AI, big data, and the future of consent

Area of Science:

• Data Ethics
• Information Governance
• Bioethics

Background:

• Current Big data practices often conflict with the principles of informed consent for personal information use.
• The ethical regulation of biomedical research provides a framework for understanding informed consent challenges in Big data.

Purpose of the Study:

• To identify and analyze problems that erode informed consent in Big data practices.
• To propose solutions for enhancing ethical oversight of personal data usage in Big data contexts.

Main Methods:

• Comparative analysis of informed consent in biomedical research versus Big data practices.
• Identification of three key problems: transparency, data repurposing, and meaningful alternatives.
• Exploration of 'soft governance' models and user-friendly consent mechanisms.

Main Results:

• Big data practices face significant challenges in upholding informed consent due to issues of transparency, data repurposing, and lack of meaningful alternatives.
• Existing ethical frameworks for biomedical research highlight deficiencies in Big data's approach to consent.

Conclusions:

• Implementing 'soft governance' ethical regulation, similar to Australia's Human Research Ethics Committees (HRECs), could improve Big data consent.
• Adopting user-friendly alternatives to standard consent forms and privacy policies, such as pictorial legal contracts, is recommended.