Bias Detection
What is Bias Detection?
The process of identifying and measuring systematic biases in AI models and their outputs. Bias detection involves statistical testing across protected characteristics such as race, gender, age, and disability.
Terms that appear alongside bias detection
Each of these is named in at least one of the same controls as bias detection. The number is how many controls name both.
- governance 4 shared controls
- eu ai act 3 shared controls
- audit 3 shared controls
- use case 3 shared controls
- nist 2 shared controls
- compliance 2 shared controls
- data governance 2 shared controls
- model validation 2 shared controls
Frameworks that govern bias detection
What the standards actually require on bias detection
Requirements naming bias detection across 5 standards, quoted from the control text.
Fairness (Kouseisei 公正性) is the third of 10 Principles per Japan AI Guidelines for Business + builds on Social Principles of Human-Centric AI 2019 + Cabinet Office Council for Science Technology and Innovation guidance.
JP-AIG-Fairness-Bias-Detection-Mitigation-Inclusive-AI-Discrimination-Prevention-10-Principles-2019-Heritage · Japan AI Guidelines Fairness + Bias Detection + Mitigation + Inclusive AI + Discrimination Prevention + 10 Principles 2019 Heritage + Protected Attributes + Disparate Impact + Statistical Parity + Counterfactual Fairness →Per UK AI Principles: Bias detection and mitigation + AI model validation and testing + fairness across protected characteristics + ongoing monitoring.
UKAI-3 · Bias Detection, Fairness, Validation →GAMP 5 2nd Edition (July 2022) key updates + FDA Computer Software Assurance (CSA) coordination. AI/ML SYSTEMS: dedicated guidance on validation of AI/ML in pharma (predictive maintenance + image analysis + drug discovery + clinical decision support);
GAMP5-2nd-Edition-AI-Cloud-Agile-CSA · 2nd Edition (2022) - AI/ML, Cloud, Agile, DevOps and Computer Software Assurance (CSA) →Clauses 7 + 7.1 establish ethical requirements definition and traceability. Per public IEEE 7000-2021 abstract + Wikipedia + academic literature (full IEEE text NOT reproduced): translate prioritised ethical values + stakeholder concerns into system requiremen...
IEEE7000-EthicalRequirements-ValueBased-Traceability-DesignIntegration · IEEE 7000 Clauses 7 + 7.1 - Ethical Requirements Definition + Value-Based Requirements + Traceability + System Engineering Design Integration + AI Validation →Operate data governance underpinning trustworthy AI per OECD Principles. Data governance must address (a) training data quality and governance with documented sourcing + provenance + consent + licensing + curation + quality controls + (b) data bias assessment...
OECDAI-5 · Data Governance, Training Data Quality, Privacy, and Bias Mitigation →Questions people ask about bias detection
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