Stefaan Verhulst

Report by Accenture/NASCIO: “…exploring the future role of the state CIO and how the state CIO will drive innovation.

The research included interviews and a survey of state CIOs to understand the role of state CIOs in promoting innovation in government.

• The study explored how state IT organizations build the capacity to innovate and which best practices help in doing so.
• We also examined how state CIOs embrace new and emerging technologies to create the best government outcomes.
• Our report illuminates compelling opportunities, persistent obstacles, strategies for accelerating innovation and inspiring real-world case studies.
• The report presents a set of practical recommendations for driving innovation…(More)”.

Policy Lab (UK): “….Compared with quantitative data, ethnography creates different forms of data – what anthropologists call ‘thick data’. Complex social problems benefit from insights beyond linear, standardised evidence and this is where thick data shows its worth. In Policy Lab we have generated ethnographic films and analysis to sit alongside quantitative data, helping policy-makers to build a rich picture of current circumstances. 

On the other hand, much has been written about big data – data generated through digital interactions – whether it be traditional ledgers and spreadsheets or emerging use of artificial intelligence and the internet of things.  The ever-growing zettabytes of data can reveal a lot, providing a (sometimes real time) digital trail capturing and aggregating our individual choices, preferences, behaviours and actions.  

Much hyped, this quantitative data has great potential to inform future policy, but must be handled ethically, and also requires careful preparation and analysis to avoid biases and false assumptions creeping in. Three issues we have seen in our projects relate to:

• partial data, for example not having data on people who are not digitally active, biasing the sample
• the time-consuming challenge of cleaning up data, in a political context where time is often of the essence
• the lack of data interoperability, where different localities/organisations capture different metrics

Through a number of Policy Lab projects we have used big data to see the big picture before then using thick data to zoom in to the detail of people’s lived experience.  Whereas big data can give us cumulative evidence at a macro, often systemic level, thick data provides insights at an individual or group level.  We have found the blending of ‘big data’ and ‘thick data’ – to be the sweet spot. 

Policy Lab’s work develops data and insights into ideas for potential policy intervention which we can start to test as prototypes with real people. These operate at the ‘meso’ level (in the middle of the diagram above), informed by both the thick data from individual experiences and the big data at a population or national level. We have written a lot about prototyping for policy and are continuing to explore how you prototype a policy compared to say a digital service….(More)”.

Paper by Andrew Guthrie Ferguson: “Predictive policing is changing law enforcement. New place-based predictive analytic technologies allow police to predict where and when a crime might occur. Data-driven insights have been operationalized into concrete decisions about police priorities and resource allocation. In the last few years, place-based predictive policing has spread quickly across the nation, offering police administrators the ability to identify higher crime locations, to restructure patrol routes, and to develop crime suppression strategies based on the new data.

This chapter suggests that the debate about technology is better thought about as a choice of policing theory. In other words, when purchasing a particular predictive technology, police should be doing more than simply choosing the most sophisticated predictive model; instead they must first make a decision about the type of policing response that makes sense in their community. Foundational questions about whether we want police officers to be agents of social control, civic problem-solvers, or community partners lie at the heart of any choice of which predictive technology might work best for any given jurisdiction.

This chapter then examines predictive policing technology as a choice about policing theory and how the purchase of a particular predictive tool becomes – intentionally or unintentionally – a statement about police role. Interestingly, these strategic choices map on to existing policing theories. Three of the traditional policing philosophies – hot spot policing , problem-oriented policing, and community-based policing have loose parallels with new place-based predictive policing technologies like PredPol, Risk Terrain Modeling (RTM), and HunchLab. This chapter discusses these leading predictive policing technologies as illustrative examples of how police can choose between prioritizing additional police presence, targeting environmental vulnerabilities, and/or establishing a community problem-solving approach as a different means of achieving crime reduction….(More)”.

Essay by Thomas Carothers: “Adverse political developments in both established and newer democracies, especially the abdication by the United States of its traditional leadership role, have cast international democracy support into doubt. Yet international action on behalf of democracy globally remains necessary and possible. Moreover, some important elements of continuity remain, including overall Western spending on democracy assistance. Democracy support must adapt to its changed circumstances by doing more to take new geopolitical realities into account; effacing the boundary between support for democracy in new and in established democracies; strengthening the economic dimension of democracy assistance; and moving technological issues to the forefront…(More)”.

