We hear much about how building up our individual and collective resilience is needed to tackle challenges like COVID and climate change; how innovation and creativity can help us respond successfully to the challenges we face. As both a data ethicist and a data science educator, I have been spending a great deal of time speculating about the conditions needed to enable a modern, data-informed community to flourish in the midst of such crises. As I examine the challenges of data sharing, trust, and data governance in these contexts, I am seeing a growing need for a compassionate approach to data science practice. Humanist system design and evaluation can keep us mindful about the people and human practices ‘represented’ in the data collected, shared, analyzed, and stored as part of the work that we do, such as through human-machine partnerships.
This humanist approach of human-machine partnerships is at the heart of my current engagement with an international community of researchers, urban planners, and data professionals tackling the challenges of urbanism and ways to support the design of resilient, healthy, and “smart” cities. Urbanization is one of the great challenges and opportunities of this century. And while data science and AI have the potential to revolutionize the dynamics and structure of cities and the lives of those who inhabit them, there is growing concern about what might be getting overlooked in the process. If we want to build data systems that respect the citizens from whom the data is sourced, we should be taking into account the well-being of people from whom the data is taken in the first place and create public, shared data spaces. Public value and public inclusion need to be foregrounded to mitigate the risk of reiterating – or worse still, amplifying – inequities and distrust in the design of government services. When we design “smart city” technologies that foreground what “ought to be” done for a community, rather than what “can be done” with technological innovation, we begin to contribute to the building of resilient, sustainable, and compassionate urban communities.
Alongside the continued deployment of AI technologies in our world, we, therefore, need to ensure that those working with these technologies are trained to remain vigilant to the challenges of data as a proxy for human practice. Human experience remains richer than what can be codified within any AI or data technologies at our disposal. Having spent many years examining the anthropology of ideas and conditions that can kickstart individual and collective creativity, I have long been intrigued by the dynamic relationship between creativity, risk, and uncertainty. As an educator in the information and data science domains, I have integrated that creative-analytic focus on information and data ethics. And as an advocate for social justice, I design training to raise awareness about the issues of data justice and to help data professionals respond to public concerns related to the deployment and use of data-sharing platforms. What does it take for a modern city (and its inhabitants) to remain resilient in the face of challenges like climate change or disease outbreaks? How does a global city use data effectively to deal with situations where information will inevitably remain incomplete, uncertain, and dynamic? How can and should data serve the ultimate end goal of urban well-being?
I believe that a resilient smart city is one where human, as well as machine intelligence, are maximized; where humans and data technologies partner effectively to serve the needs of the community within a complex and ever-evolving sociotechnical ecosystem. Despite all the benefits likely to emerge for cities from the increased application of data technologies, there are inevitable (and critical) data limitations that must give us pause as we continue to expand the deployment of smart city technologies.
Cautionary tales about overlooking the human side of technological change of human-machine partnerships are not new phenomena. In an oft-cited essay written in the dawning of a nuclear age that saw the Second World War transform into the Cold War, Vannevar Bush, often credited with being one of the first visionaries to imagine what a hyper-connected world would look like, offers evocative descriptions of situations where machine intelligence could address the limitations of human actions:
“There may be millions of fine thoughts, and the account of the experience on which they are based, all encased within stone walls of acceptable architectural form; but if the scholar can get at only one a week by diligent search, his syntheses are not likely to keep up with the current scene” (Bush, 1945, Part 5).
Achieving faster, better information access, he surmised, was critical for humanity’s future.
Like the frightening world of the early Cold War era of the time of that essay, today’s pressing concerns about global security and safety seem to call out for better, more efficient, and more effective access to information about our surroundings if we are to solve many of the world’s pressing problems. And yet, even then, amidst his envisioning of machines that could support our thinking, Bush cautioned against any assumption about total reliance on them for all our thinking:
“Much needs to occur, however, between the collection of data and observations, the extraction of parallel material from the existing record, and the final insertion of new material into the general body of the common record. For mature thought there is no mechanical substitute” (Bush, 1945, Part 3).
For me, such imaginings of technology augmenting human cognitive activity point to the need to hold on to and nurture the creative, inventive qualities of the human mind even as we design machines that can support and extend our thinking.
The demand for data science professionals possessing the transformative capacity to effectively engage with exponential increases of information and the uncertainty characteristic of shifting knowledge landscapes is particularly acute given the challenges we are facing globally and individually in 2020. To derive the meaningful insights that transform data into information takes creativity and curiosity. Learning to tolerate uncertainties not only supports our creative capacities as individuals and as a community; it also plays a big part in our individual and collective resilience. How do we hold on to and nurture the creative, inventive qualities of the human mind even as we design machines that can support and extend our thinking? Even more important, what can we do to reduce social inequity and not amplify it as we choose which problems to solve and how to tackle them? To build and work with these AI technologies for the benefit of human and planetary flourishing, our individual and collective capacities for compassion and imagination must be nurtured alongside technical know-how. I am excited to have the opportunity to engage the ODSC audience in a conversation about ways we might Rethink AI, using these questions as a launchpad for a set of key operating principles for keeping the human in our technology design as we design and deploy machines that can support and extend our thinking.
Editor’s note: Theresa is a speaker for ODSC APAC 2020. Check out her talk, “Human-Machine Partnerships to enable Human and Planetary Flourishing,” there.
Reference for human-machine partnerships:
About the author/ODSC speaker on human-machine partnerships. As a data and information ethicist, Theresa uses creative, compassionate, and contemplative practices to help communities build better digital and data futures. Building consensus through gaining and maintaining a community’s trust and implementing good practice to advance socially-just data policies is embedded in her work. Her award-winning work as an educator and researcher engages with the ever-evolving relationship between people and emerging technologies when working with data and making decisions. A social informaticist with a PhD in Information Science, she served as inaugural Director of the Master of Data Science & Innovation program at UTS from 2014-2018, leading the development of a uniquely transdisciplinary and human-centered curriculum that continues to prepare graduates for the demands of the data science fields. Now working as a freelance consultant, Theresa contributes to government, industry, and NGO efforts advancing socially-just data policies, building processes for gaining and maintaining a community’s trust in data/AI use. She recently joined the Standards Australia Data Sharing Committee (IT-027-06 and JTC 1/SC 32/WG 6). Theresa also contributes to international initiatives related to data sharing via the International Science Council’s Committee on Data and as a Sydney Ambassador for Stanford’s Women in Data Science Network.