Okay, I need to write a Korean wiki-style document about "데이터 편향" ( Bias) under the category of Machine Learning in Artificial Intelligence. Let me start by understanding the and requirements.

First, the document should have a clear title (H1 The title is already given as "데이터 편향". Then the outline should include an 개요/소개 (Overview/Introduction) section. I should explain what data bias is, why it's important in machine learning, and maybe mention some real-world implications.

Next, detailed sections (H2, H3). Possible subtopics could be types of data bias, causes, impacts, detection methods, mitigation strategies. Each of these can be H2 sections. Under types, maybe list selection bias, measurement bias, etc. Under causes, maybe data collection methods, human biases. Impacts could be on fairness, model performance. Detection might include statistical tests, visualization. Mitigation could involve data augmentation, algorithms, fairness metrics.

Need to ensure accuracy and objectivity. I should check definitions and examples. For example, selection bias is when the sample isn't representative. Measurement bias is due to flawed data collection instruments. Maybe include examples like facial recognition systems performing worse on certain demographics.

Use professional terms but explain them. For instance, when mentioning "preprocessing", "in-processing", "post-processing" techniques, define them briefly.

Markdown formatting: Use H1 for title H2 for main sections, H3 for subsections. Use bullet points, tables if needed. For technical content, code blocks, but maybe not necessary here unless providing example code for detection methods.

Length should be at least 800 characters. Given the structure, this should be manageable. Need to write in Korean, ensuring.

Let me start drafting:

Title: # 데이터 편향

개요: 데이터 편향이란 머신러닝 모델 훈련에 사용되는 데이터에 시스템적으로 왜곡된 패턴이 존재하는 현상으로, 이로 인해 모델의 예측 결과에 불공정성이나 오류가 발생할 수 있는 문제를 설명합니다. 중요성을 강조하고, 예를 들어 얼굴 인식 시스템에서 특정 인종의 인식률이 낮은를 언급.

Types of Data Bias: H2 section. Then H3 for each type: Selection Bias, Measurement Bias, Confirmation Bias, etc. Each with explanation and examples.

Causes and Origins: Data collection methods, human biases, sampling errors. Maybe mention historical data reflecting past prejudices.

Impacts and Consequences: On model performance, fairness, legal and ethical issues. Use examples like hiring algorithms biased against women.

Detection and Measurement: Statistical metrics like demographic parity, equal opportunity. Tools or methods like data visualization, bias audits.

Mitigation Strategies: Data-level approaches (resampling, augmentation), algorithm-level (fairness constraints), post-processing adjustments. Maybe include frameworks like AI Fairness 360.

Related Concepts and References: Maybe link to fairness in AI, bias-variance tradeoff, related literature or guidelines.

Check for proper flow and ensure each section connects logically. Use Korean terms appropriately, maybe provide English terms in parentheses if needed. Avoid overly technical jargon without explanation. Make sure examples are relevant and clear.

Wait, should I include a section on case studies or real-world examples? The user didn't specify, but maybe under impacts or as a separate subsection. Alternatively, integrate examples within each section.

Also, need to ensure that the document is professional but accessible. Define terms like "algorithmic fairness" when first mentioned.

Potential issues to avoid: Inaccurate definitions, unsupported claims. Need to reference established concepts in ML literature. For example, the taxonomy of biases (selection, measurement, etc.) should be correctly attributed.

Let me structure each H2 section with H3 subsections. For example:

데이터 편향의 유형

선택 편향

설명과 예시

측정 편향

설명과 예시

확인 편향

설명과 예시

Similarly for other sections.

In the mitigation strategies, maybe use bullet points for different approaches.

Need to write in Korean, so terms like "preprocessing techniques" would be "전처리 기법" and explained.

I think that's a solid outline. Now flesh out each section with accurate information, examples, and explanations. Ensure that the content meets the length requirement and follows the markdown structure as specified.