Corpo de Serviço Templário no Algarve

Bom dia,

venho falar-vos um pouco deste nosso trabalho em construção na região do Algarve.

Desde há bastante tempo que as acções de serviço voluntário e humanitário, caritativas, são uma realidade entre nós, na Comenda de Laccobriga, sem, contudo, tomarem uma estruturação formal e de continuidade.

Com o advento deste novo projecto internacional da OSMTHU, através da implementação do Templar Corps International, abriu-se uma nova janela de oportunidade para que, através da união de vontades dispersas, possamos desenvolver um Serviço nas comunidades locais da região, de forma estruturada e continuada, formal e regular.

Para tal, aceitamos todos aqueles que, imbuídos deste espírito de missão, desta vontade e querer contribuir para um mundo melhor, possam juntar o seu trabalho ao nosso e, pelo conjunto, o resultado venha a ser profícuo e valoroso.

Qual o perfil do voluntário?

Gente de bom Coração e bons costumes. Gente que aprecie o sorriso de felicidade e de bem-estar plasmado na face de quem beneficia do nosso labor. Gente que assume e que cumpre a missão para a qual aceitou ser indigitada. Gente que se cumpre, que se realiza, no contribuir por um mundo melhor. Gente que reconhece e que sabe, que se identifica, com a matriz Templária de proteger e apoiar, mesmo que não sendo membro da Ordem (ou de qualquer Ordem ou de qualquer Organização formal, fraternal, nem mesmo de uma Associação), mas que sinta este impulso do cooperar activamente.

Como proceder?

Inscrever-se como membro no Templar Corps International e solicitar a conexão ao outspot do Algarve.

Victor Varela Martins

Knight Commander

Bolivia supera los 19.000 casos positivos por Covid-19 y total de fallecidos ascienden a 630

En las últimas 24 horas se registraron más de 600 contagios, más de la mitad de ellos en el departamento de Santa Cruz, epicentro de la pandemia en el país.

La cifra de personas infectadas en Bolivia por el nuevo coronavirus ha superado este lunes el umbral de los 19.000, después de registrar más de 600 contagios en un solo día, más de la mitad de ellos en el departamento de Santa Cruz, epicentro de la pandemia en el país sudamericano.

El Ministerio de Salud boliviano ha confirmado 614 contagios adicionales en 24 horas, lo que eleva el balance provisional de positivos hasta los 19.073. Solo en Santa Cruz, se han registrado 317 infectados más, hasta un total de 11.741, evidenciando una curva ascendente que preocupa a las autoridades.

El ministro de Defensa, Fernando López, ha anunciado tras la reunión del Comité de Operaciones de Emergencia Departamental (COED) que este martes comenzará un rastreo “casa por casa” en busca de posibles casos. Será el registro “más grande de Bolivia”, para lo cual ha pedido la colaboración de todos, desde fuerzas de seguridad a empresas privadas pasando por las propias familias, según el diario boliviano ‘La Razón’.

in Latercera.com


Fuerza a nuestros hermanos en Bolivia.

Siguen las recomendaciones del Dierctório Nacional de Bolívia del Templar Corps. 

As Epidemias Silenciosas

Enquanto o mundo se debate contra a pandemia do novo coronavírus, os velhos inimigos estão à espreita – e raramente há espaço para falar deles. Nos países em desenvolvimento já falta tudo: desde antivirais para os infetados com HIV, diagnóstico e tratamento de tuberculose ou redes mosquiteiras, para evitar a dengue e a malária – só esta última doença matou mais de 400 mil pessoas em 2018. Numa altura em que tantos sistemas de saúde estão entupidos com casos de covid-19, a época das monções aproxima-se a passo rápido do sub-continente indiano e de países da África Oriental, como Moçambique. As chuvas torrenciais, a partir do início de junho, deixarão para trás grandes corpos de água parada, ideais para a reprodução de mosquitos.

«Umas duas ou três semanas depois de começar a chover aumentam os casos de malária», explica ao SOL Jorge Seixas, diretor da Unidade de Clínica Tropical, do Instituto de Higiene e Medicina Tropical (IHMT), da Universidade Nova de Lisboa. «Nessas áreas, não há um mal que venha sozinho», lamenta.

Estamos a falar de uma catástrofe a uma escala colossal, mesmo comparada com a covid-19, que já matou mais de 335 mil pessoas em todo o planeta – um número que continuará a subir. As previsões, da OMS e várias universidades, apontam que as mortes por malária possam duplicar em 2020, devido aos estragos da pandemia, com uns 6,3 milhões casos adicionais de tuberculose entre 2020 e 2025, bem como 500 mil mortes extra de pacientes com HIV, entre 2020 e 2021.

Olhando para os números e para falta de resposta, é difícil não recordar o que estas doenças infecciosas têm em comum: afetam sobretudo países em esenvolvimento. Aliás, certa vez, numa TED Talk, Bill Gates, cuja fundação se tem dedicado ao problema, queixou-se que há mais investimento na investigação à cálvice masculina do que à malária. E importa lembrar que em 2016 gastou-se o equivalente a uns escassos 4 mil milhões de euros no combate à doença – ficou-se uns 2 mil milhões de euros abaixo do objetivo da Organização Mundial de Saúde (OMS).

