Responsible Data Stewardship in Practice

Responsible Data Stewardship in Practice emphasizes the crucial role of data management in building ethical and trustworthy AI systems. This program explores best practices for data collection, storage, and usage throughout the AI lifecycle.

Recommended for:

Data scientists, engineers, and analysts involved in AI projects
Project managers and decision-makers overseeing AI development and deployment
Data governance professionals responsible for data security and privacy
Anyone interested in the ethical use of data for AI applications

You will:

Gain a comprehensive understanding of responsible data stewardship principles in AI development.
Explore the legal and ethical considerations surrounding data collection and usage in AI.
Learn about best practices for data collection, storage, and governance in AI projects.
Develop practical skills for managing data responsibly throughout the AI lifecycle.
Discover how to mitigate potential risks associated with data collection and usage in AI.

The AI Ethics and Governance in Practice series

Here is the list of titles available in AI Ethics and Governance in Practice series:

AI Ethics and Governance in Practice: An Introduction
AI Sustainability in Practice
AI Fairness in Practice
Responsible Data Stewardship in Practice
AI Safety in Practice
AI Explainability in Practice
AI Accountability in Practice

Detailed Overview

Responsible Data Stewardship in Practice tackles the critical aspect of data management in AI development.Developed by The Alan Turing Institute, this program recognizes that ethical AI relies heavily on responsible data practices. Data serves as the foundation for training and operating AI systems, and any issues within the data can have significant downstream consequences.

The program emphasizes key principles for responsible data stewardship in AI, including:

Transparency: Ensuring transparency in data collection practices and informing individuals about how their data is being used.
Fairness: Mitigating bias in data collection and usage to prevent discriminatory outcomes from AI systems.
Accountability: Establishing clear accountability for data collection, storage, and usage throughout the AI lifecycle.
Privacy: Prioritizing user privacy by obtaining informed consent and minimizing data collection to what is strictly necessary for AI functionality.
Security: Implementing robust security measures to protect data from unauthorized access, misuse, or breaches.

The program delves into specific aspects of responsible data stewardship throughout the AI development process:

Data Collection: Focusing on collecting data that is relevant, accurate, and obtained ethically with user consent.This includes minimizing data collection to only what is necessary for the intended AI function.
Data Storage and Management: Implementing secure storage practices to protect data from unauthorized access or breaches. This involves using appropriate access controls and encryption methods.
Data Preprocessing and Cleaning: Identifying and mitigating biases within the data used for training AI models. This can involve techniques like data cleaning, balancing datasets, and outlier removal.
Data Governance: Establishing clear governance frameworks to oversee data management practices within the AI project. This includes defining roles and responsibilities for data security, privacy, and access control.

By following these principles and implementing best practices, project teams can ensure their AI initiatives are built on a foundation of responsible data stewardship.

Responsible Data Stewardship in Practice empowers data scientists, engineers,and other stakeholders to manage data responsibly throughout the AI lifecycle. By prioritizing ethical data practices,developers can mitigate risks, build trust with users, and ensure AI systems deliver positive societal benefits.

The program acknowledges the ongoing evolution of data privacy regulations and the need for continuous adaptation. It encourages project teams to stay informed about evolving legal and ethical considerations surrounding data governance in AI.

Citation and Licensing

Leslie, D., Rincón, C., Briggs, M., Perini, A., Jayadeva, S., Borda, A., Bennett, SJ., Burr, C., Aitken, M., Mahomed, S., Wong, J., Hashem, Y., and Fischer, C. (2024). Responsible Data Stewardship in Practice. This workbook is published by The Alan Turing Institute and is publicly available on their website: https://www.turing.ac.uk/news/publications/ai-ethics-and-governance-practice-responsible-data-stewardship-practice

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