Pinar, is it fair to say that there are two major threads in your work that reflect somewhat different intellectual tradition?  The first is on social entrepreneurship, which tends to emphasize how broader social goals are advanced through entrepreneurship.  There’s a very strong historical emphasis at Said Business School in this area.   The second is on AI and digital transformation, which also has roots at Oxford, perhaps more broadly across the University.   How do these fit together?

Pinar started thinking about the issues that arise in her work on AI and data when she was a doctoral candidate at Stanford many years ago.   She has always been interested in the issues that have shaped Silicon Valley and tech more broadly.   The value of data has been amplified extensively. 

At the same time, her interest in banking and data sharing has led her to think about the roles of smaller firms.  At Oxford, Pinar has been surrounded by social innovators – both entrepreneurs and faculty – who are expert in this area.   She has started working on inclusion by thinking with a colleague about how entrepreneurs in Zambia, for example, created access to water for children.  She has looked at financial inclusion in Ghana.     This has led her to think about how tech can improve access for low-income people in finance as well as healthcare and education.  She’s interested in Tech for Good.

Anita asked Pinar about the Initiative on Inspiring Female Founders as a mechanism for inclusion.

Pinar began this initiative during the pandemic as she thought about why females are rarely protagonists in the cases we teach.  This led her to write a number of free and widely available case studies on inspiring female entrepreneurs.  These are available to students in high school, for example, in places like China and Ghana. The goal is to create stories and demonstrate important paths to impact.

Anita asked Pinar how she navigates the tension between the fact that big tech tends to be quite centralizing of wealth, while social entrepreneurship focuses on the challenges of low-income people.

Pinar struggles.  It’s difficult.  She’s most interested in data plays.  Those who have data become wealthy, sure.  Those who don’t wont.   It’s difficult as an entrepreneur to know in advance what data you will have access to that will be valuable, particularly if you are an immigrant, a woman, and/or LGBTQ+, for example.   If you can’t get proprietary data, it’s difficult to achieve a competitive advantage.   Building trust is critical.  We need to start to think about rules around access to data.  International collaboration on data access is key.

In your advisory work with governmental authorities, what do you suggest on this front?

Pinar says that we’re not moving fast enough, but she is hopeful that progress is forthcoming.  She described how authorities have other priorities that have arisen in part from polarization of views.  She is not hopeful that we will get a breakthrough soon, but over the long run, there are many important options.  She is particularly positive about the EU’s YouAI Act, for example, which identifies unacceptance risks such as the manipulation of human behavior that causes harm, and social surveillance.  These risks are regulated now. We need more and faster progress along these lines.

Where does Pinar see the greatest opportunities for value creation through AI?

Part of Pinar’s research is on startups using AI to help large organizations through “Human in the Loop” processes in the training of algorithms.  She is hopeful that areas such as this can create opportunities for both small and large companies to win.

Pinar gets the last word.           

She is excited about inclusion through tech, such as to banking products, childhood learning, and better curriculum development.  AI may get us a lot of progress in these domains.