set of human evaluators, but by using the machine learnin to scale the human subjectivity. Algorithms hold unknown amounts of bias. Keeping humans in the loop is an ethical solution in line with Gitcoin’s values.
- Dynamic reading and peer predictions
- A high values rules based system designed and dynamically updated using crowdsourced data analysis (Community model)
- Large ownership stake in digital public infrastructure for Gitcoin & aqueducts!!!
Note: Evo - oXS & Evo - Ops are both operational functions. The first being our decision making, meetings, calendar updates, internal comms, etc. The latter being the roles with DAO ops has requested each workstream have and our payments.