Why Hundreds of Mathematicians Are Boycotting Predictive Policing

Curated on Posted on by Stefaan Verhulst

Courtney Linder at Popular Mechanics: “Several prominent academic mathematicians want to sever ties with police departments across the U.S., according to a letter submitted to Notices of the American Mathematical Society on June 15. The letter arrived weeks after widespread protests against police brutality, and has inspired over 1,500 other researchers to join the boycott.

These mathematicians are urging fellow researchers to stop all work related to predictive policing software, which broadly includes any data analytics tools that use historical data to help forecast future crime, potential offenders, and victims. The technology is supposed to use probability to help police departments tailor their neighborhood coverage so it puts officers in the right place at the right time….

a flow chart showing how predictive policing works

RAND

According to a 2013 research briefing from the RAND Corporation, a nonprofit think tank in Santa Monica, California, predictive policing is made up of a four-part cycle (shown above). In the first two steps, researchers collect and analyze data on crimes, incidents, and offenders to come up with predictions. From there, police intervene based on the predictions, usually taking the form of an increase in resources at certain sites at certain times. The fourth step is, ideally, reducing crime.

“Law enforcement agencies should assess the immediate effects of the intervention to ensure that there are no immediately visible problems,” the authors note. “Agencies should also track longer-term changes by examining collected data, performing additional analysis, and modifying operations as needed.”

In many cases, predictive policing software was meant to be a tool to augment police departments that are facing budget crises with less officers to cover a region. If cops can target certain geographical areas at certain times, then they can get ahead of the 911 calls and maybe even reduce the rate of crime.

But in practice, the accuracy of the technology has been contested—and it’s even been called racist….(More)”.

Monitoring Corruption: Can Top-down Monitoring Crowd-Out Grassroots Participation?

Curated on Posted on by Stefaan Verhulst

Paper by Robert M Gonzalez, Matthew Harvey and Foteini Tzachrista: “Empirical evidence on the effectiveness of grassroots monitoring is mixed. This paper proposes a previously unexplored mechanism that may explain this result. We argue that the presence of credible and effective top-down monitoring alternatives can undermine citizen participation in grassroots monitoring efforts. Building on Olken’s (2009) road-building field experiment in Indonesia; we find a large and robust effect of the participation interventions on missing expenditures in villages without an audit in place. However, this effect vanishes as soon as an audit is simultaneously implemented in the village. We find evidence of crowding-out effects: in government audit villages, individuals are less likely to attend, talk, and actively participate in accountability meetings. They are also significantly less likely to voice general problems, corruption-related problems, and to take serious actions to address these problems. Despite policies promoting joint implementation of top-down and bottom-up interventions, this paper shows that top-down monitoring can undermine rather than complement grassroots efforts….(More)”.

What Ever Happened to Digital Contact Tracing?

Curated on Posted on by Stefaan Verhulst

Chas Kissick, Elliot Setzer, and Jacob Schulz at Lawfare: “In May of this year, Prime Minister Boris Johnson pledged the United Kingdom would develop a “world beating” track and trace system by June 1 to stop the spread of the novel coronavirus. But on June 18, the government quietly abandoned its coronavirus contact-tracing app, a key piece of the “world beating” strategy, and instead promised to switch to a model designed by Apple and Google. The delayed app will not be ready until winter, and the U.K.’s Junior Health Minister told reporters that “it isn’t a priority for us at the moment.” When Johnson came under fire in Parliament for the abrupt U-turn, he replied: “I wonder whether the right honorable and learned Gentleman can name a single country in the world that has a functional contact tracing app—there isn’t one.”

Johnson’s rebuttal is perhaps a bit reductive, but he’s not that far off.

You probably remember the idea of contact-tracing apps: the technological intervention that seemed to have the potential to save lives while enabling a hamstrung economy to safely inch back open; it was a fixation of many public health and privacy advocates; it was the thing that was going to help us get out of this mess if we could manage the risks.

Yet nearly three months after Google and Apple announced with great fanfare their partnership to build a contact-tracing API, contact-tracing apps have made an unceremonious exit from the front pages of American newspapers. Countries, states and localities continue to try to develop effective digital tracing strategies. But as Jonathan Zittrain puts it, the “bigger picture momentum appears to have waned.”

What’s behind contact-tracing apps’ departure from the spotlight? For one, there’s the onset of a larger pandemic apathy in the U.S; many politicians and Americans seem to have thrown up their hands or put all their hopes in the speedy development of a vaccine. Yet, the apps haven’t even made much of a splash in countries that havetaken the pandemic more seriously. Anxieties about privacy persist. But technical shortcomings in the apps deserve the lion’s share of the blame. Countries have struggled to get bespoke apps developed by government technicians to work on Apple phones. The functionality of some Bluetooth-enabled models vary widely depending on small changes in phone positioning. And most countries have only convinced a small fraction of their populace to use national tracing apps.

Maybe it’s still possible that contact-tracing apps will make a miraculous comeback and approach the level of efficacy observers once anticipated.