«O nosso amigo Bill tem assim umas imagens interessantes», diz Jorge Seixas, entre gargalhadas. «Mas é verdade. Porque estas doenças tropicais atingem populações carenciadas, que não têm visibilidade política, em países com dificuldades sérias de gestão dos seus sistemas de saúde. E por não atingirem – por enquanto – com grande intensidades países ricos», considera o médico do IHMT. Afinal, «a indústria farmacêutica, não é uma instituição de beneficência », salienta.

«A indústria farmacêutica visa distribuir lucros aos seus acionistas».

‘Quando vamos para África, é um bocadinho diferente’

Uma das grandes questões por responder é qual a interação entre o SARS-Cov-2, o vírus que causa a covid-19, e todos estes parasitas, bactérias e outros vírus: simplesmente ainda é demasiado cedo para se saber.

«Aquilo que tem sido publicado até agora ainda não é conclusivo», acautela Ana Abecasis, diretora da Unidade de Saúde Pública Internacional e Bioestatística do IHMT, cujo trabalho de investigação ao HIV tem sido premiado. Curiosamente, no que toca às pessoas infetadas por este vírus, «não parece haver grande preocupação de que esses doentes tenham especial risco de desenvolver uma doença grave por covid», considera a investigadora.

É que, apesar do HIV debilitar o sistema imunitário, há a possibilidade dos tratamentos diários antivirais a que obriga tenham alguma ação contra o SARS-Cov-2. Alguns hospitais já os têm utilizado para tratar o novo coronavírus, «obviamente, sem evidência que haja efeito contra o vírus», salienta Abecasis. «Até porque não há nenhum medicamento que tenha comprovadamente mostrado eficácia contra a SARS-Cov-2». Desde que que os pacientes estejam bem tratados, ou seja, com uma carga viral suprimida, os efeitos do HIV são muito poucos.

«Claro que quando vamos para África as coisas são um bocadinho diferentes. Ainda podemos ter doentes que não tenham a infeção controlada», nota a investigadora. Se a situação não era fácil, piorou com a perturbação na distribuição de antivirais causada pela pandemia. «Vamos ter problemas. A nossa preocupação o ano passado, na sequência dos ciclones Idai e Kenneth, em Moçambique, também foi o acesso a medicamentos», relembra.

«Os doentes que não sejam medicados adequadamente vão voltar a ter cargas virais detetáveis. O perigo, por um lado, é voltarem a evoluir na infeção. Por outro, podem transmiti-la». Ao tomarem medicamentos de forma irregular, «podem desenvolver estirpes resistentes, diferentes das que já conhecemos», receia Abecasis – algo particularmente grave em países mais pobres. É que nos mais países desenvolvidos, «todos os doentes diagnosticados com a infeção HIV imediatamente fazem um teste de resistência», explica a investigadora. Se der positivo, recebem os medicamentos adequados, sem problemas de maior – o tratamento é simplesmente mais caro. Contudo, «em África os testes de resistência
não estão disponíveis».

Mosquiteiros e milagres

«O verdadeiro custo da covid–19 em África vai ser  medido mais pelo seu impacto nas outras doenças», declarou Peter Sands, diretor do Fundo Global contra Sida, Tuberculose e Malária, a semana passada. «Uma das grandes conquistas dos últimos anos foi a redução da mortalidade infantil devido à malária», considerou. «O ‘truque’ é a criança ser diagnosticada e tratada em 24 horas. E isso não vai acontecer se houver disrupções dos serviços de saúde».

As medidas de prevenção contra a doença também começam a ficar para trás. Dado que a malária é um parasita transmitido por um mosquito – «uma mosquita aliás, é a fêmea do mosquito que a transmite», ressalva Jorge Seixas – a primeira coisa é evitar o contacto com estes insetos. «A medida mais importante é a utilização de redes mosquiteiras, impregnadas com inseticida, debaixo das quais as pessoas dormem», explica.

Outra preocupação é que se esgotem os medicamentos para a malária, muitos dos quais estão a ser apresentados como ‘curas maravilha’ para a covid-19, sem qualquer evidência científica disso.

O primeiro exemplo que vem à cabeça é a hidroxicloroquina, cujos méritos são exaltados por líderes mundiais como Donald Trump e Jair Bolsonaro – mas o medicamento nem é o mais problemático. «A hidroxicloroquina já não é utilizada no tratamento da malária, em nenhum país no mundo. Já não funciona, o parasita da malária ganhou resistência», nota Seixas – o medicamento só faz falta a doentes como lupus ou artrite reumatoide. No que toca à malária, o problema pode ser um ‘chá milagroso’ contra o novo coronavírus, produzido em Madagáscar, que dá pelo nome de Covid-Organics.

«É feito a partir de uma planta que se chama artemísia. E dessa planta tiram-se medicamentos para a malária», explica o médico. «Levanta alguma preocupação, se o chá começa a ser muito usado em África».

Doença da pobreza

Além da malária e do HIV, a terceira das chamadas ‘Big Three’, as doenças da pobreza, é a tuberculose – que continua a ter uma incidência significativa em Portugal. As estatísticas mostram que o país fez avanços quanto à doença, mas teme-se que a pandemia deite tudo a perder.

De momento, verifica-se uma diminuição nos diagnósticos de tuberculose, «o que poderá significar casos de doença ainda não diagnosticados com consequente infecciosidade», explicou ao SOL Isabel Carvalho, diretora do Programa Nacional para a Tuberculose, da Direção Geral da Saúde.

