Adversarial Reputation Attacks

1. Hypothesis

A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve attack detection and restoration more than single-score, content-only or opaque automated approaches.

2. Experiment design

Design: comparative trust-system assurance study with fairness, robustness, contestability and recovery testing focused on Adversarial Reputation Attacks Methods: algorithmic audit; fairness testing; adversarial attack simulation; user studies; longitudinal reputation analysis; governance review; portability experiments; expert review; affected-user interviews; reproducibility testing; methods adapted specifically to Adversarial Reputation Attacks Independent variables: evidence availability; provenance visibility; model or rule transparency; intervention timing; human-review level; platform context; user controls Dependent variables: attack detection and restoration; false-positive harm; user trust; correction or recovery time Confounders: legitimate criticism and rapid public response; platform differences; population composition; external events; baseline trust; data availability; reviewer expertise Measures: predictive validity; false positive rate; subgroup disparity; explainability; portability; recovery time; gaming resistance; user trust; subtopic-specific indicators for attack detection and restoration; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency Success criteria: Statistically and practically meaningful improvement in the named outcomes; calibrated uncertainty; acceptable false-positive burden; no disproportionate subgroup harm; traceable evidence; contestable decisions; repeatable performance across relevant contexts. Failure conditions: No meaningful benefit; results depend on unavailable or intrusive data; false positives or chilling effects exceed benefit; performance fails under adversarial or cross-platform conditions; affected users cannot understand, contest or recover from decisions.

3. Seed result / current evidence

DEFENSIBLE SEED RESULT — NON-EMPIRICAL. The current evidence supports Adversarial Reputation Attacks as a testable research proposition. Problem basis: Reputation systems can reduce uncertainty but also encode bias, entrench historical disadvantage, invite gaming and create irreversible harm when scores lack context, appeal or recovery pathways. For Adversarial Reputation Attacks, the specific challenge is detecting review bombing, false reporting, impersonation and coordinated reputational sabotage. Directional expectation: If supported, the proposed approach should improve attack detection and restoration, reduce correction and recovery costs, and preserve legitimate expression, privacy, procedural fairness and user agency. Proposed observations: predictive validity; false positive rate; subgroup disparity; explainability; portability; recovery time; gaming resistance; user trust; subtopic-specific indicators for attack detection and restoration; false-positive and false-negative rates; subgroup disparity; user comprehension; decision latency. Seed data profile: Evidence Strength 10/100; Confidence 25/100; Maturity 20/100; Overall Health 33/100; Novelty 70/100; Strategic Importance 90/100. Evidence boundary: No validated results yet.; experiments 0, studies 0, participants 0. This is suitable for protocol formation and baseline comparison, not as a finding of effect.

4. Seed conclusion

DEFENSIBLE SEED CONCLUSION — PROVISIONAL. Adversarial Reputation Attacks warrants structured testing because the CSV identifies a defined problem, falsifiable hypothesis, measurable outcomes and relevant literature foundations. The present position is that “A transparent, context-aware approach combining provenance, behavioural evidence and accountable human review will improve attack detection and restoration more than single-score, content-only or opaque automated approaches.” is plausible and decision-relevant, but unvalidated. Proceed to controlled testing against the stated success and failure conditions. Confirm, narrow or reject this seed after effect sizes, uncertainty, subgroup outcomes, adverse effects, persistence and handback performance are observed.

Prior-art search performed before starting

PRIOR-ART SEED BASELINE — PARTIAL. The CSV records these literature domains: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Adversarial Reputation Attacks. It also records: NIST AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework; OECD AI Principles — https://oecd.ai/; Australian Human Rights Commission — https://humanrights.gov.au/; EU AI Act information — https://digital-strategy.ec.europa.eu/. Evidence register status: “Seeded; authoritative source register initiated; empirical evidence not yet ingested”. This is defensible as a starting prior-art inventory, but not as proof of a completed systematic search because search dates, databases, exact queries, reviewer, result counts, screening decisions, claim mapping and a replayable receipt are absent.

Prior-art material named: Existing literature: Information integrity; platform governance; network science; computational social science; trust and safety; media forensics; human rights; behavioural science; literature specific to Adversarial Reputation Attacks. References: NIST AI Risk Management Framework — https://www.nist.gov/itl/ai-risk-management-framework; OECD AI Principles — https://oecd.ai/; Australian Human Rights Commission — https://humanrights.gov.au/; EU AI Act information — https://digital-strategy.ec.europa.eu/

Critical gap / next action

Create and attach a dated prior-art search log; lock the protocol; execute the proposed study; link raw data and analysis; then replace the results and conclusion placeholders with evidence-bounded findings.