A data ethics framework is a set of principles and guidelines that help organizations ensure that their data practices are ethical, responsible, and trustworthy. According to the IAPP CIPP/US Study Guide, some of the key components of a data ethics framework are1:
Data governance: the policies, processes, and standards that govern how data is collected, used, stored, and shared within an organization.
Preferability testing: the process of assessing the potential impacts and risks of data-driven solutions on stakeholders, such as customers, employees, and society.
Auditing: the process of monitoring, reviewing, and verifying the compliance and performance of data practices against the established ethical standards and legal requirements. Automated decision-making, on the other hand, is not a key component of a data ethics framework, but rather a data practice that may raise ethical issues and challenges. Automated decision-making refers to the use of algorithms, artificial intelligence, or machine learning to make decisions or recommendations without human intervention2. While automated decision-making can offer benefits such as efficiency, accuracy, and consistency, it can also pose risks such as bias, discrimination, lack of transparency, and accountability3. Therefore, automated decision-making should be subject to ethical evaluation and oversight, but it is not itself a part of a data ethics framework. References:
[IAPP CIPP/US Study Guide], Chapter 10, Section 10.4, page 287
[IAPP Glossary], Automated Decision-Making
IAPP Resources, Ethical Data Use and Automated Decision-Making: A Practical Guide
