Looking before we leap?

Research in the fields of artificial intelligence (AI) and data science is often quickly turned into products and services that affect the lives of people around the world. Research in these fields is used in the provision of public services like social care, determining which information is amplified on social media, what jobs or insurance people are offered, and even who is deemed a risk to the public by police and security services.

Since products and services built with AI and data science research can have substantial effects on people’s lives, it is essential that this research is conducted safely and responsibly, and with due consideration for the broader societal impacts it may have.

However, the traditional research governance mechanisms that are responsible for identifying and mitigating ethical and societal risks often do not address the challenges presented by AI and data science research.

In many corporate and academic research institutions, one of the primary mechanisms for assessing and mitigating ethical risks is the use of Research Ethics Committees (RECs), also known in some regions as Institutional Review Boards (IRBs) or Ethics Review Committees (ERCs).

However, the current role, scope and function of most academic and corporate RECs are insufficient for the myriad of ethical challenges that AI and data science research can pose. For example, the scope of REC reviews is traditionally only on research involving human subjects.

Our recent report explores the role that academic and corporate RECs play in evaluating AI and data science research for ethical issues, and also investigates the kinds of common challenges these bodies face.

The report draws on two main sources of evidence: a review of existing literature on RECs and research ethics challenges, and a series of workshops and interviews with members of RECs and researchers who work on AI and data science ethics.

Some of the questions discussed in this event include:

1. How well are RECs at universities and private AI labs addressing the full range of risks that AI and data science research pose?
2. What are the most common types of risks and challenges that AI and data science research are posing for RECs?
3. How might RECs need to change their structure and makeup to assess for these risks?
4. How can RECs assess for the broader societal impacts or downstream risks of AI and data science research?
5. What kinds of funding or support do RECs need to create more assurance that AI and data science research is safe?

You can watch a recording of the event below.

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