But even if technical issues implausibly subside, the apps are operating in a world of unknowns.

Most centrally, researchers still have no real idea what level of adoption is required for the apps to actually serve their function. Some estimates suggest that 80 percent of current smartphone owners in a given area would need to use an app and follow its recommendations for digital contact tracing to be effective. But other researchers have noted that the apps could slow the rate of infections even if little more than 10 percent of a population used a tracing app. It will be an uphill battle even to hit the 10 percent mark in America, though. Survey data show that fewer than three in 10 Americans intend to use contact-tracing apps if they become available…(More).

Adolescent Mental Health: Using A Participatory Mapping Methodology to Identify Key Priorities for Data Collaboration

Curated on Posted on by Stefaan Verhulst

Blog by Alexandra Shaw, Andrew J. Zahuranec, Andrew Young, Stefaan G. Verhulst, Jennifer Requejo, Liliana Carvajal: “Adolescence is a unique stage of life. The brain undergoes rapid development; individuals face new experiences, relationships, and environments. These events can be exciting, but they can also be a source of instability and hardship. Half of all mental conditions manifest by early adolescence and between 10 and 20 percent of all children and adolescents report mental health conditions. Despite the increased risks and concerns for adolescents’ well-being, there remain significant gaps in availability of data at the country level for policymakers to address these issues.

In June, The GovLab partnered with colleagues at UNICEF’s Health and HIV team in the Division of Data, Analysis, Planning & Monitoring and the Data for Children Collaborative — a collaboration between UNICEF, the Scottish Government, and the University of Edinburgh — to design and apply a new methodology of participatory mapping and prioritization of key topics and issues associated with adolescent mental health that could be addressed through enhanced data collaboration….

The event led to three main takeaways. First, the topic mapping allows participants to deliberate and prioritize the various aspects of adolescent mental health in a more holistic manner. Unlike the “blind men and the elephant” parable, a topic map allows the participants to see and discuss  the interrelated parts of the topic, including those which they might be less familiar with.

Second, the workshops demonstrated the importance of tapping into distributed expertise via participatory processes. While the topic map provided a starting point, the inclusion of various experts allowed the findings of the document to be reviewed in a rapid, legitimate fashion. The diverse inputs helped ensure the individual aspects could be prioritized without a perspective being ignored.

Lastly, the approach showed the importance of data initiatives being driven and validated by those individuals representing the demand. By soliciting the input of those who would actually use the data, the methodology ensured data initiatives focused on the aspects thought to be most relevant and of greatest importance….(More)”

Governing in a pandemic: from parliamentary sovereignty to autocratic technocracy

Curated on Posted on by Stefaan Verhulst

Paper by Eric Windholz: “Emergencies require governments to govern differently. In Australia, the changes wrought by the COVID-19 pandemic have been profound. The role of lawmaker has been assumed by the executive exercising broad emergency powers. Parliaments, and the debate and scrutiny they provide, have been marginalised. The COVID-19 response also has seen the medical-scientific expert metamorphose from decision-making input into decision-maker. Extensive legislative and executive decision-making authority has been delegated to them – directly in some jurisdictions; indirectly in others. Severe restrictions on an individual’s freedom of movement, association and to earn a livelihood have been declared by them, or on their advice. Employing the analytical lens of regulatory legitimacy, this article examines and seeks to understand this shift from parliamentary sovereignty to autocratic technocracy. How has it occurred? Why has it occurred? What have been the consequences and risks of vesting significant legislative and executive power in the hands of medical-scientific experts; what might be its implications? The article concludes by distilling insights to inform the future design and deployment of public health emergency powers….(More)”.

Addressing trust in public sector data use

Curated on Posted on by Stefaan Verhulst

Centre for Data Ethics and Innovation: “Data sharing is fundamental to effective government and the running of public services. But it is not an end in itself. Data needs to be shared to drive improvements in service delivery and benefit citizens. For this to happen sustainably and effectively, public trust in the way data is shared and used is vital. Without such trust, the government and wider public sector risks losing society’s consent, setting back innovation as well as the smooth running of public services. Maximising the benefits of data driven technology therefore requires a solid foundation of societal approval.

AI and data driven technology offer extraordinary potential to improve decision making and service delivery in the public sector – from improved diagnostics to more efficient infrastructure and personalised public services. This makes effective use of data more important than it has ever been, and requires a step-change in the way data is shared and used. Yet sharing more data also poses risks and challenges to current governance arrangements.

The only way to build trust sustainably is to operate in a trustworthy way. Without adequate safeguards the collection and use of personal data risks changing power relationships between the citizen and the state. Insights derived by big data and the matching of different data sets can also undermine individual privacy or personal autonomy. Trade-offs are required which reflect democratic values, wider public acceptability and a shared vision of a data driven society. CDEI has a key role to play in exploring this challenge and setting out how it can be addressed. This report identifies barriers to data sharing, but focuses on building and sustaining the public trust which is vital if society is to maximise the benefits of data driven technology.

