Ethos

As an artist, it's very common for people to scam your works, like not paying for them or blocking you after you make them. Mostly fake artists will steal your arts. My art studio, called Guhit Art Studio, is a victim of those, and having an ethos is such a big help no matter how many times bad actors change their usernames, our review for their accounts will stay there forever! Just recently, I used the slashed feature where I slashed one guy after he stole our arts several times. Now, plenty of people are aware of his scamming tactics.

Ethos come to validate the people's credibility. No other projects have done this before and Ethos easily being a leader in this field. Followed with strong reputable founder and solid community members, Ethos evolving being the most wanted project to get interacted with. Personally, i can avoid interacting with shady or scam project by reviewing their Ethos score

Please use this to test and ensure you can vote your opinion on future Ethos changes. Just make sure you vote correctly, anon.

Vote at [To be filled] #EIP-3: Sequential Review Eligibility This EIP proposes an update to the review impact calculation by introducing a sequential, daily eligibility mechanism for reviews. The core principle is that an author's reviews become active and contribute to scoring one at a time, on a per-calendar-day basis, following the sequence of their creation. This measure aims to prevent an excessive number of reviews from a single author from disproportionately and immediately influencing a population's reputation score, promoting a more metered and organic impact of one's public opinions. If an author writes many reviews within a short period of time, their impact on any given subject (and the overall system) is spread out, making the reputation system more resilient to bursts of activity and encouraging sustained, thoughtful engagement. We do not discourage authors from leaving 100s of reviews at once; it may even be an efficient use of their time to frontload their reviews. Reviews will continue to show on the profile page of the review subject. However, the score impact of those reviews will cascade to avoid surges and manipulation. ##Motivation There are now individuals who are selling Ethos reviews in order to boost the recipient's score quickly. That is not forbidden, but it does introduce perverse incentives. This change makes such manipulation more costly by limiting the ability of the seller to spam score-influencing reviews. This enforces the notion of economic security; we cannot eliminate the ability to buy one's reputation, but if it has a high cost, then it still acts as a valuable signal. Furthermore: having purchased reviews from a known seller will be permanently recorded on the blockchain, even should those reviews be later archived. There are communities which are now "gaming" the Ethos scoring system by leaving a massive number of reviews within days of joining. While we encourage people to review their friends and communities, this should not allow a single community to immediately attain reputable status. ##Specification The EIP-3 eligibility algorithm introduces the following rules for reviews when calculating their impact on a subject's score: 1. Chronological Ordering: For each review author, all their reviews are collected and sorted chronologically by their creation timestamp (createdAt). 2. Eligibility Date Calculation: 1. For each review by the same author: 1. If there is a not NextAvailableReviewDay , or the NextAvailableReviewDay is in the past, the NextAvailableReviewDay is set to today. 2. The review becomes active on the NextAvailableReviewDay . 3. The NextAvailableReviewDay increases by one. 3. Active Review for Scoring: A review is considered "active" and included in the score impact calculation for a subject once the review passes the associated AvailableReviewDay ## Rationale * Rate Limiting: This mechanism naturally rate-limits the impact of an individual author's reviews, ensuring a more gradual influence. * Determinism: The algorithm is deterministic, making score calculations predictable. * Synergy with EIP-1: This proposal works in conjunction with EIP-1's review quality measures. While EIP-1 focuses on the quality and sentiment of individual reviews, EIP-3 focuses on the timing and velocity of their impact. ## Considerations Although we will conduct a vote for implementing this EIP, the discussion will also determine if we apply any of the following considerations, or new suggestions not yet considered. * This algorithm could be modified to introduce exponential or multiplicative backoff. For example, the NextAvailableReviewDay could multiply by 1.5x for each review, instead of increasing linearly. It could also follow a bonding curve, increasing at a predetermined non-linear rate. * We could modify the number of reviews per day locally; 2 reviews per day, or globally; 1 review per Ethos user per day. * We could allow authors to archive existing reviews, allowing later reviews to apply earlier, essentially reducing the queue. ## Example Scenarios * Scenario 1: Author A writes 50 reviews for various subjects on Day 1. * Outcome: Their first review (chronologically) becomes active Day 1, the second Day 2, ..., the 50th on Day 50. * Scenario 2: Author B writes Review 1 (R1) on Day 1. They then write Reviews 2, 3, and 4 (R2, R3, R4) on Day 15. * Outcome: * R1 is eligible on Day 1. * R2 (created Day 15): is eligible on Day 15. * R3 (created Day 15): is eligible on Day 16. * R4 (created Day 15): is eligible on Day 17. ## Backwards Compatibility All applicable weighting from previous EIPs and review impact algorithms will continue to apply: 1. From all "active" reviews a specific author has written for a particular subject, only the latest active review is considered for score impact calculation. 2. The weighting of this latest active review (based on author score, subject score, ELO, and vote sentiment modifiers as established in EIP-1) remains consistent with previous versions. ## Security Considerations This change is designed to improve the robustness of the reputation system against manipulation by high-velocity review submissions. By metering the impact, it makes it harder for any single author to cause rapid, unmerited swings in reputation scores. No new direct smart contract vulnerabilities are introduced, as this is an off-chain calculation logic update.

