Ethical, Legal, and Social Issues Arising from Big Data and Artificial Intelligence

PUBLIC CONSULTATION ON ETHICAL, LEGAL AND SOCIAL ISSUES ARISING FROM BIG DATA AND ARTIFICIAL INTELLIGENCE USE IN HUMAN BIOMEDICAL RESEARCH

1. The Bioethics Advisory Committee (BAC) will conduct a public consultation to seek views and feedback on the ethical, legal and social issues arising from the use of big data and Artificial Intelligence (AI) in human biomedical research. The online consultation will be conducted from 2 May 2023 to 14 July 2023 and can be accessed from here or REACH Portal.

Ethical Challenges of Big Data and AI Use in Human Biomedical Research

2. With advances in information technology, data and computational analytics in recent decades, the use of big data and AI in human biomedical research is becoming increasingly important, enabling researchers and healthcare professionals to analyse massive datasets, generate useful insights, and enhance data-driven decisions. While the growing use of big data and AI in biomedical research promises benefits, it also raises ethical issues such as the need for protecting data privacy versus ensuring societal benefit; the importance of obtaining informed consent and respecting the individual’s rights and autonomy; and the extent of data security obligations with respect to the value of data.

Aim of Consultation

3. Recognising these ethical challenges, the BAC has developed a public consultation paper ‘Big Data and Artificial Intelligence Use in Human Biomedical Research’ to discuss the ethical principles underpinning the use of big data and AI applications in human biomedical research and selected use-cases in clinical research, such as respect for persons, solidarity^[1], justice, proportionality^[2] and sustainability. Other ethical considerations are also discussed, including integrity, transparency, accountability, consistency and stakeholder engagement. The paper is intended to guide academics, researchers, healthcare professionals, Clinical Ethics Committees (CECs) and Institutional Review Boards (IRBs) on the ethical use of big data and AI applications in human biomedical research. Other clinical or healthcare aspects will not be covered within the scope of the paper.

4. The report builds upon previous BAC reports and recommendations and references relevant legislations such as the Personal Data Protection Act (2012) and the Human Biomedical Research Act (2015), and complements other big data and AI reports and ethical guidelines in Singapore to provide guidance to decision-makers who work with big data in health and research, codify good practice and support the safe growth of AI in biomedical and healthcare research.

5. The views of the public, academic, research, and healthcare institutions, CECs, IRBs, professional bodies and societies, and other interested organisations will assist the BAC in developing its recommendations.

Scope of Consultation

6. The public consultation paper covers ethical issues arising from the use of big data and AI in human biomedical research, such as responsible data usage, data ownership, custodianship, and stewardship, data privacy, accessibility and security, data anonymisation and other ethical considerations and issues specific to AI. Please refer to Annex A for the outline of key ethical questions and issues raised in the consultation paper.

Period of Consultation

7. The public consultation will last for a period of two months from 2 May 2023 to 14 July 2023. All comments should be sent in by 14 July 2023. Any comments received after 14 July 2023 will not be considered.

Feedback Channels

8. The public consultation paper can be accessed from the BAC website or REACH Portal. Members of the public are invited to provide feedback on the issues discussed in the public consultation paper through the following channels:

a. By email to:
<bioethics_singapore@moh.gov.sg>

b. By post to:
Biomedical Ethics Coordinating Office
1 Maritime Square
#09-66 HarbourFront Centre
Singapore 099253

c. By online feedback form at:

• Respondent's Form

• scan QR code below:

9. There will be focus group discussions conducted virtually via Zoom in June 2023. The BAC will invite representatives from various academic, research and healthcare institutions, CECs, IRBs, professional bodies and societies, as well as industry partners to participate in these sessions.

Summary of Response

10. A summary of the main comments/feedback received, together with the final report and recommendations will be published on the BAC website and REACH Portal in end 2023.

Footnotes

^[1]Solidarity reflects the willingness and moral obligations of individuals to share the costs associated with research participation, such as potential risks, in return for the common good.
^[2]Proportionality requires that the methods or processes used in biomedical research are necessary and appropriate in relation to the research intent and the range of public and private interests at stake.
^[3]Genomic data is the DNA data of organisms that reveal how differences in DNA affect human health and disease. Whole genomic data i.e., Whole Genome Sequence refers to the entire DNA of the genome of an organism and Whole Exome Sequence refers to the protein coding sequences from the genome.
^[4]Examples of data treatment controls include removal of direct identifiers such as national identification numbers, names, and addresses, and generalisation of indirect identifiers such as age and demographic data.
^[5]Examples of managerial controls include applying access controls such as physical, technical and/or administrative measures to restrict access to only authorised parties, designing approval processes and process controls that minimise risks of collusion, and prohibiting unauthorised re-identification of individuals.
^[6]Transparency refers to the need to openly inform and communicate with various stakeholders, at all stages of the AI system’s development and implementation, how the AI system is designed, developed, and applied.
^[7]Explainability of AI systems refers to the interpretability of input, output, and behaviour of the AI model and how it contributes to the outcome of the prediction.
^[8]An explainable AI model enables biomedical researchers, users, and patients to understand the AI models’ predictions and how the outcomes were derived.

Press Release (PDF)