Investing in AI research and development (Principle 2.1)

Scientific breakthroughs enabled by AI could help solve societal challenges and create entirely new industries. These possibilities underscore the importance of basic research and of considering long time horizons in research policy. While the private sector has taken the lead in applied AI R&D investments in recent years, governments, at times complemented by foundations focused on public good, have an important role to play in providing sustained investment in public research with long-term horizons. This type of investment is essential to driving and shaping trustworthy AI innovation and ensuring beneficial outcomes for all, particularly areas under-served by market-driven investments. Publicly funded research can help address challenging technological issues that affect a broad range of AI actors and stakeholders. AI research includes research in: AI applications, such as natural language processing; techniques to teach AI systems, such as neural networks; optimisation notably to reduce the amount of data required for AI development, such as one-shot-learning; and research addressing societal considerations, such as transparency and explainability, as well as technologies to protect data integrity.

In addition, because AI has broad reach and pervasive implications on multiple facets of life, this recommendation calls for investment in inter-disciplinary research, on social, legal and ethical implications of AI that are relevant to public policy.

Due to the importance of data to the AI system lifecycle, a key element in ensuring further and better AI R&D is the availability of open, accessible and representative datasets that do not compromise privacy and personal and consumer data protection, intellectual property rights and other important rights. In particular, while it may be impossible to achieve a completely “bias free” environment, by providing (and providing incentives for) representative datasets that are publicly available, governments can contribute to mitigating the risks of inappropriate bias in AI systems. For example, AI systems using datasets that are not sufficiently representative in accordance with the system’s intended use, even without the intention to discriminate.

This recommendation complements the recommendation on fostering a digital ecosystem for AI (2.2), since long-term investment in digital technologies and infrastructure and mechanisms for sharing AI knowledge are means to fostering this digital ecosystem. In particular, investment in open, accessible and representative datasets facilitates sharing of AI knowledge.