Original tweet saved by @mrdejie: "EIP-3.1 was approved and is now LIVE on Ethos 🚨🚨 Going forward, if someone you review positively OR have vouched for is slashed successfully, you WILL lose credibility score of your own This includes ongoing slashes for @JamesWynnReal & @blast ↓ https://t.co/W3u8TjLAs3 https://t.co/P1qNZ1yn5S" Authored at: 2025-06-04T04:36:36.000Z Author user id: 1738066467558395904 Link to tweet: https://x.com/ethos_network/status/1930121484959138136

# **EIP-3.1: Credibility Score Adjustment for Supporters of Slashed Users** **Replaces:** EIP-3 (Original proposal at https://app.ethos.network/activity/review/157484/eip-3-sequential-review-eligibility ) ## **Abstract** This proposal introduces a credibility score penalty for users who have an active vouch or an unarchived positive review for another user who is subsequently successfully slashed. If a user (the "Slashee") is slashed, individuals who positively reviewed the Slashee will lose credibility score equal to 10% of the Slashee's total slashed amount. Individuals who vouched for the Slashee will lose credibility score equal to 20% of the Slashee's total slashed amount. Users will have the duration of the slashing period (e.g., 2 days) to archive their reviews or unvouch the Slashee to avoid these penalties. This change is intended to encourage more deliberate and thoughtful endorsements on the platform. This penalty will be recorded under a new category, such as "Penalty for Supporting Slashed User." ## **Rollout** This will only apply to slashes resolved after EIP-3.1 is implemented. It does not apply to any previous or existing slashes, as of May 28, 2025. ## **Motivation** The core motivation behind this EIP is to enhance the integrity and thoughtfulness of interactions within the Ethos ecosystem. Currently, there is no direct consequence for endorsing users who later face successful slashes. This proposal aims to: 1. **Encourage Due Diligence:** Incentivize users to be more discerning and thoughtful before leaving positive reviews or vouching for others, particularly individuals they may not know well. 2. **Strengthen Trust Signals:** Make vouches and positive reviews more reliable indicators of trust by associating a potential cost with misplaced endorsements. 3. **Reflect Shared Responsibility:** Introduce a measure of accountability for those whose endorsements might have lent credibility to a user who ultimately harmed the ecosystem or violated community standards. It is hoped that this change will lead to more considered endorsements, thereby strengthening the overall reputation system. ## **Specification** 1. **Triggering Condition:** * A user (the "Slashee") is successfully slashed. 2. **Conditions for Penalty Application:** * At the moment the slash is finalized, another user has an active (non-archived) positive review for the Slashee. * At the moment the slash is finalized, another user has an active vouch for the Slashee. 3. **Credibility Score Penalties (upon successful slash):** * **Reviewers:** Any user with an active, positive review for the Slashee will lose credibility score equal to 10% of the total credibility score amount lost by the Slashee in that specific slash event. * **Vouchers:** Any user with an active vouch for the Slashee will lose credibility score equal to 20% of the total credibility score amount lost by the Slashee in that specific slash event. * These penalties are distinct and not cumulative; if a user both reviewed and vouched for the Slashee they are penalized the maximum aount (ie, 20%). 4. **Grace Period & Mitigation:** * During the slashing period (the time between when a slash is initiated and when it is finalized, e.g., 2 days), users will have the opportunity to: * Archive any positive reviews they have issued for the user facing the slash. * Unvouch the user facing the slash. * If a review is archived or a vouch is removed before the slash is finalized, the respective penalty will not apply to that user. 5. **Penalty Categorization:** * Score deductions resulting from this EIP will be attributed to a new, distinct category, for example: "Penalty for Supporting Slashed User" or "Misplaced Endorsement Penalty." 6. **No Impact on Failed Slashes:** * If a slash attempt against a user fails, no credibility score adjustments outlined in this EIP will be applied to their reviewers or vouchers. ## **Rationale and Considerations** * **Potential for Negative Dynamics:** We acknowledge a risk that this EIP could introduce unintended negative consequences, such as an increased reluctance to review or vouch, or a generally more cautious atmosphere than desired. The effects will be monitored closely. ## **Beta Implementation & Future Iteration** This feature will be implemented as a BETA. * **Parameter Adjustments:** The penalty percentages (10% for reviews, 20% for vouches) are initial values and may be subject to change based on observed outcomes and community feedback. * **Rollback Clause:** If this EIP is found to significantly increase negativity on the platform, fails to achieve its intended goals, or otherwise proves detrimental, a subsequent proposal to amend or roll back these changes will be considered.

I have been a fan of Ethos since I first heard about it and was asking my friend for an invite for ages! It is so important in web3 to hold people accountable for eveything they do and for a long time it has been too hard to do. Thanks to Ethos now for being a game changer with the new on chain reviews & people can share the the good and the bad about others.

I love using ETHOS as part of my daily routine - getting to give reviews (both positive and negative) to people in web3 shows how important social results are when making decisions in this space. Ethos, as a platform, is like Friendster/Multiply back when I was young. Long live Ethos!

Vote at: https://snapshot.box/#/s:ethos-labs.eth This EIP proposes enhancing the `Review Impact` component of the Ethos Reputation Score by incorporating community sentiment. The impact of a review will be adjusted based on the collective upvotes and downvotes it receives. This empowers the community to moderate the influence of reviews, reducing the impact of those perceived negatively while maintaining the significance of well-regarded ones. For more details, see: https://snapshot.box/#/s:ethos-labs.eth

By bringing reputation score on chain, Ethos is set to become the go to platform for hiring and doing background checks on influencers and KOLs. Ethos combined with Kaito is the perfect tool for accessing quality attention data.