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責任あるAIデータマネジメント

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更新日 2026/05

AIプロジェクトの全工程と運用後までを見据え、データを責任を持って管理する理論を学びます。

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TheoryArtificial Intelligence

1時間

16 ビデオ

51 演習

3,500 XP

9,317

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コース説明

前提条件

Supervised Learning with scikit-learn

1

Introduction to Responsible AI Data Management

Learn about the fundamental theory behind responsible data management in AI. You’ll review key dimensions such as security, transparency, fairness, and more before conceptualizing the metrics and challenges associated with these dimensions and understanding how to balance responsible AI with other business and technical requirements.

Responsible data dimensions

Developing AI responsibly

Dimensions of responsible data

Responsible AI metrics

Planning a responsible AI project

Responsible data use

Fairness in AI projects

Challenges of responsible AI

Trade-offs in responsible AI

Professional duties and ethical conduct

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2

Regulation Compliance and Licensing

Data regulation is essential to the legality of any AI project. Learn about key regulations, third-party licenses, and compliance strategies for informed consent and data-sharing agreements (with legal counsel). Finally, you'll learn about developing robust data governance strategies and management plans to ensure your project remains compliant throughout its lifecycle.

Overview of data regulation

Data protection laws

Data regulation in a project

Data compliance

Data owner rights and compliance

Data use agreements (DUAs)

Third-party licensing

Types of licenses

Licensing agreements

Selecting a license

Data governance and data management plan (DMP)

Data management

Components of a DMP

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3

Data Acquisition

Navigate through the responsible selection and integration of data sources by understanding the importance of data origin, nature, and temporality, emphasizing legal compliance, diversity, and fairness. By exploring types of bias and their origins, you’ll look at data fairness and representation to create a comprehensive dataset for modeling.

Data sources

Data source types

Data source and responsible dimensions

Data source limitations

Limitations in data sources

Data sources and bias

Data source selection

Evaluation of data sources

Data augmentation

Data integration

Data integration steps

Risks and benefits of data integration

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4

Data Validation and Bias Mitigation Strategies

Understand data audits, data validation, and bias mitigation. Data pre-processing and catching bias in modeling do not sound like fun, but let's streamline them with common approaches and trusted techniques!

Data audit in the project lifecycle

Need for data audit

Data validation

Defining data validation

Data validation approaches

Data validation best practices

Subgroup analysis

Data validation in the project

Pre-processing and bias

Bias mitigation

Mitigation strategies

Bias mitigation throughout project lifecycle

Consequences of bias mitigation

Post-deployment bias

Congratulations!

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