Essay by Bapu Vaitla, Stefaan Verhulst, Linus Bengtsson, Marta C. González, Rebecca Furst-Nichols & Emily Courey Pryor in Special Issue on Big Data of Nature Medicine: “Women and girls are legally and socially marginalized in many countries. As a result, policymakers neglect key gendered issues such as informal labor markets, domestic violence, and mental health¹. The scientific community can help push such topics onto policy agendas, but science itself is riven by inequality: women are underrepresented in academia, and gendered research is rarely a priority of funding agencies.

However, the critical importance of better gender data for societal well-being is clear. Mental health is a particularly striking example. Estimates from the Global Burden of Disease database suggest that depressive and anxiety disorders are the second leading cause of morbidity among females between 10 and 63 years of age². But little is known about the risk factors that contribute to mental illness among specific groups of women and girls, the challenges of seeking care for depression and anxiety, or the long-term consequences of undiagnosed and untreated illness. A lack of data similarly impedes policy action on domestic and intimate-partner violence, early marriage, and sexual harassment, among many other topics.

‘Big data’ can help fill that gap. The massive amounts of information passively generated by electronic devices represent a rich portrait of human life, capturing where people go, the decisions they make, and how they respond to changes in their socio-economic environment. For example, mobile-phone data allow better understanding of health-seeking behavior as well as the dynamics of infectious-disease transmission³. Social-media platforms generate the world’s largest database of thoughts and emotions—information that, if leveraged responsibly, can be used to infer gendered patterns of mental health⁴. Remote sensors, especially satellites, can be used in conjunction with traditional data sources to increase the spatial and temporal granularity of data on women’s economic activity and health status⁵.

But the risk of gendered algorithmic bias is a serious obstacle to the responsible use of big data. Data are not value free; they reproduce the conscious and unconscious attitudes held by researchers, programmers, and institutions. Consider, for example, the training datasets on which the interpretation of big data depends. Training datasets establish the association between two or more directly observed phenomena of interest—for example, the mental health of a platform user (typically collected through a diagnostic survey) and the semantic content of the user’s social-media posts. These associations are then used to develop algorithms that interpret big data streams. In the example here, the (directly unobserved) mental health of a large population of social-media users would be inferred from their observed posts….(More)”.

Marietje Schaake at the Financial Times: “Technology companies have governments over a barrel. Whether they are maximising traffic flow efficiency, matching pupils with their school preferences, trying to anticipate drought based on satellite and soil data, most governments heavily rely on critical infrastructure and artificial intelligence developed by the private sector. This growing dependence has profound implications for democracy.

An unprecedented information asymmetry is growing between companies and governments. We can see this in the long-running investigation into interference in the 2016 US presidential elections. Companies build voter registries, voting machines and tallying tools, while social media companies sell precisely targeted advertisements using information gleaned by linking data on friends, interests, location, shopping and search.

This has big privacy and competition implications, yet oversight is minimal. Governments, researchers and citizens risk being blindsided by the machine room that powers our lives and vital aspects of our democracies. Governments and companies have fundamentally different incentives on transparency and accountability.

While openness is the default and secrecy the exception for democratic governments, companies resist providing transparency about their algorithms and business models. Many of them actively prevent accountability, citing rules that protect trade secrets.

We must revisit these protections when they shield companies from oversight. There is a place for protecting proprietary information from commercial competitors, but the scope and context need to be clarified and balanced when they have an impact on democracy and the rule of law.

Regulators must act to ensure that those designing and running algorithmic processes do not abuse trade secret protections. Tech groups also use the EU’s General Data Protection Regulation to deny access to company information. Although the regulation was enacted to protect citizens against the mishandling of personal data, it is now being wielded cynically to deny scientists access to data sets for research. The European Data Protection Supervisor has intervened, but problems could recur. To mitigate concerns about the power of AI, provider companies routinely promise that the applications will be understandable, explainable, accountable, reliable, contestable, fair and — don’t forget — ethical.

Yet there is no way to test these subjective notions without access to the underlying data and information. Without clear benchmarks and information to match, proper scrutiny of the way vital data is processed and used will be impossible….(More)”.

Chris Stokel-Walker at Wired: “The video shows a faint glow in the distance, zig-zagging like a piece of paper caught in an underdraft, slowly meandering towards the horizon. Then there’s a bright flash and the trees in the foreground are thrown into shadow as Ukraine International Airlines flight PS752 hits the ground early on the morning of January 8, killing all 176 people on board.

At first, it seemed like an accident – engine failure was fingered as the cause – until the first video showing the plane seemingly on fire as it weaved to the ground surfaced. United States officials started to investigate, and a more complicated picture emerged. It appeared that the plane had been hit by a missile, corroborated by a second video that appears to show the moment the missile ploughs into the Boeing 737-800. While military and intelligence officials at governments around the world were conducting their inquiries in secret, a team of investigators were using open-source intelligence (OSINT) techniques to piece together the puzzle of flight PS752.

