#40 Human and digital - solving "messy problems" with Vivienne Ming

"Ai is just a powerful set of tools, what we really need is to understand human problems better.."

A thought provoking and insightful discussion with Vivienne Ming on seeking to solve the world's messiest human problems using technology, in companies and in communities, by creating a more inclusive world. We discuss the power of AI and technological tools coupled with an understanding of just how ward it is to tackle human problems such as economic inequality, sexism or discrimination. How can we measure this untapped human potential and use it for philanthropic ends ?

Messy problems have messy solutions - the answer is always more than a simple yes or no - as technology advances, nothing is that binary, and neither are humans. How to manage this increasing complexity and bring our best selves to our everyday lives ? How can we harness the potential of collective intelligence differently ?

We discuss and explore these questions and more as Vivienne shares her stories, insights and research on this fascinating and complex subject.

The main insights from this episode are :

• The world depicted by science fiction is not always so wonderful, but is some of it achievable? Whilst society has limitations, there is always the idea that something could be better. 
• Also seeking to channel energy and expertise into philanthropic, profoundly human projects for the greater good (e.g. in the fields of education, public health) – why not build it so people can use it? But this could easily become dystopian…
• AI is a powerful set of tools, but we can’t do everything with it. There is a lack of understanding about just how hard it is to tackle human problems, such as economic inequality or sexism, for example. People do not always act rationally.
• There is no such thing as ‘AI dust’ – the current challenge is technology utopianists claiming problems will be solved vs. those advocating the wholesale banning of technology. We are both scared and excited by technology, so what do we do with it? 
• We must make good choices and take responsibility; we must limit the negative impact and ensure that no one group suffers. Messy problems have messy solutions, and we must decide what is right and what is wrong. The answer is always more than a simple yes or no - as technology advances, nothing is that binary, and neither are humans.
• We must bring our best selves to our lives every day and create an environment that allows us to do so. We must realise that our best self is not perfect, but we still have to try. We should not aim too high nor feel like a failure - life is not perfect and that is not the purpose.
• Scientists conducting research hope to be ‘less wrong’ than their predecessors. Nothing is a shock - science is never right, and its dirty secret is that (simple) truisms are hardly ever 100% accurate but they generalise, look for patterns/clues and are based on heterogeneity.
• When it comes to collective intelligence, what makes a group smart? The biggest predictor is how diverse the group is. In developmental psychology, enriched, i.e. diverse, environments produced bigger brains - more thoughts, more emotional resilience, more cultural enrichment; (how) can these positive interactions be economically productive?
• Looking at peoples’ potential, what is their uniqueness that will make a difference? Most people will not have the opportunity in life to make a difference, but why is it so scary for those who do to share the good fortune they have and allow others to try?
• ‘All of our lives would be better if all of our lives were better.’ There is an enormous amount of untapped human potential in the world, and this must change: it is not us vs. them, but us vs. nothing or us vs. ourselves: everyone can give back if they are given the chance to do so. 
• We must deliberately find our world’s problems (in areas such as education, ethics, AI) and take an ‘intelligently messy’ approach to solving them. It is about solving problems, not about the person solving them and Socos Labs is well placed to help. 
• There are many smart inventions that are not in use, and not everyone works with big data, but things are predictable, and our eyes are the best indicators of the future. Vivienne built herself a superpower: it was imperfect, complicated and messy but it improved lives. It is not about patents, licenses, or making money, it is about helping overcome challenges.
• Predictive models can be better than their real-life equivalents, giving rise to the idea of cyborgs, which could be reality, not just science fiction. We should not say no to something that might offer improvement - no is as much of an ethical choice as yes, but if you say no, people could die if something is not invented (in the field of medicine, for example). 
• We must change the definition of what it is to be human while we can still make choices for ourselves (before Musk’s, Zuckerberg’s or Google’s neuroscience gets into our heads!). Technology and entrepreneurship for good have a huge impact on a collective scale, 
• A simple solution is not a solution; models are extremely complicated and full of interdependencies. And all systems are about tension – imperfect and constantly adapting - and we must accept that messiness means tension. In the face of uncertainty, how can leaders/entrepreneurs/scientists/philanthropists make a difference here? 
• Just do it or build it! Understand that it won’t work initially but multiple attempts to solve the problem will lead you to understand it. No one is smart enough to outthink reality – it’s too big and messy (like the human brain!).
• Start by looking at the research - the problem is not new and there have been previous intelligent and knowledgeable attempts to solve it. There are powerful reasons that must be understood as it is never an obvious thing that needs fixing.
• Ideas are not always fanciful, and we must find our way to clever ones and make them meaningful by building nudging systems that make small differences: live the problem, collect the data, make decisions and observe.
• Example of the gender pay gap – why do women make less ambitious work choices? It seems irrational but aligns with how the choices pay off in reality - otherwise why would you invest in it? The Inclusion Impact Index uses data and AI to communicate what marginalised groups have achieved, also in terms of financial consequences, economic activity, real and potential impact. 
• But what do we actually do to create jobs, file patents or register inventions? It requires funding and amazing people. Socos Labs has a causal model in development; numbers are meaningful to the global economy and specific programs to help are the way forward. 
• Hard choices have to be made but the world would be a better place if we build things, show that they work and give them away.