Ethical Data Collection for AI Implementation

Course details

For any AI implementation, data collection is the first major computation stage within the AI development lifecycle. The quality, trustworthiness, and long-term value of AI-powered products hinges on incorporating ethical practices, which includes maintaining transparency and accountability. Ethical considerations include respecting the rights and privacy of individuals whose data is being collected, avoiding data misuse, and ensuring fairness while building trust. In this course, instructor Brandeis Marshall covers key strategies that reinforce ethical data collection management, respect people's autonomy, and comply with legal regulations. Along the way, gather insights on the impact of implementing these strategies on knowledge workers—and learn how to address their concerns.

Learning objectives
Clarify the beneficial relationship between effectiveness and ethics in data collection.
Understand the ethical strategies that can be implemented during data collection.
Summarize the common tensions faced by AI development teams.
Evaluate data collection practices from both the consumer and developer perspectives.

Concepts

Introduction

Ethics comes before the data

Balancing Effectiveness and Ethics

Defining ethical objectives and key results
Developing a data governance framework
Implementing data protection measures
Selecting data collection tools

Ethical Strategies for Responsible Data Collection

Obtaining explicit and ongoing data collection consent
Protecting privacy through anonymization and encryption
Mitigating biases in data
Following ethical review processes

Practical Concerns Blocking Responsible Data Collection

Violating data privacy regulations
Lacking an established data strategy
Choosing the right tools and configurations
Mishandling data assets and the fear of messing up

Conclusion

Next steps