É que, enquanto estamos todos preocupados com covid-19, «a tuberculose apresenta sintomas semelhantes a outras infeções respiratórias, tal como a tosse produtiva, febre e astenia», nota a médica. «Frequentemente, estes sintomas são desvalorizados, podendo conduzir ao atraso no diagnóstico», avisa, lembrando: «O início precoce do tratamento protege a família e conviventes e melhora o prognóstico».

Não que seja tudo más notícias. «O isolamento social, as medidas de etiqueta respiratória e de utilização de máscara permitem também ajudar na diminuição do contágio inerente à tuberculose», afirma a dirigente de saúde.

Ainda assim, a nível mundial, a brutal crise económica causada pela pandemia «terá repercussão no aumento de doenças infecciosas como a tuberculose, frequentemente associadas a grupos mais vulneráveis e com menor possibilidade de acesso aos cuidados de saúde», alerta Isabel Carvalho.

Trata-se de uma doença silenciosa e esquecida. «Quanto menos tuberculose, menos pensamos nela. E mais tarde será o diagnóstico, com consequência para a família», lamenta a médica.

No entanto, as ‘Big Three’ nem sequer estão entre as doenças mais negligenciadas – estas são literalmente apelidadas de ‘Doença Tropicais Negligenciadas’. A lista é longa e incluí nomes como dengue, leishmaniose, úlcera de Buruli ou a chicungunha. A OMS estima que mais de mil milhões de pessoas sofram de alguma destas doenças, um pouco por todo o mundo – mas sobretudo em países em desenvolvimento.

in SOL, Maio 2020, por João Campos Rodrigues

Networks: The ties that bind in times of stress

There is a lesson from the coronavirus that is front and center—the centrality of government. On a daily basis we see demonstrated the critical role of government, and the difference between strong, smart leadership from government officials who have society’s interest in mind and those that are slow to respond.

Looking beyond the daily headlines focused on the actions or inadequacies of government leaders, there is a companion lesson—the equally critical role of civil society and the hundreds/thousands of formal and informal networks that are sustaining our physical and mental health. The first responders acting as part of established organizations or citizens collaborating spontaneously to meet urgent needs; the many organizations and individuals that are providing patterns for protective masks so they can be made at home; the two college students who in 72 hours mobilized 1,300 volunteers (called Invisible Hands) to deliver food and medicine to the elderly and other vulnerable residents of New York City.

It is important at this time of crisis to understand, not just the value of government and the quality of its leaders—particularly as we approach a presidential election—but also the critical role of informal networks during a crisis and the rebuilding afterwards. Fortunately, a unique piece of new research helps us understand the role of informal networks in economic and social crises and restoration.

It’s not often that a book provides new insight into a long-standing topic, or, more accurately in this case, provides the rationale for long-held assumptions. “Extreme Economies” by Richard Davies is written principally for an economics audience. But it is both an economic and a sociological study that is particularly relevant today as we try to understand and deal with the coronavirus, and, in addition, for those of us working in the development field for our everyday work.

The book reviews nine micro-economies, what Davies identifies as “extreme economies.”  He investigates three economies that bounded back from disaster, three that collapsed, and three that provide a window into the future.

While he does not highlight this as an overall finding, what is striking is the role of informal networks in building social trust, the glue that binds societies and nations. Davies’ stories reveal a variety and diversity of functions performed by informal networks.

VITALITY

He documents how informal networks, given a flexible enabling environment that allows cultural norms and instincts to blossom, can meet community needs. He explains how following the 2004 devastating tsunami, local networks and practices, fueled restoration in Ache, Indonesia. It was the gold of women’s bangles that allowed families to finance home reconstruction, and individuals’ knowledge of local dynamics as to where to rebuild business that spurred their restoration. While donor projects played a role, they sometimes went astray: The Chinese construction of one village had many modern conveniences but was built inland away from costal activity, so the shiny new village developed no vibe and vitality.

In the refugee camp Zaatari in Jordan near the Syrian border and the Angola prison in Louisiana, illegal trading networks make life more vibrant and livable. In contrast to Zaatari, the more formal, better-designed and structured camp of Azraq, further from the Syrian border and from the nearest town, is less amenable and constrains informal networks that make life more tolerable.

COLLAPSE

In looking at economic failures, a common thread is that adverse government decisions can interfere with the functioning of traditional networks. The village of Bajo Chiquito—an indigenous tribal area in the famously impenetrable Darien Gap in the eastern region of Panama bordering Colombia—suffered economic collapse when construction of the Pan-American Highway bypassed the area, undercutting its role as the major river transportation hub to dispersed villages. The indigenous population failed to organize around its inherent advantage—to monetize its knowledge of the terrain that could be used to guide migrants daring to cross the Darien Gap.

Kinshasa, an economic disaster from decades of malign Congolese governance, has a vibrant pirate economy. Informal, illegal networks sustain life, but only to a level just above poverty and do not make up for incompetent and corrupt government.

Glasgow in the 19th and into the 20th century was a trading and industrial hub of wealth and economic vitality centered first on tobacco trade and subsequently as the world’s foremost builder of ocean vessels. Post-World War II rebuilding of shipyards by Germany and Japan with more modern techniques deprived Glasgow of its economic competitiveness. At the same time, the design of new urban structures broke historic neighborhood networks important to the city’s vitality and support structures.