It’s not unusual nowadays for OSINT to lead the way in decoding key news events. When Sergei Skripal was poisoned, Bellingcat, an open-source intelligence website, tracked and identified his killers as they traipsed across London and Salisbury. They delved into military records to blow the cover of agents sent to kill. And in the days after the Ukraine Airlines plane crashed into the ground outside Tehran, Bellingcat and The New York Times have blown a hole in the supposition that the downing of the aircraft was an engine failure. The pressure – and the weight of public evidence – compelled Iranian officials to admit overnight on January 10 that the country had shot down the plane “in error”.

So how do they do it? “You can think of OSINT as a puzzle. To get the complete picture, you need to find the missing pieces and put everything together,” says Loránd Bodó, an OSINT analyst at Tech versus Terrorism, a campaign group. The team at Bellingcat and other open-source investigators pore over publicly available material. Thanks to our propensity to reach for our cameraphones at the sight of any newsworthy incident, video and photos are often available, posted to social media in the immediate aftermath of events. (The person who shot and uploaded the second video in this incident, of the missile appearing to hit the Boeing plane was a perfect example: they grabbed their phone after they heard “some sort of shot fired”.) “Open source investigations essentially involve the collection, preservation, verification, and analysis of evidence that is available in the public domain to build a picture of what happened,” says Yvonne McDermott Rees, a lecturer at Swansea University….(More)”.

Paper by Michael McGann, Tamas Wells & Emma Blomkamp: “Governments are increasingly establishing innovation labs to enhance public problem solving. Despite the speed at which these new units are being established, they have only recently begun to receive attention from public management scholars. This study assesses the extent to which labs are enhancing strategic policy capacity through pursuing more collaborative and citizen-centred approaches to policy design. Drawing on original case study research of five labs in Australia and New Zealand, it examines the structure of lab’s relationships to government partners, and the extent and nature of their activities in promoting citizen-participation in public problem solving….(More)”.

Essay by Richard Bellamy: “This essay explores how far democracy is compatible with lies and deception, and whether it encourages or discourages their use by politicians. Neo-Kantian arguments, such as Newey’s, that lies and deception undermine individual autonomy and the possibility for consent go too far, given that no democratic process can be regarded as a plausible mechanism for achieving collective consent to state policies. However, they can be regarded as incompatible with a more modest account of democracy as a system of public equality among political equals.

On this view, the problem with lies and deception derives from their being instruments of manipulation and domination. Both can be distinguished from ‘spin’, with a working democracy being capable of uncovering them and so incentivising politicians to be truthful. Nevertheless, while lies and deception will find you out, bullshit and post truth disregard and subvert truth respectively, and as such prove more pernicious as they admit of no standard whereby they might be challenged….(More)”.

Paper by Nikita Aggarwal et al: “Recent advances in machine learning (ML) and Big Data techniques have facilitated the development of more sophisticated, automated consumer credit scoring models — a trend referred to as ‘algorithmic credit scoring’ in recognition of the increasing reliance on computer (particularly ML) algorithms for credit scoring. This chapter, which forms part of the 2018 collection of short essays ‘Autonomous Systems and the Law’, examines the rise of algorithmic credit scoring, and considers its implications for the regulation of consumer creditworthiness assessment and consumer credit markets more broadly.

The chapter argues that algorithmic credit scoring, and the Big Data and ML technologies underlying it, offer both benefits and risks for consumer credit markets. On the one hand, it could increase allocative efficiency and distributional fairness in these markets, by widening access to, and lowering the cost of, credit, particularly for ‘thin-file’ and ‘no-file’ consumers. On the other hand, algorithmic credit scoring could undermine distributional fairness and efficiency, by perpetuating discrimination in lending against certain groups and by enabling the more effective exploitation of borrowers.

The chapter considers how consumer financial regulation should respond to these risks, focusing on the UK/EU regulatory framework. As a general matter, it argues that the broadly principles and conduct-based approach of UK consumer credit regulation provides the flexibility necessary for regulators and market participants to respond dynamically to these risks. However, this approach could be enhanced through the introduction of more robust product oversight and governance requirements for firms in relation to their use of ML systems and processes. Supervisory authorities could also themselves make greater use of ML and Big Data techniques in order to strengthen the supervision of consumer credit firms.

Finally, the chapter notes that cross-sectoral data protection regulation, recently updated in the EU under the GDPR, offers an important avenue to mitigate risks to consumers arising from the use of their personal data. However, further guidance is needed on the application and scope of this regime in the consumer financial context….(More)”.