How data will shape the new urban future
On 18-19 November, I had the privilege of joining researchers from around the world at the 4th NYUAD Transport Symposium in Abu Dhabi to discuss new technologies and approaches in mobility, logistics, and mobility that could pave the way towards intelligent cities that can adapt to rapid urbanization in a digitizing society.
The consequences of this urbanization are immediate and pressing. The number of people living in cities around the world has risen from 751 million in 1950 to 4.2 billion today, and the United Nations estimates that this will increase to 6.7 billion by 2050. This massive increase in urban population, coupled with the continuing globalisation of supply chains and growing interconnectivity of economies, is presenting challenges and opportunities we have never witnessed before, from serving the world's megacities to fighting cyber-crime.
For example, the vast amounts of data being generated by what is commonly referred to as the Internet of Things, or IoT, coupled with machine intelligence and advances in computing power, are allowing us to gather and process huge quantities of data in real time. This is a boon for re/insurers like Swiss Re as it allows us to develop better models for predicting the frequency and severity of extreme events, from natural catastrophes and financial risks to health risks and the risks arising from the digitization of our societies.
However, these technologies also raise ethical and social concerns. Regulators, executives, and consumers should be wrestling with these material risks as they enjoy the benefits.
Threaded through all these digital conversations is data. I find it useful to analyse data as the new oil. And while many are focused on data extraction and distribution, most are under-investing in data refinement. Many people are gathering loads of data and hiring data scientists to build algorithms, but most aren't focusing on the curation of these data. This circumstance is similar to trying to run a modern car engine, with all its refinement and precision, on dirty, unrefined oil; the result is engine failure. Similarly, dirty data can create problems for machine-intelligence-enabled algorithms. Often noisy data degrades model performance, which will become more apparent to everyone as societies become more dependent on automation. Noisy data and poor algorithms or algorithmic malpractice will continue to create more system vulnerabilities as society becomes more digitized unless researcher, regulators, and companies spend more resources on systematically remedying these inadequacies. We need more standards developed and enforced when it comes to implementing automation. That is, we need more than just automation, we need intelligent automation.
Another overarching concern relates to data governance. If we don't focus on deciding which data we need, who owns the data, and how we share the value from the data, we will see a shift from an oil-based economy with all its dysfunctionalities to a new data-based economy with a whole new set of dysfunctionalities. Building a robust and sustainable digital society requires more than just technology, it needs clear thinking on frameworks for how institutions will need to transform to avoid the pitfalls experienced with prior societal transformations.
As we automate data processing, a critical question becomes how to best use humans. There are tasks a machine can do much better than us, like crunching vast amounts of information. But there are others where we perform much better than machines, such as critical decision-making when faced with a new problem. How to best augment humans is a core question of our times.
The ethics of algorithmic decision-making is also critical to building fair, resilient cities. As re/insurers, we are facing questions like: "Who is liable when a system fails?" e.g., when an autonomously driven car crashes due to algorithmic failure.
New developing technologies are beginning to converge into a digital ecosystem that provides the tools to resolve many of these conflicts and concerns. For example, Distributed Ledger Technologies (DLT) plus IoT can help us better manage processes e.g., tracking of supply chain disruption following a natural catastrophe such as the 2011 Tohuku tsunami in Japan; or tracking vehicle carbon emissions to produce real-time, auditable records of environmental impact.
In the insurance space, these technologies transform the full insurance value chain while providing tools to reduce fraud and speed up insurance pay outs based on pre-established triggers. This can be critical in the case of events like pandemics. Read more about how Swiss Re is working with the World Bank to support communities in the event of an Ebola outbreak.
What can be done today? What are the right regulations given the risks that we face? What are the ethical implications? How can we use current data and models to drive better behaviours and sustainable systems? How do we define ethics in a machine intelligence world? How much data should companies have access to? Most importantly, how do we use these new technologies and the digital society they produce to improve our lives in a sustainable, responsible way?
We need to work together in Public Private Partnerships, together with research institutions, to figure out the best models and solutions to ensure we incentivize / foster the right development. We want to create large, open access data repositories and work with the scientific community to co-create a better future.