FUTURE

Davies looks at three locales that provide insights into the future. In Akita, the most aged region in a rapidly aging Japan, civic organizations are providing the elderly with group activities that keep them physically and mentally active and alive. In Tallinn, Estonia, advanced adoption of e-government has provided the foundation for business and civic innovation. In Santiago, Chile, implementation of the economic policies of the “Chicago Boys” created an economic success story but also one of the widest economic divides in the world, bringing a sense of relative poverty and resentment of elites that has sparked spontaneous populist protests.

LESSONS FOR CRISES AND INTERNATIONAL DEVELOPMENT

The development community, particularly those focusing on humanitarian and fragile environments, understands the critical role of the social compact: trust between citizens and government. What has not been fully understood or received sufficient attention is the role of informal networks. They can catalyze and cement the social compact, or, where trust in government is absent, provide citizens the services and sense of community that government fails to provide.

In this time of global crisis affecting all countries, developed and developing, we would do well to take Davies’ research as a political economy guide in understanding the essential role of networks and how we can allow them to flourish, or, at a minimum, do them no harm. “Extreme Economies” is a must-read, not just for my colleagues who are practitioners and students of international development, but for anyone seized with thinking and acting to get us beyond COVID-19 and rebuild our economic and social structures.

in Brookings.org by 

Turning back the Poverty Clock: How will COVID-19 impact the world’s poorest people?

The release of the IMF’s World Economic Outlook provides an initial country-by-country assessment of what might happen to the world economy in 2020 and 2021.

Using the methods described in the World Poverty Clock, we ask what will happen to the number of poor people in the world—those living in households with less than $1.90 per person per day in actual or imputed spending—given this new economic forecast.

We take the difference between the IMF’s April 2020 forecasts for GDP growth and their forecast from October 2019 as “the COVID effect,” a slight simplification because other things have also changed in the world that may have caused the IMF to alter its forecasts. However, the largest change is clearly caused by COVID-19 and the policy response around the world.

The summary result is that some 690 million people are likely to be in poor households in 2020, compared to our previous estimates of 640 million people. (A careful reader might note that the World Poverty Clock had been estimating about 600 million in poverty in 2020, but newly updated population estimates, new household expenditure data, and new household survey data have also been incorporated into the model. We don’t count those changes as part of the COVID effect, however.)

Our post-COVID-19 estimate is that extreme poverty in the world will rise this year by about 50 million people compared to the original 2020 forecast, and by 40 million people compared to our 2019 estimate. This is right in the middle of the range estimated by a team of World Bank economists—40 million to 60 million more poor people. This is not surprising as we are using very similar methodologies and data. The number is, however, far smaller than the estimates put forward in one scenario by Sumner and co-authors, who suggested that poverty could rise by 420 million to 580 million people, a figure that has been picked up by the media and advocacy organizations as “half a billion.”

All these estimates have a high degree of uncertainty and yet COVID-19 has attacked relatively advanced economies where the absolute numbers of extreme poor are small. If we were looking at the impact of COVID-19 on poverty as defined by national poverty lines, the number would be far higher. We also have little real-time information on how lockdowns will affect income distribution, or about how effective government efforts to strengthen safety net programs are likely to be. COVID-19 may be less disruptive to subsistence farmers, who are heavily represented among the extreme poor than to urban workers who may be vulnerable to income losses but whose initial living conditions were better.

Bearing this in mind, if we accept the IMF scenario for 2020, it suggests that all the progress in reducing poverty since the launch of the Sustainable Development Goals (SDGs) in September 2015 has been lost. We will enter the U.N. Decade of Action with the same distance to travel on poverty reduction, but only ten years in which to do it.

To put this into context, 2020 will be the first time this century that the number of poor people will rise, a fact which can be seen in real time as the World Poverty Clock ticks “backward.” This comes after a spell of poverty reduction averaging almost 100 million people per year between 2008 and 2013. And even though the escape rate out of poverty had fallen recently, with poverty becoming more concentrated in fragile states where progress has been harder to achieve, there was still movement in the right direction.

COVID-19 has seriously affected these trends in ways that are still not clear-cut. The figures below try to identify the most seriously affected countries. Figure 1 shows 12 countries that are likely to see an increase in poverty of over 1 million people in 2020 as a result of COVID-19. They are in Asia and Africa, with Brazil as the sole exception. India and Nigeria stand out as likely to add 10 million and 8 million to the poverty rolls in 2020. In all these countries, COVID-19 has demonstrated the vulnerability of people who have only recently been able to escape poverty.

Figure 1. Countries where poverty headcounts are likely to rise the most due to COVID-19

An alternative way of looking at the impact of COVID-19 is to ask which countries are likely to have the largest increase in poverty rates after COVID-19. Countries with an increase in extreme poverty rates (defined as people living in poverty divided by the total population) greater than 3 percentage points are shown in Figure 2. There are several small island states in this group, including Timor Leste, Sao Tome and Principe, and the Solomon Islands. In fact, there are now 60 countries that are off-track to meet the SDG target of eradicating poverty, even using the less-demanding World Bank threshold of counting countries as off-track if they do not bring extreme poverty down to below 3 percent of their population.

