Bias mitigeren tijdens de projectlevenscyclus

Een AI-gezondheidsapp wil voedingsadvies en receptsuggesties personaliseren met behulp van gebruikersdata, waaronder leeftijd, gender en gezondheidsdoelen. De app analyseert invoer en gedrag van gebruikers en geeft gepersonaliseerde aanbevelingen die zich in de tijd aanpassen.

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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.

Exercise 1: Responsible data dimensions Exercise 2: Developing AI responsibly Exercise 3: Dimensions of responsible data Exercise 4: Responsible AI metrics Exercise 5: Planning a responsible AI project Exercise 6: Responsible data use Exercise 7: Fairness in AI projects Exercise 8: Challenges of responsible AI Exercise 9: Trade-offs in responsible AI Exercise 10: Professional duties and ethical conduct

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.

Exercise 1: Overview of data regulation Exercise 2: Data protection laws Exercise 3: Data regulation in a project Exercise 4: Data compliance Exercise 5: Data owner rights and compliance Exercise 6: Data use agreements (DUAs)Exercise 7: Third-party licensing Exercise 8: Types of licenses Exercise 9: Licensing agreements Exercise 10: Selecting a license Exercise 11: Data governance and data management plan (DMP)Exercise 12: Data management Exercise 13: Components of a DMP

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.

Exercise 1: Data sources Exercise 2: Data source types Exercise 3: Data source and responsible dimensions Exercise 4: Data source limitations Exercise 5: Limitations in data sources Exercise 6: Data sources and bias Exercise 7: Data source selection Exercise 8: Evaluation of data sources Exercise 9: Data augmentation Exercise 10: Data integration Exercise 11: Data integration steps Exercise 12: Risks and benefits of data integration

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!

Exercise 1: Data-audit Exercise 2: Data-audit in de projectlevenscyclus Exercise 3: Noodzaak van een data-audit Exercise 4: Datavalidatie Exercise 5: Data validation definiëren Exercise 6: Aanpakken voor gegevensvalidatie Exercise 7: Best practices voor datavalidatie Exercise 8: Subgroepanalyse Exercise 9: Datavalidatie in het project Exercise 10: Voorbewerking en bias Exercise 11: Bias beperken Exercise 12: Mitigatiestrategieën Exercise 13: Bias mitigeren tijdens de projectlevenscyclus

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Exercise 14: Gevolgen van bias-mitigatie Exercise 15: Bias na implementatie Exercise 16: Gefeliciteerd!