AI’s capacity to democratise knowledge will ‘massively affect’ social impact outcomes

We spoke to Professor Matthew Grimes on his February visit to The Centre for Social Impact at the University of New South Wales (CSI UNSW) where he delivered a seminar on Artificial Intelligence and the future of entrepreneurship research.

Professor Grimes is a Professor of Entrepreneurship and Sustainable Futures and Co-Director of the Entrepreneurship Centre at Judge Business School, University of Cambridge. His visit is part of a 5-year knowledge-sharing collaboration between CSI UNSW and Cambridge Centre for Social Innovation.

What are some of the unexpected ways that social impact and social innovation is changing the global economic and organisational landscape? What trends are you observing in entrepreneurs tackling social problems?

One of the unexpected ways that social innovation and social impact are changing the global economic and organisational landscape is that they are beginning to challenge all organisations to begin to think more critically about how they account for multiple bottom lines from social to financial to environmental outcomes. These focal points are no longer just the purview of social innovators, and so the unexpected effect is that there’s this positive spillover, reorganising how we think about the nature of organisations and how we organise the for-profit and not-for-profit sectors more generally.

One of the more important trends is that these innovators are increasingly focused not just on introducing new products and services to create social impact but ultimately introducing systems change. So, there’s a growing emphasis around thinking about the broader inter-dependencies within local regions or within specific industries, and also thinking about how those systems can be reorganised to ensure the sustainability of the social impact or longevity of social impact. It’s not just about introducing new products; it’s about changing systems.

You have conducted research on impact investing with CSI UNSW’s Director Danielle Logue, can you talk about the difference between impact investing versus traditional financial investing in driving innovation? What are some of the global developments or conversations in this space that we should pay attention to?

I think both impact investing and traditional financial investing have the potential to increase innovation or constrain innovation, depending on how they’re set up. In some ways one of the advantages that impact investing has in terms of encouraging innovation is due to the nature of innovation itself.

Innovation, if you think about it, is really about the combination or recombination of different kinds of ideas, and impact investing, fundamentally, is about encouraging entrepreneurs to think more recombinationally about impacts. So, moving beyond single bottom line returns to think more critically about double and triple bottom lines. So that broadening out in terms of accounting, but also again in terms of considering broader systems, encourages individual innovators to think about those opportunities to recombine ideas from across systems.

“…when you think about the really hard problem within the impact investing space it’s about improving our measurement of impact.”

In terms of conversations or global developments …There are three trends that come to mind. One is, when you think about the really hard problem within the impact investing space it’s about improving our measurement of impact. So how do we reliably and accurately understand the impacts of a given intervention or a given innovation?

For a really long time, I would say that within the context of impact-focused ventures a lot of the measurement involved quite a bit of ‘hand waving’ because the impact measures themselves were difficult to obtain or unreliable, so innovators tried to offer qualitative insight to make up for the lack of precision. We’re seeing increases in the sophistication of those measurement approaches and standards across the space.

The second one is around the increased regulation around this space. As this new asset class has emerged and grown significantly, governments are trying to understand how to regulate this space more carefully. I think this works in combination with the increased precision and specification of impact measurement, because if we can more reliably measure impact then we can more reliably regulate this space to ensure that organisations that are claiming to deliver on impact are really doing so.

“social innovators, regardless of their industry, are looking to increase their creativity or increase the efficiency of their operations.”

And the third thing here is that impact investing has long been thought of as a separate asset class but increasingly with the move towards ESG and organisations’ disclosures of their impacts on SDG (sustainable development goals), for instance, there’s increasing consideration of impact within all asset classes/investments. So that’s another global development we should begin to pay attention to.

A lot of your research is in AI with much of the discourse on AI warning of the disruptive/catastrophic fallout of its misuse and/or hailing its unlimited potential for good.

How is AI currently being harnessed by entrepreneurs and business to create social impact and social innovation? How do you imagine AI facilitating improved social impact outcomes in the not-for-profit and for-profit sectors in the future?

I think the simplest answer to this—if you think about the nature of AI—what it’s really good at is navigating problems of information asymmetry and problems related to exploration. So, for example, in settings where the social impact is dependent on that exploration AI holds a lot of promise. For instance, in the pharmaceutical industry as organisations are searching for new drug discoveries for chronic or acute illnesses, AI has been incredibly useful in exponentially increasing the rate of drug discovery. On the product side it has the potential to really improve the quality of new products, but it also has the ability to improve efficiencies within organisational processes. So social innovators, regardless of their industry, are looking to increase their creativity or increase the efficiency of their operations. AI, of course, has the capacity to increase both of those. Better and more ideas and better efficiencies within operations.

In terms of imagining how AI might facilitate improved social impact outcomes in the not-for-profit and for-profit sectors in the future, I think that one of the key ways that it will do so is just in terms of the democratisation of knowledge. So much of how we distribute knowledge in the past has been through really inefficient distribution systems; people have to show up in a classroom setting and learn through pedagogical methods that are not necessarily customised to individual learning needs. I think social impact outcomes are going to be massively affected by the capacity to democratise knowledge, allowing more and more people to engage in innovation in socially impactful ways and ensuring that those social impacts are more sustainable as well.

Interview by Nicole Trian.