Figure 2. Countries with the largest increases in poverty rates from COVID-19

So what can be done? While advanced economies are trying to balance the impact on public health and the impact on the economy by adjusting policy responses like the degree of social distancing, developing countries are faced with much harder policy choices. Most are commodity dependent (in two-thirds of developing countries, commodities account for over 60 percent of exports), and have seen prices fall by 21 percent so far this year. Many rely on remittances, projected to decline by double digits, and/or tourism, which has almost collapsed. They face substantial non-resident portfolio outflows, estimated at almost $100 billion in March and April alone. Over 90 countries have already applied to access the IMF’s emergency credit facilities, and the G-20 have agreed to a moratorium on debt service payments owed by the poorest countries.

The health responses, in terms of lockdowns and social distancing, are also less compelling in most developing countries, sometimes as a matter of choice and sometimes as a matter of practicality. Although the number of cases in developing countries is still small, double digit (or close to) increases in active cases are now being recorded in India, Brazil, Mexico, Ecuador, and South Africa. Social distancing is hard to apply or enforce in the slums of many developing country cities, and the safety net is not sufficiently well developed to allow people to stay at home without working and still feed their families.

With limited fiscal space, developing countries are planning on some fiscal stimulus to expand national health services and protect households, and on fiscal or credit help to keep small businesses afloat and help pay workers’ salaries, but they are heavily constrained. Many have high debt and are being downgraded (Fitch has downgraded 33 countries since the crisis), and if they fund spending by issuing national currency, they will suffer currency depreciations and inflation. The multilateral development banks are helping to a degree, but front-loading and accelerated disbursements cannot match the scale of what is needed. Developing country economies will contract, but not as much as in advanced economies partly because they cannot enforce total lockdowns to the same degree.

For now, the worst fears of the pandemic raging through developing countries have not been realized. If the IMF growth forecasts are roughly correct, both in the size of the global downturn and the distribution across countries, then the impact will be to raise global poverty to a level last seen in 2015. The challenge then will be to accelerate inclusive growth in the recovery phase.

in Brookings.org by Homi Kharas and Kristofer Hamel

What is the future of poverty in Africa?

The global burden of poverty is highly concentrated in Africa, with more than 150 million people living in extreme poverty in just two countries – Nigeria and the Democratic Republic of the Congo – according to World Data Lab.

Projections from its system estimate that nearly 80% of the countries unable to eliminate poverty by 2030 will be in Africa. When weighted for absolute number of people living in poverty, that figure increases to more than 90%.

In September 2020, the Sustainable Development Goals (SDGs) will conclude their fifth year. While countries still have ample time to implement more aggressive policies before the SDGs expire in 2030, five years is enough time to begin talking about what progress has been made. Or in many cases, how much work remains to be done.

The SDGs are a broad suite of 17 development goals with an even more extensive list of 169 targets established by the United Nations (UN) in 2015. They aim to advance solutions for everything from climate change to urban planning to gender equality – and nearly everything in between.

At base however, the SDGs are about poverty reduction. ‘The SDGs are a bold commitment to finish what we started (i.e. Millennium Development Goals), and end poverty in all forms and dimensions by 2030,’ according to the UN.

The reality is that Africa is showing both gains and losses. Roughly 40% of people in Africa live below US$1.90 a day. People in sub-Saharan Africa are more than twice as likely to live in poverty as those in South Asia, the next poorest region globally.

According to projections from the International Futures (IFs) modelling platform, hosted by the Frederick S Pardee Center for International Futures at the University of Denver, sub-Saharan Africa accounts for roughly 60% of the global population living in poverty in 2020.

There does appear to be marginal progress, as that figure is down slightly from about 70% in 2015. This coincides with a recent report from the Brookings Institution, which noted that for the first time in decades more Africans are escaping poverty than are being dragged into it.

However, that snapshot ought to be taken with a pinch of salt. For one, the global economy has been churning at a pace not seen since before the great financial crisis. Calls for a global recession have eased slightly, but a global slowdown is still possible, particularly given recent financial shocks in the United States, uncertainty regarding the economic future of the United Kingdom and various concerns in China.

There are also other factors that contributed to the recent boon that could reverse. The recovery of oil prices since 2016 has no doubt enabled governments to step up social grant programmes and other poverty-reduction measures.

Brent Crude is trading 40% higher than in September 2015, and many African governments tend to use commodity revenues in the absence of sustainable revenue collection practices, a phenomenon sometimes called the resource curse. A global slowdown would dampen demand for oil – among other things.

By 2030 only 11 of Africa’s 54 countries are projected to be able to eliminate extreme poverty, according to World Data Lab. Nearly all the countries projected to achieve the target are in North Africa or are small island states. Throughout the rest of the world, only 12 other countries are forecast to fall short of the SDG target.

Both IFs and World Data Lab projections point to a reversal of the current trend of declining poverty. In other words, the burden of poverty is not only expected to be concentrated in Africa, but the number of people living in poverty in Africa is also forecast to rise over the next 10 years.

One factor undeniably driving this long-term increase in poverty is Africa’s rapid population growth. Africa is projected to decrease the proportion of people living in poverty by nearly five percentage points between 2015 and 2030, according to IFs. But despite that percentage reduction, the absolute number of people living in poverty is forecast to more than double over that same period, swelling from around 270 million in 2015 to more than 550 million in 2030.

