Ethical Data Collection for AI Implementation

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.