Hurricane Ian Destroyed Their Homes. Algorithms Sent Them Money

Curated on Posted on by Stefaan Verhulst

Article by Chris Stokel-Walker: “The algorithms that power Skai’s damage assessments are trained by manually labeling satellite images of a couple of hundred buildings in a disaster-struck area that are known to have been damaged. The software can then, at speed, detect damaged buildings across the whole affected area. A research paper on the underlying technology presented at a 2020 academic workshop on AI for disaster response claimed the auto-generated damage assessments match those of human experts with between 85 and 98 percent accuracy.

In Florida this month, GiveDirectly sent its push notification offering $700 to any user of the Providers app with a registered address in neighborhoods of Collier, Charlotte, and Lee Counties where Google’s AI system deemed more than 50 percent of buildings had been damaged. So far, 900 people have taken up the offer, and half of those have been paid. If every recipient takes up GiveDirectly’s offer, the organization will pay out $2.4 million in direct financial aid.

Some may be skeptical of automated disaster response. But in the chaos after an event like a hurricane making landfall, the conventional, human response can be far from perfect. Diaz points to an analysis GiveDirectly conducted looking at their work after Hurricane Harvey, which hit Texas and Louisiana in 2017, before the project with Google. Two out of the three areas that were most damaged and economically depressed were initially overlooked. A data-driven approach is “much better than what we’ll have from boots on the ground and word of mouth,” Diaz says.

GiveDirectly and Google’s hands-off, algorithm-led approach to aid distribution has been welcomed by some disaster assistance experts—with caveats. Reem Talhouk, a research fellow at Northumbria University’s School of Design and Centre for International Development in the UK, says that the system appears to offer a more efficient way of delivering aid. And it protects the dignity of recipients, who don’t have to queue up for handouts in public…(More)”.

Nowcasting daily population displacement in Ukraine through social media advertising data

Curated on Posted on by Stefaan Verhulst

Pre-Publication Paper by Douglas R. Leasure et al: “In times of crisis, real-time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022 forcibly displaced millions of people from their homes including nearly 6m refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged near real-time social media marketing data to estimate sub-national population sizes every day disaggregated by age and sex. Our metric of internal displacement estimated that 5.3m people had been internally displaced away from their baseline administrative region by March 14. Results revealed four distinct displacement patterns: large scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While this innovative approach provided one of the only quantitative estimates of internal displacement in virtual real-time, we conclude by acknowledging risks and challenges for the future…(More)”.

Mapping community resources for disaster preparedness: humanitarian data capability and automated futures

Curated on Posted on by Stefaan Verhulst

Report by Anthony McCosker et al: “This report details the rationale, background research and design for a platform to help local communities map resources for disaster preparedness. It sets out a first step in improving community data capability through resource mapping to enhance humanitarian action before disaster events occur.The project seeks to enable local community disaster preparedness and thus build community resilience by improving the quality of data about community strengths, resources and assets.

In this report, the authors define a gap in existing humanitarian mapping approaches and the uses of open, public and social media data in humanitarian contexts. The report surveys current knowledge and present a selection of case studies delivering data and humanitarian mapping in local communities.

Drawing on this knowledge and practice review and stakeholder workshops throughout 2021, the authors also define a method and toolkit for the effective use of community assets data…(More)”

Localising AI for crisis response

Curated on Posted on by Stefaan Verhulst

Report by Aleks Berditchevskaia and Kathy Peach, Isabel Stewart: “Putting power back in the hands of frontline humanitarians and local communities.

This report documents the results of a year-long project to design and evaluate new proof-of-concept Collective Crisis Intelligence tools. These are tools that combine data from crisis-affected communities with the processing power of AI to improve humanitarian action.

The two collective crisis intelligence tool prototypes developed were:

  • NFRI-Predict: a tool that predicts which non-food aid items (NFRI) are most needed by different types of households in different regions of Nepal after a crisis.
  • Report and Respond: a French language SMS-based tool that allows Red Cross volunteers in Cameroon to check the accuracy of COVID-19 rumours or misinformation they hear from the community while they’re in the field, and receive real-time guidance on appropriate responses.

Both tools were developed using Nesta’s Participatory AI methods, which aimed to address some of the risks associated with humanitarian AI by involving local communities in the design, development and evaluation of the new tools.

The project was a partnership between Nesta’s Centre for Collective Intelligence Design (CCID) and Data Analytics Practice (DAP), the Nepal Red Cross and Cameroon Red Cross, IFRC Solferino Academy, and Open Lab Newcastle University, and it was funded by the UK Humanitarian Innovation Hub.

We found that collective crisis intelligence:

  • has the potential to make local humanitarian action more timely and appropriate to local needs.
  • can transform locally-generated data to drive new forms of (anticipatory) action.

We found that participatory AI:

  • can overcome several critiques and limitations of AI – as well as helping to improve model performance.
  • helps to surface tensions between the assumptions and standards set by AI gatekeepers versus the pragmatic reality of implementation.
  • creates opportunities for building and sharing new capabilities among frontline staff and data scientists.

