Four Principles for Integrating AI & Good Governance

Curated on Posted on by Stefaan Verhulst

Oxford Commission on AI and Good Governance: “Many governments, public agencies and institutions already employ AI in providing public services, the distribution of resources and the delivery of governance goods. In the public sector, AI-enabled governance may afford new efficiencies that have the potential to transform a wide array of public service tasks.
But short-sighted design and use of AI can create new problems, entrench existing inequalities, and calcify and ultimately undermine government organizations.

Frameworks for the procurement and implementation of AI in public service have widely remained undeveloped. Frequently, existing regulations and national laws are no longer fit for purpose to ensure
good behaviour (of either AI or private suppliers) and are ill-equipped to provide guidance on the democratic use of AI.
As technology evolves rapidly, we need rules to guide the use of AI in ways that safeguard democratic values. Under what conditions can AI be put into service for good governance?

We offer a framework for integrating AI with good governance. We believe that with dedicated attention and evidence-based policy research, it should be possible to overcome the combined technical and organizational challenges of successfully integrating AI with good governance. Doing so requires working towards:


Inclusive Design: issues around discrimination and bias of AI in relation to inadequate data sets, exclusion of minorities and under-represented
groups, and the lack of diversity in design.
Informed Procurement: issues around the acquisition and development in relation to due diligence, design and usability specifications and the assessment of risks and benefits.
Purposeful Implementation: issues around the use of AI in relation to interoperability, training needs for public servants, and integration with decision-making processes.
Persistent Accountability: issues around the accountability and transparency of AI in relation to ‘black box’ algorithms, the interpretability and explainability of systems, monitoring and auditing…(More)”

Trade-offs and considerations for the future: Innovation and the COVID-19 response

Curated on Posted on by Stefaan Verhulst

Essay by Benjamin Kumpf: “…Here are some of the relevant trade-offs I identified. 

Rigour vs. Speed

How to best balance high-quality rigorous research and the need to gain actionable insights rapidly?  

Responding to a pandemic requires working at pace, while investing in ongoing research and the cross-fertilization of disciplines. In our response, we witness the importance of strong networks with academia and DFID’s focus on high-quality research. In parallel, we invest in supporting partners with rapid data collection through methods such as phone surveys, field visits, onsite interviews where possible as well as big data analysis and more. For example, through the International Growth Centre, DFID has supported a Sierra Leone COVID-19 dashboard, providing real time data on current economic conditions and trends from phone–based surveys from 195 towns and villages across Sierra Leone. ….

Breadth vs. depth

How to best balance providing services to large proportions of populations in need, while addressing challenges of specific communities?  

We are seeing emerging evidence that the virus and measures to prevent spread are disproportionately impacting marginalized communities and minorities. For example, in indigenous people are disproportionally affected by the virus in Brazil, Dalits are among the worst affected in India. In development and humanitarian contexts, it is paramount to guide innovation efforts with explicit values, including on the trade-off between scale and addressing last-mile challenges to leaveno–one behind. For example, to facilitate behaviour-change and embed insights from behavioural science and adaptive practices, DFID is supporting the Hygiene Hub, hosted at the London School for Hygiene and Tropical Medicine. The Hub provides free-of-charge advisory services to governments and non-governmental organizations working on COVID-19 related challenges in low and medium-income countries, balancing the need to reach large audiences and to design bespoke interventions for specific communities.  

Exploration vs. adaptation

How to best diversify innovation efforts and investments betweensearching for local solution and adapting proven approaches? …

Adaptive vs. locally-led

How to best learn and adapt, while providing ownership to local players? …

Single-point solutions vs. systems-practices

How to advance specific tech and non-tech innovations that address urgent needs, while further improving existing systems? …

Supporting domestic innovators vs. strengthening local solutions and ecosystems

We need explicit conversations to ensure better transparency about this trade-off in innovation investments generally.…(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)”

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)”.

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)”.

Ethical and Legal Aspects of Open Data Affecting Farmers

Curated on Posted on by Stefaan Verhulst

Report by Foteini Zampati et al: “Open Data offers a great potential for innovations from which the agricultural sector can benefit decisively due to a wide range of possibilities for further use. However, there are many inter-linked issues in the whole data value chain that affect the ability of farmers, especially the poorest and most vulnerable, to access, use and harness the benefits of data and data-driven technologies.

There are technical challenges and ethical and legal challenges as well. Of all these challenges, the ethical and legal aspects related to accessing and using data by the farmers and sharing farmers’ data have been less explored.

We aimed to identify gaps and highlight the often-complex legal issues related to open data in the areas of law (e.g. data ownership, data rights) policies, codes of conduct, data protection, intellectual property rights, licensing contracts and personal privacy.

This report is an output of the Kampala INSPIRE Hackathon 2020. The Hackathon addressed key topics identified by the IST-Africa 2020 conference, such as: Agriculture, environmental sustainability, collaborative open innovation, and ICT-enabled entrepreneurship.

The goal of the event was to continue to build on the efforts of the 2019 Nairobi INSPIRE Hackathon, further strengthening relationships between various EU projects and African communities. It was a successful event, with more than 200 participants representing 26 African countries. The INSPIRE Hackathons are not a competition, rather the main focus is building relationships, making rapid developments, and collecting ideas for future research and innovation….(More)”.

Blind-sided by privacy? Digital contact tracing, the Apple/Google API and big tech’s newfound role as global health policy makers

Curated on Posted on by Stefaan Verhulst

Paper by Tamar Sharon: “Since the outbreak of COVID-19, governments have turned their attention to digital contact tracing. In many countries, public debate has focused on the risks this technology poses to privacy, with advocates and experts sounding alarm bells about surveillance and mission creep reminiscent of the post 9/11 era. Yet, when Apple and Google launched their contact tracing API in April 2020, some of the world’s leading privacy experts applauded this initiative for its privacy-preserving technical specifications. In an interesting twist, the tech giants came to be portrayed as greater champions of privacy than some democratic governments.