There are many areas where the sharing of anonymised and identifiable personal data by the public sector already improves services, prevents harm, and benefits the public. Over the last 20 years, different governments have adopted various measures to increase data sharing, including creating new legal sharing gateways. However, despite efforts to increase the amount of data sharing across the government, and significant successes in areas like open data, data sharing continues to be challenging and resource-intensive. This report identifies a range of technical, legal and cultural barriers that can inhibit data sharing.

Barriers to data sharing in the public sector

Technical barriers include limited adoption of common data standards and inconsistent security requirements across the public sector. Such inconsistency can prevent data sharing, or increase the cost and time for organisations to finalise data sharing agreements.

While there are often pre-existing legal gateways for data sharing, underpinned by data protection legislation, there is still a large amount of legal confusion on the part of public sector bodies wishing to share data which can cause them to start from scratch when determining legality and commit significant resources to legal advice. It is not unusual for the development of data sharing agreements to delay the projects for which the data is intended. While the legal scrutiny of data sharing arrangements is an important part of governance, improving the efficiency of these processes – without sacrificing their rigour – would allow data to be shared more quickly and at less expense.

Even when legal, the permissive nature of many legal gateways means significant cultural and organisational barriers to data sharing remain. Individual departments and agencies decide whether or not to share the data they hold and may be overly risk averse. Data sharing may not be prioritised by a department if it would require them to bear costs to deliver benefits that accrue elsewhere (i.e. to those gaining access to the data). Departments sharing data may need to invest significant resources to do so, as well as considering potential reputational or legal risks. This may hold up progress towards finding common agreement on data sharing. When there is an absence of incentives, even relatively small obstacles may mean data sharing is not deemed worthwhile by those who hold the data – despite the fact that other parts of the public sector might benefit significantly….(More)”.

German coronavirus experiment enlists help of concertgoers

Curated on Posted on by Stefaan Verhulst

Philip Oltermann at the Guardian: “German scientists are planning to equip 4,000 pop music fans with tracking gadgets and bottles of fluorescent disinfectant to get a clearer picture of how Covid-19 could be prevented from spreading at large indoor concerts.

As cultural mass gatherings across the world remain on hold for the foreseeable future, researchers in eastern Germany are recruiting volunteers for a “coronavirus experiment” with the singer-songwriter Tim Bendzko, to be held at an indoor stadium in the city of Leipzig on 22 August.

Participants, aged between 18 and 50, will wear matchstick-sized “contact tracer” devices on chains around their necks that transmit a signal at five-second intervals and collect data on each person’s movements and proximity to other members of the audience.

Inside the venue, they will also be asked to disinfect their hands with a fluorescent hand-sanitiser – designed to not just add a layer of protection but allow scientists to scour the venue with UV lights after the concerts to identify surfaces where a transmission of the virus through smear infection is most likely to take place.

Vapours from a fog machine will help visualise the possible spread of coronavirus via aerosols, which the scientists will try to predict via computer-generated models in advance of the event.

The €990,000 cost of the Restart-19 project will be shouldered between the federal states of Saxony and Saxony-Anhalt. The project’s organisers say the aim is to “identify a framework” for how larger cultural and sports events could be held “without posing a danger for the population” after 30 September….

To stop the Leipzig experiment from becoming the source of a new outbreak, signed-up volunteers will be sent a DIY test kit and have a swab at a doctor’s practice or laboratory 48 hours before the concert starts. Those who cannot show proof of a negative test at the door will be denied entry….(More)”.

The Dark Side of Public Innovation

Curated on Posted on by Stefaan Verhulst

Paper by Albert Meijer & Marcel Thaens: “The positive features of innovation are well known but the dark side of public innovation has received less attention. To fill this gap, this article develops a theoretical understanding of the dark side of public innovation. We explore a diversity of perverse effects on the basis of a literature review and an expert consultation. We indicate that these perverse effects can be categorized on two dimensions: low public value and low public control. We confront this exploratory analysis with the literature and conclude that the perverse effects are not coincidental but emerge from key properties of innovation processes such as creating niches for innovation and accepting uncertainty about public value outcomes. To limit perverse effects, we call for the dynamic assessment of public innovation. The challenge for innovators is to acknowledge the dark side and take measures to prevent perverse effects without killing the innovativeness of organizations…(More)“.

Coronavirus: how the pandemic has exposed AI’s limitations

Curated on Posted on by Stefaan Verhulst

Kathy Peach at The Conversation: “It should have been artificial intelligence’s moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact – hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management – the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

The unlikely hero

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations – something that highly-specialised AI systems just can’t do. At least yet….

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to “follow the herd” by failing to hear diverse or minority views, or challenge assumptions – all of which are criticisms that have been levelled at the British government’s scientific advisory committees throughout the pandemic…(More)”.

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

Curated on Posted on by Stefaan Verhulst

Paper by Christopher Loynes, Jamal Ouenniche & Johannes De Smedt: “This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster’s geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an on-the-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further…(More)”.