Responsible AI Data Management
Learn about responsible AI data management practices. Discover strategies covering all stages of an AI project to help you develop AI responsibly.
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Course Description
Responsible data management has become increasingly important nowadays. This course covers the basics of responsible data practices, including data acquisition, key regulations, main strategies to identify, and approaches to mitigate bias in data.
You will learn about the dimensions of responsible data, how they relate to fair AI, and what implications they may bring for stakeholders.
Learn About Regulatory Compliance and Licensing
Data is critical for AI, and you need lots of data. You will learn how to source data from various sources ethically. You will cover key regulations, licensing aspects, and ethical expectations.Master Identifying and Mitigating Bias in the Data
You will also learn about data validation and bias identification in data. You will put these concepts together by reviewing and applying bias mitigation strategies. By the end of this course, you will understand how to use data responsibly throughout all stages of an AI project and anticipate possible issues with deploying your AI model. You will look at your data management practices through a more critical lens and will be aware of potential issues that may arise to mitigate them at the start. Ultimately, you will be able to make better decisions and have more trust in your AI modeling results by applying the concepts and strategies covered in this course.For Business
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Introduction to Responsible AI Data Management
FreePrepare to master responsible data management in AI! To begin the course, you will learn about responsible data dimensions and some responsible AI metrics. Real-world examples will illustrate the challenges of balancing responsible AI with business factors and technical performance.
Responsible data dimensions50 xpDeveloping AI responsibly50 xpDimensions of responsible data100 xpResponsible AI metrics50 xpPlanning a responsible AI project50 xpResponsible data use100 xpFairness in AI projects100 xpChallenges of responsible AI50 xpTrade-offs in responsible AI100 xpProfessional duties and ethical conduct50 xp - 2
Regulation Compliance and Licensing
Data regulation is the cornerstone of the lawfulness of an AI project. This chapter delves into key regulations like GDPR and HIPAA, detailing compliance strategies for obtaining informed consent and establishing data-sharing agreements. Exploring various third-party licenses, you'll gain insight into selecting the right one for your dataset or model. Through crafting robust data governance strategies and management plans, you will master the basics of data regulation and compliance.
Overview of data regulation50 xpData protection laws100 xpData regulation in a project100 xpData compliance50 xpData owner rights and compliance50 xpData use agreements (DUAs)100 xpThird-party licensing50 xpTypes of licenses100 xpLicensing agreements100 xpSelecting a license50 xpData governance and data management plan (DMP)50 xpData management100 xpComponents of a DMP50 xp - 3
Data Acquisition
This chapter navigates the selection and integration of data sources within the context of responsible data practices. It highlights the importance of data origin, nature, and temporality, emphasizing legal compliance, diversity, and fairness. By exploring types of bias and their origins, we look at data fairness and representation to create a comprehensive dataset for modeling.
Data sources50 xpData source types100 xpData source and responsible dimensions50 xpData source limitations50 xpLimitations in data sources50 xpData sources and bias100 xpData source selection50 xpEvaluation of data sources100 xpData augmentation50 xpData integration50 xpData integration steps100 xpRisks and benefits of data integration50 xp - 4
Data Validation and Bias Mitigation Strategies
Diving into the data, let's embark on a final quest to 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 audit50 xpData audit in the project lifecycle50 xpNeed for data audit100 xpData validation50 xpDefining data validation50 xpData validation approaches50 xpData validation best practices50 xpSubgroup analysis100 xpData validation in the project50 xpPre-processing and bias100 xpBias mitigation50 xpMitigation strategies50 xpBias mitigation throughout project lifecycle100 xpConsequences of bias mitigation50 xpPost-deployment bias100 xpCongratulations!50 xp
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Audio Recorded By
Prerequisites
Supervised Learning with scikit-learnMaria Prokofieva
See MoreLead ML Engineer
I am a Lead ML engineer at the Mitchell Institute, Vic.
With PhD in Computer Science and CPA qualification, I work in the area of deep learning applications in business and healthcare. I love LLMs and fight for responsible AI.
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