This article proposes to view the Apple/Google API in terms of a broader phenomenon whereby tech corporations are encroaching into ever new spheres of social life. From this perspective, the (legitimate) advantage these actors have accrued in the sphere of the production of digital goods provides them with (illegitimate) access to the spheres of health and medicine, and more worrisome, to the sphere of politics. These sphere transgressions raise numerous risks that are not captured by the focus on privacy harms. Namely, a crowding out of essential spherical expertise, new dependencies on corporate actors for the delivery of essential, public goods, the shaping of (global) public policy by non-representative, private actors and ultimately, the accumulation of decision-making power across multiple spheres. While privacy is certainly an important value, its centrality in the debate on digital contact tracing may blind us to these broader societal harms and unwittingly pave the way for ever more sphere transgressions….(More)”.

The Data Delusion: Protecting Individual Data is Not Enough When the Harm is Collective

Curated on Posted on by Stefaan Verhulst

Essay by Martin Tisné: “On March 17, 2018, questions about data privacy exploded with the scandal of the previously unknown consulting company Cambridge Analytica. Lawmakers are still grappling with updating laws to counter the harms of big data and AI. In the Spring of 2020, the Covid-19 pandemic brought questions about sufficient legal protections back to the public debate, with urgent warnings about the privacy implications of contact tracing apps. But the surveillance consequences of the pandemic’s aftermath are much bigger than any app: transport, education, health
systems and offices are being turned into vast surveillance networks. If we only consider individual trade-offs between privacy sacrifices and alleged health benefits, we will miss the point. The collective nature of big data means people are more impacted by other people’s data than by data about them. Like climate change, the threat is societal and personal.

In the era of big data and AI, people can suffer because of how the sum of individual data is analysed and sorted into groups by algorithms. Novel forms of collective data-driven harms are appearing as a result: online housing, job and credit ads discriminating on the basis of race and gender, women disqualified from jobs on the basis of gender and foreign actors targeting light-right groups, pulling them to the far-right.2 Our public debate, governments, and laws are ill-equipped to deal with these collective, as opposed to individual, harms….(More)”.

Hammer or nudge? New brief on international policy options for COVID-19

Curated on Posted on by Stefaan Verhulst

Paper by Luc Soete: “…But over time the scientific comments given on TV and radio in my two home countries, the Netherlands and Belgium, as well as neighbouring Germany and France, became dominated by each country’s own, national virology and epidemiological experts explaining how their country’s approach to ‘flattening the curve’ and bringing down the reproduction rate was best, it became clear, even to a non-expert in the field like myself, that many of the science-based policies used to contain COVID-19 were first and foremost based on ‘hypotheses’. With the exception of Germany, not really on facts. And as Anthony Fauci, Director of the US National Institute of Allergy and Infectious Disease, probably the world’s most respected virologist once put it: “Data is real. The model is hypothesis.”

So at the risk of being an ultracrepidarian – an old word which has suddenly risen in popularity – it seemed appropriate to have a closer, more critical look at the science-based policy advice during this COVID-19 pandemic. For virologists and epidemiologists, the logical approach to a new, unknown but highly infectious virus such as SARS-CoV-2, spreading globally at pandemic speed, is ‘the hammer’: the tool to crush down quickly and radically through extreme measures (social distancing, confinement, lockdown, travel restrictions) the spread of the virus and get the transmission rate’s value as far as possible below. The stricter the confinement measures, the better.

For a social scientist and social science-based policy adviser, a hammer represents anything but a useful tool to approach society or the economy with. Her or his preference will rather go to measures, such as ‘nudges’ which alter people’s behaviour in a predictable way without coercion. Actually, the first COVID-19 measure was based on a typical ‘nudge’: improving hand hygiene among healthcare workers which was now enlarged to the whole population. ‘Nudging’ in the face of a new virus such as SARS-CoV-2 will consist of making sure incremental policy measures build up to a societal behavioural change, starting from hand hygiene, social distancing, to confinement and various forms of lockdown. It will be crucial to measure the additional, marginal impact of each measure in its contribution to the overall reduction in the transmission of the virus. Introducing all measures at once, as in the case of the ‘hammer’ strategy, subsequently provides little useful information on the effectiveness of each measure ( on the contrary, in fact). In a period of deconfinement, one now has little information on which measures are likely to be the most effective. From a nudge perspective, achieving a change in social behaviour with respect to physical distancing: the so-called one-and-a-half metre society, will be an essential variable and measuring its impact on the spreading of the virus crucial. One of the reasons is that full adoption of such physical distancing automatically and without the need of coercion, will prevent the occurrence of large or smaller social gatherings without authorities having to specify the rules. This is implicit in the principle of nudging: it will be the providers, the entrepreneurs of personal service sectors who will have to come up with organisational innovations enabling physical distancing in the safe delivery of such services.

Most noteworthy, however, is the purely national setting within which most virology and epidemiological science-based policy advice is currently framed. This contrasts sharply with the actual scientific research in the field which is today purely global, based on shared data and open access. For years now, epidemiological studies have taken individual countries as ‘containers’ for data collection and data analysis. It is also the national setting that provides the framework for estimating the capacity of medical facilities, especially the total number of available intensive care units needed to handle COVID-19 patients in each country. In the case of Europe and as a result, it has led to the reintroduction of internal borders which had ‘disappeared’ 25 years ago for fear of cross-border contamination. Doing so, COVID-19 has undermined the notion of European values. This policy brief is my attempt to clarify the situation….(More)”.