The elimination of extreme poverty must be the foundation of any comprehensive development strategy for Africa, regardless of the stakeholders developing the guiding documents. African governments, development agencies, non-governmental organisations and civil society need to design policies and advocate strategies that have, at their core, poverty elimination as their primary focus.

Providing basic infrastructure (Goal 6), quality healthcare (Goal 3) and education (Goal 4) while attempting to create a globally competitive economy (Goal 8) are all worthwhile ambitions. But these achievements are less meaningful if hundreds of millions of Africans continue to miss out on those opportunities. Furthermore, it is likely that gaps in inequality will only continue to increase, with unpredictable consequences for political and economic stability.

The investment necessary to achieve the full suite of SDGs is enormous and potentially beyond the scope of African governments to achieve without significant help from the international community. Even if Africa were to halve extreme poverty from its current levels by 2030, that would be a significant achievement based on current projections.

The international community should not view a halving of poverty in Africa as a failure because the continent did not officially meet Target 1.1. The challenge is massive, and the resources required to combat it are no less overwhelming. But if this is an issue the international community takes seriously, then it is already behind schedule.

in issafrica.org by BY ZACHARY DONNENFELD

Ethical algorithm design should guide technology regulation

Society expects people to respect certain social values when they are entrusted with making important decisions. They should make judgments fairly. They should respect the privacy of the people whose information they are privy to. They should be transparent about their deliberative process.

But increasingly, algorithms and the automation of certain processes are being incorporated into important decision-making pipelines. Human resources departments now routinely use statistical models trained via machine learning to guide hiring and compensation decisions. Lenders increasingly use algorithms to estimate credit risk. And a number of state and local governments now use machine learning to inform bail and parole decisions, and to guide police deployments. Society must continue to demand that important decisions be fair, private, and transparent even as they become increasingly automated.

Nearly every week, a new report of algorithmic misbehavior emerges. Recent examples include an algorithm for targeting medical interventions that systematically led to inferior outcomes for black patients,[1] a resume-screening tool that explicitly discounted resumes containing the word “women” (as in “women’s chess club captain”), and a set of supposedly anonymized MRI scans that could be reverse-engineered to match to patient faces and names.[2]

In none of the previous cases were the root causes some malintent or obvious negligence on the part of the programmers and scientists who built and deployed these models. Rather, algorithmic bias was an unanticipated consequence of following the standard methodology of machine learning: specifying some objective (usually a proxy for accuracy or profit) and algorithmically searching for the model that maximizes that objective using colossal amounts of data. This methodology produces exceedingly accurate models—as measured by the narrow objective the designer chooses—but will often have unintended and undesirable side effects. The necessary solution is twofold: a way to systematically discover “bad behavior” by algorithms before it can cause harm at scale, and a rigorous methodology to correct it.

Many algorithmic behaviors that we might consider “antisocial” can be detected via appropriate auditing—for example, explicitly probing the behavior of consumer-facing services such as Google search results or Facebook advertising, and quantitatively measuring outcomes like gender discrimination in a controlled experiment. But to date, such audits have been conducted primarily in an ad-hoc, one-off manner, usually by academics or journalists, and often in violation of the terms of service of the companies they are auditing.

We propose that more systematic, ongoing, and legal ways of auditing algorithms are needed. Regulating algorithms is different and more complicated than regulating human decision-making. It should be based on what we have come to call ethical algorithm design,[3] which is now being conducted by a community of hundreds of researchers. Ethical algorithm design begins with a precise understanding of what kinds of behaviors we want algorithms to avoid (so that we know what to audit for), and proceeds to design and deploy algorithms that avoid those behaviors (so that auditing does not simply become a game of whack-a-mole).

Let us discuss two examples. The first comes from the field of algorithmic privacy and has already started to make the transition from academic research to real technology used in large-scale deployments. The second comes from the field of algorithmic fairness, which is in a nascent stage (perhaps 15 years behind algorithmic privacy), but is well-positioned to make fast progress.

DATA PRIVACY: ADVANCING TO A BETTER SOLUTION

Corporate and institutional data privacy practices unfortunately rely on heuristic and largely discredited notions of “anonymizing” or “de-identifying” private data. The basic hope is that, by removing names, social security numbers, or other unique identifiers from sensitive datasets, they will be safe for wider release (for instance, to the medical research community). The fundamental flaw with such notions is that they assume the dataset in question is the only one in the world, and are thus highly vulnerable to “de-anonymization” attacks that combine multiple sources of data.

The first high-profile example of such an attack was conducted in the mid-1990s by Latanya Sweeney, who combined allegedly anonymized medical records released by the state of Massachusetts with publicly available voter registration data to uniquely identify the medical record of then-governor William Weld—which she mailed to his office for dramatic effect.[4] As in this example, anonymization techniques often fail because of the wealth of hard-to-anticipate extra information that is out there in the world, ready to be cross-referenced by a clever attacker.

The breakthrough that turned the field of data privacy into a rigorous science occurred in 2006, when a team of mathematical computer scientists introduced the concept of differential privacy.[5] What distinguished differential privacy from previous approaches is that it specified a precise yet extremely general definition of the term “privacy”: specifically, that no outside observer (regardless of what extra information they might have) should be able to determine better than random guessing whether any particular individual’s data was used to construct a data release. This implies that the observer cannot infer any properties of that individual’s data that are idiosyncratic to them.