We also validated that collective crisis intelligence and participatory AI can help increase trust in AI tools, but more research is needed to untangle the factors that were responsible…(More)”.

Using Wikipedia for conflict forecasting

Curated on Posted on by Stefaan Verhulst

Article by Christian Oswald and Daniel Ohrenhofer: “How can we improve our ability to predict conflicts? Scholars have struggled with this question for a long time. However, as a discipline, and especially over the last two decades, political science has made substantial progress. In general, what we need to improve predictions are advances in data and methodology. Data advances involve both improving the quality of existing data and developing new data sources. We propose a new data source for conflict forecasting efforts: Wikipedia.

The number of country page views indicates international salience of, or interest in, a country. Meanwhile, the number of changes to a country page indicate political controversy between opposing political views.

We took part in the Violence Early-Warning System’s friendly competition to predict changes in battle-related deaths. In our work, we evaluate our findings with out-of-sample predictions using held-out, previously unseen data, and true forecasts into the future. We find support for the predictive power of country page views, whereas we do not for page changes…

Globally available data, updated monthly, are ideal for (near) real-time forecasting. However, many commonly used data sources are available only annually. They are updated once a year, often with considerable delay.

Some of these variables, such as democracy or GDP, tend to be relatively static over time. Furthermore, many data sources face the problem of missing values. These occur when it is not possible to find reliable data for a variable for a given country.

Wikipedia is updated in real time, unlike many commonly used data sources, which may update only annually and with considerable delay

More recent data sources such as Twitter, images or text as data, or mobile phone data, often do not provide global coverage. What’s more, collecting and manipulating data from such sources is typically computationally and/or financially costly. Wikipedia provides an alternative data source that, to some extent, overcomes many of these limitations…(More)”.

Unsustainable Alarmism

Curated on Posted on by Stefaan Verhulst

Essay by Taylor Dotson: “Covid is far from the only global challenge we see depicted as a cataclysm in the making. In 1968, Paul Ehrlich predicted impending famine and social collapse driven by overpopulation. He compared the threat to a ticking bomb — the “population bomb.” And the claim that only a few years remain to prevent climate doom has become a familiar refrain. The recent film Don’t Look Up, about a comet barreling toward Earth, is obviously meant as an allegory for climate catastrophe.

But catastrophism fails to capture the complexities of problems that play out over a long time scale, like Covid and climate change. In a tornado or a flood, which are not only undeniably serious but also require immediate action to prevent destruction, people drop political disputes to do what is necessary to save lives. They bring their loved ones to higher ground. They stack sandbags. They gather in tornado shelters. They evacuate. Covid began as a flood in early 2020, but once a danger becomes long and grinding, catastrophism loses its purchase, and more measured public thinking is required.

Even if the extension of catastrophic rhetoric to longer-term and more complex problems is well-intentioned, it unavoidably implies that something is morally or mentally wrong with the people who fail to take heed. It makes those who are not already horrified, who do not treat the crisis as an undeniable, act-now-or-never calamity, harder to comprehend: What idiot wouldn’t do everything possible to avert catastrophe? This kind of thinking is why global challenges are no longer multifaceted dilemmas to negotiate together; they have become conflicts between those who recognize the self-evident truth and those who have taken flight from reality….(More)”.

Non-human humanitarianism: when ‘AI for good’ can be harmful

Curated on Posted on by Stefaan Verhulst

Paper by Mirca Madianou: “Artificial intelligence (AI) applications have been introduced in humanitarian operations in order to help with the significant challenges the sector is facing. This article focuses on chatbots which have been proposed as an efficient method to improve communication with, and accountability to affected communities. Chatbots, together with other humanitarian AI applications such as biometrics, satellite imaging, predictive modelling and data visualisations, are often understood as part of the wider phenomenon of ‘AI for social good’. The article develops a decolonial critique of humanitarianism and critical algorithm studies which focuses on the power asymmetries underpinning both humanitarianism and AI. The article asks whether chatbots, as exemplars of ‘AI for good’, reproduce inequalities in the global context. Drawing on a mixed methods study that includes interviews with seven groups of stakeholders, the analysis observes that humanitarian chatbots do not fulfil claims such as ‘intelligence’. Yet AI applications still have powerful consequences. Apart from the risks associated with misinformation and data safeguarding, chatbots reduce communication to its barest instrumental forms which creates disconnects between affected communities and aid agencies. This disconnect is compounded by the extraction of value from data and experimentation with untested technologies. By reflecting the values of their designers and by asserting Eurocentric values in their programmed interactions, chatbots reproduce the coloniality of power. The article concludes that ‘AI for good’ is an ‘enchantment of technology’ that reworks the colonial legacies of humanitarianism whilst also occluding the power dynamics at play…(More)”.

