Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

Curated on Posted on by Stefaan Verhulst

Paper by Emily Aiken et al: “The COVID-19 pandemic has devastated many low- and middle-income countries (LMICs), causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have mobilized targeted social assistance programs. Targeting is a central challenge in the administration of these programs: given available data, how does one rapidly identify the individuals and families with the greatest need? This challenge is particularly acute in the large number of LMICs that lack recent and comprehensive data on household income and wealth.

Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our approach uses traditional survey-based measures of consumption and wealth to train machine learning algorithms that recognize patterns of poverty in non-traditional data; the trained algorithms are then used to prioritize aid to the poorest regions and mobile subscribers. We evaluate this approach by studying Novissi, Togo’s flagship emergency cash transfer program, which used these algorithms to determine eligibility for a rural assistance program that disbursed millions of dollars in COVID-19 relief aid. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to the geographic targeting options considered by the Government of Togo at the time, the machine learning approach reduces errors of exclusion by 4-21%. Relative to methods that require a comprehensive social registry (a hypothetical exercise; no such registry exists in Togo), the machine learning approach increases exclusion errors by 9-35%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date….(More)”.

Governing smart cities: policy benchmarks for ethical and responsible smart city development

Curated on Posted on by Stefaan Verhulst

Report by the World Economic Forum: “… provides a benchmark for cities looking to establish policies for ethical and responsible governance of their smart city programmes. It explores current practices relating to five foundational policies: ICT accessibility, privacy impact assessment, cyber accountability, digital infrastructure and open data. The findings are based on surveys and interviews with policy experts and city government officials from the Alliance’s 36 “Pioneer Cities”. The data and insights presented in the report come from an assessment of detailed policy elements rather than the high-level indicators often used in maturity frameworks….(More)”.

When Machines Can Be Judge, Jury, and Executioner

Curated on Posted on by Stefaan Verhulst

Book by Katherine B Forrest on “Justice in the Age of Artificial Intelligence”: “This book explores justice in the age of artificial intelligence. It argues that current AI tools used in connection with liberty decisions are based on utilitarian frameworks of justice and inconsistent with individual fairness reflected in the US Constitution and Declaration of Independence. It uses AI risk assessment tools and lethal autonomous weapons as examples of how AI influences liberty decisions. The algorithmic design of AI risk assessment tools can and does embed human biases. Designers and users of these AI tools have allowed some degree of compromise to exist between accuracy and individual fairness.

Written by a former federal judge who lectures widely and frequently on AI and the justice system, this book is the first comprehensive presentation of the theoretical framework of AI tools in the criminal justice system and lethal autonomous weapons utilized in decision-making. The book then provides a comprehensive explanation as to why, tracing the evolution of the debate regarding racial and other biases embedded in such tools. No other book delves as comprehensively into the theory and practice of AI risk assessment tools….(More)”.

Analytical modelling and UK Government policy

Curated on Posted on by Stefaan Verhulst

Paper by Marie Oldfield & Ella Haig:  “In the last decade, the UK Government has attempted to implement improved processes and procedures in modelling and analysis in response to the Laidlaw report of 2012 and the Macpherson review of 2013. The Laidlaw report was commissioned after failings during the Intercity West Coast Rail (ICWC) Franchise procurement exercise by the Department for Transport (DfT) that led to a legal challenge of the analytical models used within the exercise. The Macpherson review looked into the quality assurance of Government analytical models in the context of the experience with the Intercity West Coast franchise competition. This paper examines what progress has been made in the 8 years since the Laidlaw report in model building and best practise in government and proposes several recommendations for ways forward. This paper also discusses the Lords Science and Technology Committees of June 2020 that analysed the failings in the modelling of COVID. Despite going on to influence policy, many of the same issues raised within the Laidlaw and Macpherson Reports were also present in the Lords Science and Technology Committee enquiry. We examine the technical and organisational challenges to progress in this area and make recommendations for a way forward….(More)”.

Government algorithms are out of control and ruin lives

Curated on Posted on by Stefaan Verhulst


Nani Jansen Reventlow at Open Democracy: “Government services are increasingly being automated and technology is relied on more and more to make crucial decisions about our lives and livelihoods. This includes decisions about what type of support we can access in times of need: welfare, benefits, and other government services.

Technology has the potential to not only reproduce but amplify structural inequalities in our societies. If you combine this drive for automation with a broader context of criminalising poverty and systemic racism, this can have disastrous effects.