Broadly speaking, differential privacy is achieved by carefully adding noise or randomness to data or computations in a way that obscures individual data points while still providing useful estimates of statistical quantities. For example, to privately release the average of a set of employee salaries, we first compute the average to numerical precision, but then add a randomly chosen number to it before release. Given enough data, the noisy version is still accurate, but it contains very little information about any particular employee’s salary.

The introduction of differential privacy sparked more than a decade of algorithmic research determining how to use data that is subject to privacy guarantees, and what the trade-offs are between the accuracy of estimates and privacy guarantees. In recent years, differential privacy has become mature enough for serious deployment. There are large-scale implementations in the tech industry by Google,[6] Apple,[7] and other companies. But the true “moonshot” application of the technology is just around the corner. The U.S. Census Bureau will apply the protections of differential privacy to all statistics released as part of the 2020 census.[8] Here, the trade-offs are more than hypothetical, and census officials who are obligated to protect privacy are engaged in a vigorous debate with the downstream users of census data[9] about how exactly to balance privacy and accuracy. Differential privacy (correctly) takes no position on how this balance should be chosen, but it provides a precise language in which to focus the debate.

ALGORITHMIC FAIRNESS: A WORK IN PROGRESS

In contrast to differential privacy, the study of algorithmic fairness is relatively nascent. There is no agreement on a single definition, and indeed, it is known that several appealing and reasonable measures of algorithmic fairness are in mathematical conflict with one another.[10] It is thus already known that the study of algorithmic fairness will necessarily be nuanced and complex—practitioners will need to think about trade-offs not only between fairness and accuracy, but also between different notions of fairness.

Nevertheless, the field is off to a promising start. It is possible to quantify different kinds of harms that an algorithm can cause, such as denying a loan to a creditworthy applicant or overestimating an inmate’s risk of recidivism. One can then demand that such harms do not disproportionately fall on one group (such as a racial minority or gender) more than another. Recent research has developed algorithms that can enforce such demands even on relatively refined subgroups that combine multiple protected classes, such as race, gender, age, income, and disability status.[12] For example, developers can enforce constraints demanding that the rate of false loan rejections for disabled Hispanic women over age 55 not be higher than the false rejection rate for the overall population. Such methods can provide progressively stronger fairness guarantees should stakeholders feel they are necessary.

Equally important is the fact that one can audit algorithms and predictive models for such harm imbalances. For example, a stark difference in false positive rates between black and white inmates in a recidivism prediction algorithm known as COMPAS was the subject of a well-publicized 2016 ProPublica article. Note that checking that an algorithm has similar false positive rates across two populations does not require the code of the algorithm; all that is necessary is black-box experimentation allowing us to compute a small number of averages.

ALGORITHMIC APPROACHES TO TECHNOLOGY REGULATION

To summarize, there are now operational definitions of algorithmic privacy and fairness, some understanding of how to design algorithms that satisfy those definitions, and methods to audit whether a given algorithm or model violates them (and by how much). We believe this emerging science of ethical algorithm design invites reconsideration of how large technology companies and their products and services are regulated.

The current technology regulatory framework is largely reactive. Consider the Federal Trade Commission’s (FTC) recent $5 billion fine against Facebook for data privacy violations, currently under review by a federal judge. While widely hailed as a harbinger of a newly aggressive regulatory era, the fine was, in fact, in response to violations of a previous 2011 consent agreement. These violations were uncovered by The New York Times and The Observer of London, not the FTC itself. And like the earlier agreement, the recent settlement contains virtually no technical mechanisms for enforcement, only human and organizational ones, such as new corporate procedures and the creation of oversight committees. This cycle is typical of U.S. technology regulation: The damage is discovered after the fact, not during the harm; a monetary fine is levied, and new guidelines imposed; but the regulator has no ongoing, real-time mechanisms to monitor that things have actually changed or improved.

An alternative approach is to enable tech regulators to be proactive in their enforcement and investigations. If there really is gender bias in the credit limits granted for Apple’s new credit card (as has been alleged anecdotally), it could be discovered by regulators in a controlled, confidential, and automated experiment with black-box access to the underlying model. If there is racial bias in Google search results or housing ads on Facebook, regulator-side algorithms making carefully designed queries to those platforms could conceivably discover and measure it on a sustained basis.

Let us anticipate and address some objections to such proposals, from the perspectives of both tech companies and their regulators. Large technology companies typically protest calls to make their algorithms, models, or data more openly accessible, on the grounds that it severely compromises their intellectual property. Google’s search and Facebook’s Newsfeed algorithms, as well as the details of their advertising platforms and predictive models, are claimed to be the “secret sauce” that drives the well-earned competitive advantages that such companies enjoy. Tech giants might argue that allowing unfettered, automated access to such proprietary resources would permit reverse engineering by competitors, as well as “gaming” by rogue actors on both the user and advertising sides.

We agree, which is why we do not propose such access for everyone—only for the appropriate regulators, and only for permitted legal and regulatory purposes. There is some precedent for such arrangements in the much more heavily regulated finance industry. The Securities and Exchange Commission, Commodity Futures Trading Commission, and the sector’s self-regulatory agency, FINRA, have direct and timely access to tremendously sensitive and granular trading data, which allows them to identify prices, volumes, and counterparties. Such data permits these agencies, for example, to infer the portfolios of large investors and the underlying strategies and algorithms of the most proprietary hedge funds. It also allows the agencies to monitor for insider trading and illegal market behaviors such as “spoofing.” And of course, these regulators are not permitted to use this data for nonregulatory purposes, such as starting a competing hedge fund. Similar restrictions would bind tech regulators.

