Code 8.7: Creating Incentives for Action – Research, Regulation and Rewards
Dr Zoe Trodd began this session by reminding participants that savvy use of statistics, data visualization and mapping technology has been at the heart of abolition efforts for over 200 years. As the rich variety of creative uses of AI, machine learning and technology discussed in previous sessions had shown, the challenge is not to conceive how these techniques and technologies may assist anti-slavery efforts; it is rather to think about how to incentivize that kind of approach, and ensure that the resulting effort adds up to more than the sum of its parts.
Professor Keith Marzullo began the discussion with an overview of how government, academic and private sector partners can interact to foster effective innovation and research collaboration. The traditional model, involving government providing funding inputs, sometimes in collaboration with private sector stakeholders, and the research community providing ideas, has been disrupted in recent years. Now, we see greater overlap between information, policy and technology sectors, with funding existing as a cross-cutting issue for all of them.
In response, research actors have developed several new strategies that help them achieve impact as innovation hubs:
Go large: framing research inquiries through institutionalized “centres” of excellence, whether real or virtual;
Go broad: defining a broad research agenda, where possible linked to larger policy puzzles, to foster broad impact. This often results in interdisciplinarity in research design and execution; and
Go experiential: making these initiatives highly participatory—not just the redoubt of technical experts in far-off ivory towers, but drawing in early career researchers, students, business and members of the broader community. This helps generate social buy-in and social capital around the initiative, as well as fostering discursive continuity and social sustainability.
Next, Dr Daniel Lopresti discussed the role of the Computing Community Consortium as a catalyst for computational research, through networked activity. Dr Lopresti described a number of activities that CCC undertakes that could be applied to help to catalyse research related to Target 8.7. CCC’s task forces offer small, agile, limited life-time initiatives for accelerating mobilization in particular areas. Its workshops can help develop new research agendas, ensuring buy-in from the North American computational science research community. CCC can also serve a match-making function, connecting computational research expertise to operational actors that need it. And it can foster foundational research to underpin the development of more advanced research and programming.
Third, Sophia Tu discussed efforts by IBM to develop secure data-sharing arrangements. In a first collaboration with Liberty Shared, STOP THE TRAFFIK, financial institutions and law enforcement, as part of its “purpose alongside profits” approach, IBM developed an app that allows “anyone, anywhere in the world” to report suspected cases of human trafficking, and has been used, said Ms Tu, to disrupt trafficking enterprises around the world. Now, IBM is developed an IBM-cloud-based “hub” for sharing and blending information from civil society, financial sector institutions and open source data, generally from news sources.
In response to the speakers’ presentations, I suggested that the speakers’ remarks pointed to the need to recognize that—like cancer or climate change—modern slavery is both a knot of complex research questions and an evolving public policy challenge. Unlike climate change and cancer, however, the Target 8.7 field lacks a clear understanding of the hierarchy of research questions that need to be answered to foster forward progress on this policy puzzle, and also lack access to the kind of national research strategies that we see in the fights against cancer and climate change.
Further elaborating this point, one participant in the discussion that followed suggested a need to consider how Code 8.7 could develop from a community of interest into a community of intent. Beyond the broad goal of fostering progress towards Target 8.7, what is the shared intent of this community? What are its research goals, and what is the use case for the applications of AI and machine learning it seeks to foster?
Participants also noted that the absence of some basic shared infrastructure—such as a secure, trusted data-sharing system—has impeded progress. This is, in part an incentives or public goods problem: as one participant noted, “Everyone wants to buy a car but no one wants to pay for the road.” Other participants noted that solutions to this problem may be emerging, for example in the IBM data-sharing infrastructure mentioned above, in Delta 8.7, in the information-sharing infrastructure emerging in Europe through EU and OSCE collaboration. Others pointed out that governments have a history of investing in public research infrastructure—CERN, for example—when presented with a strong case with clear potential public payoffs from the research, and clear buy-in from diverse stakeholders. Others noted that public-private partnership models might be conceivable through subscription-based analytic outputs, for instance. Finally, other participants saw a strong case for tech companies to get more heavily involved.
Some participants highlighted, however, that several recent attempts to mobilize the anti-slavery community around data-sharing have foundered on questions of incentives and trust. Even as Code 8.7 works to develop an overarching vision and framework for collaboration, it may be necessary to build trust up from the bottom, through practical instances of collaborative problem-solving. Here, Code 8.7 may have an important catalytic and match-making role.
Dr James Cockayne is the Delta 8.7 Project Director and Director of the United Nations University Centre for Policy Research.
This article has been prepared by James Cockayne as a contributor to Delta 8.7. As provided for in the Terms and Conditions of Use of Delta 8.7, the opinions expressed in this article are those of the author and do not necessarily reflect those of UNU or its partners.