A recent example is the ‘child benefits scandal’ that brought down the Dutch government at the start of 2021. In the Netherlands, working parents are eligible for a government contribution toward the costs of daycare. This can run up to 90% of the actual costs for those with a low income. While contributions are often directly paid to childcare providers, parents are responsible for them. This means that, if the tax authorities determine that any allowance was wrongfully paid out, parents are liable for repaying them.

To detect cases of fraud, the Dutch tax authorities used a system that was outright discriminatory. An investigation by the Dutch Data Protection Authority last year showed that parents were singled out for special scrutiny because of their ethnic origin or dual nationality.  “The whole system was organised in a discriminatory manner and was also used as such,” it stated.

The fallout of these ‘fraud detection’ efforts was enormous. It is currently estimated that 46,000 parents were wrongly accused of having fraudulently claimed child care allowances. Families were forced to repay tens of thousands of euros, leading to financial hardship, loss of livelihood, homes, and in one case, even loss of life – one parent died by suicide. While we can still hope that justice for these families won’t be denied, it will certainly be delayed: this weekend, it became clear that it could take up to ten years to handle all claims. An unacceptable timeline, given how precarious the situation will be for many of those affected….(More)”.

Transparency’s AI Problem

Curated on Posted on by Stefaan Verhulst

Paper by Hannah Bloch-Wehba: “A consensus seems to be emerging that algorithmic governance is too opaque and ought to be made more accountable and transparent. But algorithmic governance underscores the limited capacity of transparency law—the Freedom of Information Act and its state equivalents—to promote accountability. Drawing on the critical literature on “open government,” this Essay shows that algorithmic governance reflects and amplifies systemic weaknesses in the transparency regime, including privatization, secrecy, private sector cooptation, and reactive disclosure. These deficiencies highlight the urgent need to reorient transparency and accountability law toward meaningful public engagement in ongoing oversight. This shift requires rethinking FOIA’s core commitment to public disclosure of agency records, exploring instead alternative ways to empower the public and to shed light on decisionmaking. The Essay argues that new approaches to transparency and accountability for algorithmic governance should be independent of private vendors, and ought to adequately represent the interests of affected individuals and communities. These considerations, of vital importance for the oversight of automated systems, also hold broader lessons for efforts to recraft open government obligations in the public interest….(More)”

Facial Recognition Technology: Federal Law Enforcement Agencies Should Better Assess Privacy and Other Risks

Curated on Posted on by Stefaan Verhulst

Report by the U.S. Government Accountability Office: “GAO surveyed 42 federal agencies that employ law enforcement officers about their use of facial recognition technology. Twenty reported owning systems with facial recognition technology or using systems owned by other entities, such as other federal, state, local, and non-government entities (see figure).

Ownership and Use of Facial Recognition Technology Reported by Federal Agencies that Employ Law Enforcement Officers

HLP_5 - 103705

Note: For more details, see figure 2 in GAO-21-518.

Agencies reported using the technology to support several activities (e.g., criminal investigations) and in response to COVID-19 (e.g., verify an individual’s identity remotely). Six agencies reported using the technology on images of the unrest, riots, or protests following the death of George Floyd in May 2020. Three agencies reported using it on images of the events at the U.S. Capitol on January 6, 2021. Agencies said the searches used images of suspected criminal activity.

All fourteen agencies that reported using the technology to support criminal investigations also reported using systems owned by non-federal entities. However, only one has awareness of what non-federal systems are used by employees. By having a mechanism to track what non-federal systems are used by employees and assessing related risks (e.g., privacy and accuracy-related risks), agencies can better mitigate risks to themselves and the public….GAO is making two recommendations to each of 13 federal agencies to implement a mechanism to track what non-federal systems are used by employees, and assess the risks of using these systems. Twelve agencies concurred with both recommendations. U.S. Postal Service concurred with one and partially concurred with the other. GAO continues to believe the recommendation is valid, as described in the report….(More)”.

Ethics and governance of artificial intelligence for health

Curated on Posted on by Stefaan Verhulst

The WHO guidance…”on Ethics & Governance of Artificial Intelligence for Health is the product of eighteen months of deliberation amongst leading experts in ethics, digital technology, law, human rights, as well as experts from Ministries of Health.  While new technologies that use artificial intelligence hold great promise to improve diagnosis, treatment, health research and drug development and to support governments carrying out public health functions, including surveillance and outbreak response, such technologies, according to the report, must put ethics and human rights at the heart of its design, deployment, and use.

