Design for data ethics: using service design approaches to operationalize ethical principles on four projects

Area of Science:

• Data Science Ethics
• Social Good Applications
• Algorithmic Societal Impact

Background:

• Ethical frameworks guide data projects but don't offer definitive answers.
• Public perception of data ethics is nuanced, balancing public good and privacy.
• Existing frameworks are evolving to provide more practical tools.

Purpose of the Study:

• To explore the evolution of data science ethical frameworks.
• To highlight the need for practical guidance in ethical data projects.
• To propose service design approaches as a method for enhancing data project ethics.

Main Methods:

• Review of the emergence of data science ethical frameworks.
• Analysis of the updated UK Government Data Ethics Framework.
• Case studies applying service design tools to data projects.

Main Results:

• Ethical frameworks are increasingly recognized as needing practical application.
• Service design offers a 'wrap around' toolkit for making data projects more ethical.
• Practical application of service design tools demonstrated potential for ethical enhancement.

Conclusions:

• Service design approaches can significantly improve the ethical execution of data projects.
• Integrating service design with ethical frameworks offers a pathway from 'what to do' to 'how to do it'.
• This approach is valuable for data scientists, analysts, and policymakers working with data for social good.