The digitalisation of social protection before and since the onset of Covid-19: opportunities, challenges and lessons

Curated on Posted on by Stefaan Verhulst

Paper by the Overseas Development Institute: “…discusses the main opportunities and challenges associated with digital social protection, drawing on trends pre-Covid and since the onset of the pandemic. It offers eight lessons to help social protection actors capitalise on technology’s potential in a risk-sensitive manner.

  • The response to Covid-19 accelerated the trend of increasing digitalisation of social protection delivery.
  • Studies from before and during the pandemic suggest that well-used technology holds potential to enhance provision for some service users, and played a notable role in rapid social protection expansion during Covid-19. It may also help reduce leakage or inclusion errors, lower costs and support improvements in programme design.
  • However, unless designed and implemented with careful mitigating measures, digitalisation may in some cases do more harm than good. Key concerns relate to potential risks and challenges of exclusion, protection and privacy violations, ‘technosolutionism’ and obscured transparency and accountability.
  • Ultimately, technology is a tool, and its outcomes depend on the needs it is expected to meet, the goals it is deployed to pursue, and the specific ways in which it is designed and implemented…(More)”.

Data scientists are using the most annoying feature on your phones to save lives in Ukraine

Curated on Posted on by Stefaan Verhulst

Article by Bernhard Warner: “In late March, five weeks into Russia’s war on Ukraine, an international team of researchers, aid agency specialists, public health experts, and data nerds gathered on a Zoom call to discuss one of the tragic by-products of the war: the refugee crisis.

The numbers discussedweregrim. The United Nations had just declared Ukraine was facing the biggest humanitarian crisis to hit Europe since World War II as more than 4 million Ukrainians—roughly 10% of the population—had been forced to flee their homes to evade Russian President Vladimir Putin’s deadly and indiscriminate bombing campaign. That total has since swelled to 5.5 million, the UN estimates.

What the aid specialists on the call wanted to figure out was how many Ukrainian refugees still remained in the country (a population known as “internally displaced people”) and how many had crossed borders to seek asylum in the neighboring European Union countries of Poland, Slovakia, and Hungary, or south into Moldova. 

Key to an effective humanitarian response of this magnitude is getting accurate and timely data on the flow of displaced people traveling from a Point A danger zone to a Point B safe space. And nobody on the call, which was organized by CrisisReady, an A-team of policy experts and humanitarian emergency responders, had anything close to precise numbers.

But they did have a kind of secret weapon: mobility data.

“The importance of mobility data is often overstated,” Rohini Sampoornam Swaminathan, a crisis specialist at Unicef, told her colleagues on the call. Such anonymized data—pulled from social media feeds, geolocation apps like Google Maps, cell phone towers and the like—may not give the precise picture of what’s happening on the ground in a moment of extreme crisis, “but it’s valuable” as it can fill in points on a map. ”It’s important,” she added, “to get a picture for where people are moving, especially in the first days.”

Ukraine, a nation of relatively tech-savvy social media devotees and mobile phone users, is rich in mobility data, and that’s profoundly shaped the way the world sees and interprets the deadly conflict. The CrisisReady group believes the data has an even higher calling—that it can save lives.

Since the first days of Putin’s bombing campaign, various international teams have been tapping publicly available mobility data to map the refugee crisis and coordinate an effective response. They believe the data can reveal where war-torn Ukrainians are now, and even where they’re heading. In the right hands, the data can provide local authorities the intel they need to get essential aid—medical care, food, and shelter—to the right place at the right time…(More)”

Data sharing between humanitarian organisations and donors

Curated on Posted on by Stefaan Verhulst

Report by Larissa Fast: “This report investigates issues related to data sharing between humanitarian actors and donors, with a focus on two key questions:

  • What formal or informal frameworks govern the collection and sharing of disaggregated humanitarian data between humanitarian actors and donors?
  • How are these frameworks and the related requirements understood or perceived by humanitarian actors and donors?

Drawing on interviews with donors and humanitarians about data sharing practices and examination of formal documents, the research finds that, overall and perhaps most importantly, references to ‘data’ in the context of humanitarian operations are usually generic and lack a consistent definition or even a shared terminology. Complex regulatory frameworks, variability among donor expectations, both among and within donor governments (e.g., at the country or field/headquarters levels), and among humanitarian experiences of data sharing all complicate the nature and handling of data sharing requests. Both the lack of data literacy and the differing perceptions of operational data management risks exacerbate many issues related to data sharing and create inconsistent practice (see full summary of findings in Table 3).

More specifically, while much formal documentation about data sharing between humanitarians and donors is available in the public domain, few contain explicit policies or clauses on data sharing, instead referring only to financial or compliance data and programme reporting requirements. Additionally, the justifications for sharing disaggregated humanitarian data are framed most often in terms of accountability, compliance, efficiency, and programme design. Most requests for data are linked to monitoring and compliance, as well as requests for data as ‘assurances’. Even so, donors indicated that although they request detailed/disaggregated data, they may not have the time, or human and/or technical capacity to deal with it properly. In general, donor interviewees insisted that no record level data is shared within their governments, but only aggregated or in low or no sensitivity formats….(More)”.