The report identifies the ethical challenges and risks with the use of artificial intelligence of health, six consensus principles to ensure AI works to the public benefit of all countries. It also contains a set of recommendations that can ensure the governance of artificial intelligence for health maximizes the promise of the technology and holds all stakeholders – in the public and private sector – accountable and responsive to the healthcare workers who will rely on these technologies and the communities and individuals whose health will be affected by its use…(More)”

National strategies on Artificial Intelligence: A European perspective

Curated on Posted on by Stefaan Verhulst

Report by European Commission’s Joint Research Centre (JRC) and the OECD’s Science Technology and Innovation Directorate: “Artificial intelligence (AI) is transforming the world in many aspects. It is essential for Europe to consider how to make the most of the opportunities from this transformation and to address its challenges. In 2018 the European Commission adopted the Coordinated Plan on Artificial Intelligence that was developed together with the Member States to maximise the impact of investments at European Union (EU) and national levels, and to encourage synergies and cooperation across the EU.

One of the key actions towards these aims was an encouragement for the Member States to develop their national AI strategies.The review of national strategies is one of the tasks of AI Watch launched by the European Commission to support the implementation of the Coordinated Plan on Artificial Intelligence.

Building on the 2020 AI Watch review of national strategies, this report presents an updated review of national AI strategies from the EU Member States, Norway and Switzerland. By June 2021, 20 Member States and Norway had published national AI strategies, while 7 Member States were in the final drafting phase. Since the 2020 release of the AI Watch report, additional Member States – i.e. Bulgaria, Hungary, Poland, Slovenia, and Spain – published strategies, while Cyprus, Finland and Germany have revised the initial strategies.

This report provides an overview of national AI policies according to the following policy areas: Human capital, From the lab to the market, Networking, Regulation, and Infrastructure. These policy areas are consistent with the actions proposed in the Coordinated Plan on Artificial Intelligence and with the policy recommendations to governments contained in the OECD Recommendation on AI. The report also includes a section on AI policies to address societal challenges of the COVID-19 pandemic and climate change….(More)”.

To regulate AI, try playing in a sandbox

Curated on Posted on by Stefaan Verhulst

Article by Dan McCarthy: “For an increasing number of regulators, researchers, and tech developers, the word “sandbox” is just as likely to evoke rulemaking and compliance as it is to conjure images of children digging, playing, and building. Which is kinda the point.

That’s thanks to the rise of regulatory sandboxes, which allow organizations to develop and test new technologies in a low-stakes, monitored environment before rolling them out to the general public. 

Supporters, from both the regulatory and the business sides, say sandboxes can strike the right balance of reining in potentially harmful technologies without kneecapping technological progress. They can also help regulators build technological competency and clarify how they’ll enforce laws that apply to tech. And while regulatory sandboxes originated in financial services, there’s growing interest in using them to police artificial intelligence—an urgent task as AI is expanding its reach while remaining largely unregulated. 

Even for all of its promise, experts told us, the approach should be viewed not as a silver bullet for AI regulation, but instead as a potential step in the right direction. 

Rashida Richardson, an AI researcher and visiting scholar at Rutgers Law School, is generally critical of AI regulatory sandboxes, but still said “it’s worth testing out ideas like this, because there is not going to be any universal model to AI regulation, and to figure out the right configuration of policy, you need to see theoretical ideas in practice.” 

But waiting for the theoretical to become concrete will take time. For example, in April, the European Union proposed AI regulation that would establish regulatory sandboxes to help the EU achieve its aim of responsible AI innovation, mentioning the word “sandbox” 38 times, compared to related terms like “impact assessment” (13 mentions) and “audit” (four). But it will likely take years for the EU’s proposal to become law. 

In the US, some well-known AI experts are working on an AI sandbox prototype, but regulators are not yet in the picture. However, the world’s first and (so far) only AI-specific regulatory sandbox did roll out in Norway this March, as a way to help companies comply with AI-specific provisions of the EU’s General Data Protection Regulation (GDPR). The project provides an early window into how the approach can work in practice.

“It’s a place for mutual learning—if you can learn earlier in the [product development] process, that is not only good for your compliance risk, but it’s really great for building a great product,” according to Erlend Andreas Gjære, CEO and cofounder of Secure Practice, an information security (“infosec”) startup that is one of four participants in Norway’s new AI regulatory sandbox….(More)”