'bias' 검색 결과

Evaluate research papers and scientific claims by examining study design, statistics, bias, and evidence quality. Receive a structured critique of strengths, weaknesses, supported conclusions, and practical improvements, with uncertainty and limitations made explicit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a paper's methods and conclusions
• Assessing evidence quality across studies
• Checking scientific claims in media reports

Generates the "Risk of Bias" results section for a meta-analysis based on assessment tables and statistics. Use when the user wants to draft the risk of bias analysis text from provided data tables.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guides the combined DPIA and AI Act conformity assessment for AI systems processing personal data. Covers EDPB-EDPS Joint Opinion 5/2021, training data lawfulness under Art. 6 and Art. 9, Art. 22 automated decision-making, algorithmic bias detection, and NIST AI RMF MAP function. Keywords: AI privacy, DPIA, AI Act, algorithmic bias, automated decision-making, Art. 22, training data, NIST AI RMF.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan 8-type / 3-category leakage taxonomy; the illegitimate-proxy / sampling-bias / pretraining-contamination subtypes hand off as needs_external_check, naming but NEVER running Oren-2023 exchangeability / Shi-2023 Min-K% / Golchin-2023 Time-Travel / BIG-bench canary); a load-bearing LLM judge is conflicted (same model/family as a compared system) or unvalidated (no human-agreement, no bias control) (HP-JUDGE-VALIDITY); a declared condition/metric is dropped or switched to favor the method, or 'best' is chosen with no held-out set (HP-SELECTIVE-REPORTING). Verdict-bearing at L0/L1 from the DESCRIBED protocol — NOT repo-gated like experiment-forensics; L2 only CONFIRMS against split/preprocessing/result files. A fresh cross-model reviewer (gpt-5.6-sol xhigh, read-only, fresh thread per pass) PROPOSES findings, each span-anchored to a ledger claim_id; tools/adjudicate_findings.py DECIDES the verdict. Leakage and under-reporting are usually HONEST methodological errors — every finding describes a discrepancy to CHECK, never an accusation. An LLM generating GROUND-TRUTH labels is HP-FAKE-GT (experiment-forensics) — routed there, not here. Emits eval-design-forensics.findings.json; computes NO verdict. Detect-only. Triggers: "eval design audit", "evaluation validity", "train/test leakage", "data leakage", "is the score measuring generalization", "LLM judge bias", "is the judge validated", "selective reporting", "cherry-picked results", "评估设计审计", "评测有效性", "数据泄漏", "训练测试集泄漏", "裁判模型有没有验证", "选择性报告".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Bias detection and mitigation, fairness metrics, privacy frameworks, consent models, transparency requirements, and accountability structures for data science practice. Covers algorithmic bias sources, disparate impact testing, differential privacy, GDPR principles, model cards, datasheets for datasets, responsible AI frameworks, and the organizational governance needed to make ethics actionable. Use when auditing models for bias, designing privacy-preserving systems, establishing governance processes, or evaluating the social impact of data-driven decisions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cross-session shared memory for agents. Use at session start to load the persistent bias-log via `mcp__memex__recent_observations`, mid-session to search prior context with `mcp__memex__search`, and immediately when a bias surfaces to log a new `observation` via `mcp__memex__add`. The six-layer reconstruction trail (raw → embedded → indexed → graphed → curated → reflexive) means memex stores facts AND the agent's own reasoning patterns across sessions. Prefer querying memex before re-deriving prior architectural decisions; re-derivation is itself evidence that the trail is incomplete.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Log a bias-log / vipassana observation to the memex store the moment a reasoning pattern surfaces, mid-session or at close. Use when you catch a bias in your own reasoning (anchoring, confirmation, trust-the-summary), when a pace tell or verification gap appears, or when meditate/observe produces a reflection worth persisting across sessions. Primary path pipes the body to `memex add` over stdin (`--title` required) then runs the `meditate-vipassana` extractor to make it queryable; secondary path appends a numbered bullet to `docs/OBSERVATIONS.md`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit AI systems for bias and fairness. TRIGGERS - Use when user needs help with ai-bias-audit related tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit AI systems for bias with testing frameworks and mitigation strategies. TRIGGERS - Use when user needs help with ai-bias-auditor related tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

"Apply behavioral finance theory to identify systematic investor biases and their impact on asset prices. Use this skill when the user needs to analyze irrational market behavior, explain pricing anomalies through cognitive biases, diagnose investor decision errors, or when they ask 'why do investors hold losers too long', 'how does loss aversion affect pricing', or 'what biases drive this market pattern'.".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when defining AI governance policies, designing human oversight workflows, documenting bias mitigation strategies, or meeting regulatory transparency requirements for Salesforce AI features. Trigger keywords: responsible AI, AI bias, AI audit trail, AI transparency, human-in-the-loop, AI risk inventory, AI disclosure. NOT for the MLOps pipeline or Policy-as-Code engine architecture — use architect/ai-governance-architecture. NOT for Trust Layer technical configuration — use agentforce/einstein-trust-layer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit AI systems for bias with testing frameworks and mitigation strategies. TRIGGERS - Use when user needs help with ai-bias-auditor related tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Audit AI systems for bias and fairness. TRIGGERS - Use when user needs help with ai-bias-audit related tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design criteria for using an LLM to judge AI responses through scoring or comparisons between two answers. Includes checks for order and length bias, plus validation against human judgments to assess evaluation reliability.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an automated evaluation pipeline for AI responses
• Compare responses after changing a prompt or model
• Define shared scoring criteria for reviewers

Review investment and business decisions using frameworks drawn from Charlie Munger’s public work. Examine failure paths, thinking biases, and incentives, compare opposing evidence, and identify questions that fall outside the available knowledge.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing how a business decision could fail
• Checking for interacting thinking biases
• Examining management incentives and risk

Use W&B to record machine learning experiments, visualize training, and compare results. The skill also covers automated searches for training settings and version tracking for datasets and models, helping teams share experiments and trace their inputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Logging training metrics and viewing progress in real time
• Comparing experiments with different training settings
• Searching automatically for better training settings

Evaluate language models with over 100 benchmark tests covering knowledge, math, coding, safety, and image understanding. Configure runs on Docker, Slurm computing clusters, or cloud infrastructure, compare models, and export results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models using the same benchmark tests
• Run large evaluations on a Slurm cluster
• Evaluate safety and image understanding

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

Review an analysis before sharing it with stakeholders. Check calculations, data selection, comparison methods, potential bias, charts, and whether conclusions follow from the evidence, then receive a readiness assessment and specific improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing an analysis before an executive presentation
• Spot-checking SQL results and aggregation logic
• Checking comparisons across different time periods

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Set up W&B to record machine learning metrics and settings, visualize training progress, and compare experiments. Includes automated searches for training settings, version tracking for datasets and models, and shared reports for team collaboration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording and visualizing training metrics
• Comparing experiments with different training settings
• Automatically searching for better training settings

Helps extend how much text a language model can process at once. Covers choosing RoPE, YaRN, ALiBi, or position interpolation, implementing position information, fine-tuning on longer documents, and managing memory during inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extend an existing model to handle longer documents
• Compare context extension methods
• Implement position information in attention layers