This contribution is published as part of the UNRISD Think Piece Series, From Disruption to Transformation? Linking Technology and Human Rights for Sustainable Development, launched to coincide with the 37th Session of the UN Human Rights Council and the 70th anniversary of the Universal Declaration of Human Rights. In this series, experts from academia, think tanks and civil society engage with the topic of linking technology and human rights, and share their experience at the front lines of policy-driven research and advocacy aimed at leaving no one behind in an increasingly digital, automated world.
Big data is being heralded by some as the solution to the missing statistics which are needed to measure progress on the Sustainable Development Goals. But big data is beset with its own flaws and pitfalls that may in fact compound human rights issues associated with the SDGs. This think piece discusses the role of indicators and whether new forms of statistics gathering are helping or hindering efforts to leave no one behind.
is Senior Research Officer for the Human Rights, Big Data and Technology project at the Human Rights Centre, University of Essex.
Making SDG 3 accountable
The latest global initiative to address the vast inequalities in the world, including health inequalities, is the 2030 Agenda for Sustainable Development
and the 17 corresponding Sustainable Development Goals (SDGs), which were adopted with a rallying call to "leave no one behind".
To know whether anyone is being left behind, the SDGs need an accountability system that includes monitoring and evaluation. To that end, the Agenda has 169 targets, and each target has at least one indicator on which all countries must measure and report. In SDG 3 (health for all), there are 13 targets and 27 indicators
, covering a wide spectrum of health measures from maternal and neonatal mortality to traffic accident deaths and HIV incidence
, to name just a few.
Indicators and their measurement are contentious subjects because they can result in unintended consequences. For example, before the SDGs, the Millennium Development Goals (MDGs) covered the years from 2000-2015, and had similarly aspirational objectives to halve global poverty and achieve various health and other social targets. Arguments have been made that poorly chosen MDG indicators diverted attention from other critically important lifesaving programmes. Case studies presented in the compelling Power of Numbers project led researchers to conclude that "target-setting is a valuable but a limited and blunt tool
, and that the methodology for target-setting should be refined to include policy responsiveness in addition to data availability criteria."
Neglected human rights in the SDGs
In our recently published paper, "Neglecting human rights: accountability, data and Sustainable Development Goal 3
", Paul Hunt
and I examine accountability and SDG 3, including monitoring. We posit that international human rights law places obligations on states at all times, including for activities and policies related to the SDGs. We looked at the SDG 3 indicators and found gaps through which breaches of human rights could fall undetected, especially around participation and quality health care. We also looked at SDG 3 data availability and examined suggestions that big data can help fill statistical gaps. In this think piece, I present some of that research examining whether the targets and indicators will capture the human rights duties of states to respect, protect and fulfill health rights.
Robust statistics are frequently absent in those countries and communities most "left behind". The Inter-agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs) which had overall responsibility for the development of the SDG indicators, notes that even for the 93 indicators with the most straightforward data collection systems, only about 50 percent of countries regularly produce that data
. When the Sustainable Development Solutions Network (SDSN) first proposed the SDG indicators in a report for the UN Secretary General, they acknowledged the indicators would take time to achieve, and estimated the global cost of improving data information systems to enable annual reporting at 1 billion USD annually, of which "at least $100–200 m will be required in incremental ODA
[official development assistance]."
Using big data to fill the gap?
To develop information systems to the level where they can generate data takes more than money; it also takes time–time to extend the systems out to where data must be collected from, to train people in gathering and transferring the data, and to build capacity in the national statistics offices for data analysis. It requires increasing budgets to employ and train more people–difficult enough in wealthy countries, let alone low- and middle-income countries. It has therefore been suggested that we could turn to big data
: proponents claim that data arising from online search queries, web posts, twitter and other social media, can provide more timely and even more accurate statistics than the traditional surveys and other tools used by national statistics offices. Furthermore, it is argued that such data collection methods are quicker and cost less, thus they could be appealing to cash strapped institutions or governments. Use of online searches has already shown, in some cases, to predict disease outbreaks more accurately than traditional methods. But mistakes have also been made
—outbreaks have been predicted that simply did not happen. Just because people use search terms such as "flu" this does not mean the searcher has flu or any other disease. In public health, this can result in a "false positive"—something is thought to be present when it isn’t.
An even greater risk in terms of people’s health rights are "false negatives"—something is happening, but it isn’t detected. To illustrate: there are 24 countries in which internet access reaches fewer than 10 percent of the population. If epidemic surveillance depends upon social media and online searches, then any outbreak of disease amongst the majority communities living entirely off the grid will not be captured. In this false negative scenario, the risk is high that an outbreak of infectious disease is not curtailed because it isn’t identified. People’s lives and health are threatened, especially the people too poor, marginalized, or remote to have access to the internet or to other forms of electronic communication.
Digital divides pose human rights risks
It has long been recognized that there is a digital divide both between and within countries that, globally, leaves four billion people without internet access
. The consequences of this divide could become an even greater human rights risk if our means of monitoring disease or reporting on SDG health indicators becomes dependent on big data. It has been suggested that big data could monitor SDG indicators on malaria, TB, HIV, "complementing traditional data sources and filling the gaps where they exist
". But there are two problems in poor countries with limited online access: big data arises from only small segments of the population, undoubtedly the urban wealthier communities, and secondly, countries without high internet coverage are the same countries that are missing the traditional data generated by national statistics offices. Of the 24 countries with fewer than 10 percent of the population having online access, the maternal mortality ratios range from 115 to 1374 (average 532) per 100,000 live births
, compared with the average of 14 across OECD countries
. Nations that have low internet access rates also have poor health systems, including health information systems.
This means that if we want to know whether people are being left behind in the global campaign to eliminate poverty and achieve health for all, we must first build robust statistics systems, and strong health systems which include good flows of health data. This should be the primary focus of overseas development assistance to improve data–not investment in big data initiatives in countries with weak national statistics systems. Big data, of course, has important roles to play in supplementing data collection in countries that have well-functioning national statistics offices; but in those without, developing reliance on big data tools will increase the risk that those people living off the grid become even less visible, with health rights unrealized, and left even further behind.
This piece originally appeared
on the Human Rights, Big Data and Technology Project blog. It has been republished with the author’s permission.
This work was supported by the UK’s Economic and Social Research Council [grant number ES/M010236/1].