A more technical objection is that algorithmic auditing cannot identify and fix all potential regulatory problems, and that what we refer to here as “algorithms” and “models” are often complex, interacting systems that might cross organizational or even corporate boundaries. For instance, a recent study demonstrated bias in Google search results toward showing STEM job advertisements to men more frequently than women, but at least part of the cause was the willingness of advertisers to pay more for female clicks. In this instance, the blame cannot be placed exclusively or even primarily on Google’s underlying algorithms. But we believe that auditing and measuring such bias is still an important regulatory goal, since its discovery is the first step toward understanding and solutions—even if there may not be simple fixes.

An objection or observation from the regulatory side is that the agencies are currently ill-equipped to engage in an algorithmic arms race with their subjects. Queries and experiments must be designed carefully and scientifically, A/B testing must become a standard tool, and deep understanding and practical experience in AI and machine learning will be a prerequisite. While some of the agencies are fortunate to have significant quantitative expertise (for example, in the form of economics doctorates that are prepared to consider theoretical questions about markets and competition), there are few leaders or staffers whose training is in artificial intelligence, computer science, mathematics and statistics—in other words, the areas of expertise of the companies they oversee. A nontrivial change in the composition of these agencies would be necessary.

We would argue, however, that there is no viable alternative. The sooner these changes begin, the better for society as a whole. Regulators have been playing catch-up with their tech subjects for a couple of decades now, and the gap is getting wider. Legal and policy changes are required as well. For instance, in matters of acquisitions and mergers, tech regulators are often forced to view transactions through the lens of whether a given market is “nascent” or “mature.” The fluidity of technology, and the data that powers it, often renders such distinctions quaint at best, debilitating at worst. Tech giants often view an acquisition not from the perspective of what “market” it lies in, but what new source of consumer, advertising, logistic, or other data it will provide. They view their various products and services (search, advertising, browser, maps, email, etc. in the case of Google; shopping, advertising, video, Alexa, etc. in the case of Amazon) not as silos in separate markets, but of a single piece with integrated technology, data and strategy. The longer regulators are forced to decompose the world in ways that are at odds with industry reality, the bigger the gap between regulators and subjects becomes.

Decision-making driven by machine learning—because of its speed and scale, and because of the unanticipated side effects of its behavior—requires a new regulatory approach. It must be guided by the emerging science of ethical algorithm design, which can both shed light on the specific social properties we want from algorithms and give us guidance on how to audit and enforce these properties. Existing or new regulatory agencies must be able to automatically audit algorithms at scale. This will require sea changes at the organizational level, but it is already feasible at the scientific level.

FOOTNOTES
1Ziad Obermeyer, Brian Powers, Christine Vogeli, Sendhil Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science Vol. 366, Issue 6464, pp. 447-453, Oct. 25, 2019. DOI: 10.1126/science.aax2342
2Christopher G. Schwarz, et al., “Identification of Anonmyous MRI Research Participants with Face-Recognition Software,” The New England Journal of Medicine, 2019; 381:1684-1686, Oct. 24, 2019. DOI: 10.1056/NEJMc1908881
3Michael Kearns and Aaron Roth, “The Ethical Algorithm,” Oxford University Press, Nov. 1, 2019.
4Latanya Sweeney, “Weaving Technology and Policy Together to Maintain Confidentiality,” The Journal of Law, Medicine & Ethics,” Vol. 25, Issue 2-3, June 1, 1997. https://doi.org/10.1111/j.1748-720X.1997.tb01885.x
5Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith, “Calibrating Noise to Sensitivity in Private Data Analysis,” Journal of Privacy and Confidentiality, May 30, 2017. https://doi.org/10.29012/jpc.v7i3.405
6Ulfar Erlingsson, Vasyl Pihur, Aleksandra Korolova, “RAPPOR: Randomized Aggregable Privacy-Preserving Ordinal Response,” CCS ’14: Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, pp. 1054-1067, November 2014. https://doi.org/10.1145/2660267.2660348
7“Learning with Privacy at Scale,” Apple Machine Learning Journal, Vol. 1, Issue 8, December 2017. https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html
8John M. Abowd, “The U.S. Census Bureau Adopts Differential Privacy,” KDD’ 18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2867, July 2018. https://doi.org/10.1145/3219819.3226070
9Census data is used to distribute federal funds to local communities, and is used by social scientists to study demographic trends.
10Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores,” Proceedings of Innovations in Theoretical Computer Science, 2017. arXiv:1609.05807
11For example, equalizing the rate at which a bank initiates loans across demographic groups will generally be incompatible with equalizing the “false negative” rate (i.e., the rate at which creditworthy applicants are denied loans across groups), which in turn will generally be incompatible with equalizing the positive predictive value of lending decisions (i.e., the rate at which people granted loans avoid default) across groups.
12Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu, “Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness,” Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2564-2572, 2018. http://proceedings.mlr.press/v80/kearns18a.html

in Brookings.edu by Michael Kearns and Aaron RothMonday, 2020