The UK’s Gender Pay Gap Open Data Law Has Flaws, But Is A Positive Step Forward

Curated on Posted on by Stefaan Verhulst

Article by Michael McLaughlin: “Last year, the United Kingdom enacted a new regulation requiring companies to report information about their gender pay gap—a measure of the difference in average pay between men and women. The new rules are a good example of how open data can drive social change. However, the regulations have produced some misleading statistics, highlighting the importance of carefully crafting reporting requirements to ensure that they produce useful data.

In the UK, nearly 11,000 companies have filed gender pay gap reports, which include both the difference between the mean and median hourly pay rates for men and women as well the difference in bonuses. And the initial data reveals several interesting findings. Median pay for men is 11.8 percent higher than for women, on average, and nearly 87 percent of companies pay men more than women on average. In addition, over 1,000 firms had a median pay gap greater than 30 percent. The sectors with the highest pay gaps—construction, finance, and insurance—each pay men at least 20 percent more than women. A major reason for the gap is a lack of women in senior positions—UK women actually make more than men between the ages of 22-29. The total pay gap is also a result of more women holding part-time jobs.

However, as detractors note, the UK’s data can be misleading. For example, the data overstates the pay gap on bonuses because it does not adjust these figures for hours worked. More women work part-time than men, so it makes sense that women would receive less in bonus pay when they work less. The data also understates the pay gap because it excludes the high compensation of partners in organizations such as law firms, a group that includes few women. And it is important to note that—by definition—the pay gap data does not compare the wages of men and women working the same jobs, so the data says nothing about whether women receive equal pay for equal work.

Still, publication of the data has sparked an important national conversation. Google searches in the UK for the phrase “gender pay gap” experienced a 12-month high the week the regulations began enforcement, and major news sites like Financial Times have provided significant coverage of the issue by analyzing the reported data. While it is too soon to tell if the law will change employer behavior, such as businesses hiring more female executives, or employee behavior, such as women leaving companies or fields that pay less, countries with similar reporting requirements, such as Belgium, have seen the pay gap narrow following implementation of their rules.

Requiring companies to report this data to the government may be the only way to obtain gender pay gap data, because evidence suggests that the private sector will not produce this data on its own. Only 300 UK organizations joined a voluntary government program to report their gender pay gap in 2011, and as few as 11 actually published the data. Crowdsourced efforts, where women voluntary report their pay, have also suffered from incomplete data. And even complete data does not illuminate variables such as why women may work in a field that pays less….(More)”.

Sharing the benefits: How to use data effectively in the public sector

Curated on Posted on by Stefaan Verhulst

Report by Sarah Timmis, Luke Heselwood and Eleonora Harwich (for Reform UK): “This report demonstrates the potential of data sharing to transform the delivery of public services and improve outcomes for citizens. It explores how government can overcome various challenges to ‘get data right’ and enable better use of personal data within and between public-sector organisations.

Ambition meets reality

Government is set on using data more effectively to help deliver better public services. Better use of data can improve the design, efficiency and outcomes of services. For example, sharing data digitally between GPs and hospitals can enable early identification of patients most at risk of hospital admission, which has reduced admissions by up to 30 per cent in Somerset. Bristol’s Homeless Health Service allows access to medical, psychiatric, social and prison data, helping to provide a clearer picture of the complex issues facing the city’s homeless population. However, government has not yet created a clear data infrastructure, which would allow data to be shared across multiple public services, meaning efforts on the ground have not always delivered results.

The data: sticking points

Several technical challenges must be overcome to create the right data infrastructure. Individual pieces of data must be presented in standard formats to enable sharing within and across services. Data quality can be improved at the point of data collection, through better monitoring of data quality and standards within public-sector organisations and through data-curation-processes. Personal data also needs to be presented in a given format so linking data is possible in certain instances to identify individuals. Interoperability issues and legacy systems act as significant barriers to data linking. The London Metropolitan Police alone use 750 different systems, many of which are incompatible. Technical solutions, such as Application Programming Interfaces (APIs) can be overlaid on top of legacy systems to improve interoperability and enable data sharing. However, this is only possible with the right standards and a solid new data model. To encourage competition and improve interoperability in the longer term, procurement rules should make interoperability a prerequisite for competing companies, allowing customers to integrate their choices of the most appropriate products from different vendors.

Building trustworthiness

The ability to share data at scale through the internet has brought new threats to the security and privacy of personal information that amplifies the need for trust between government and citizens and across government departments. Currently, just 9 per cent of people feel that the Government has their best interests at heart when data sharing, and only 15 per cent are confident that government organisations would deal well with a cyber-attack. Considering attitudes towards data sharing are time and context dependent, better engagement with citizens and clearer explanations of when and why data is used can help build confidence. Auditability is also key to help people and organisations track how data is used to ensure every interaction with personal data is auditable, transparent and secure. …(More)”.

How to Prevent Winner-Takes-All Democracy

Curated on Posted on by Stefaan Verhulst

Kaushik Basu at Project Syndicate: “Democracy is in crisis. Fake news – and fake allegations of fake news – now plagues civil discourse, and political parties have proved increasingly willing to use xenophobia and other malign strategies to win elections. At the same time, revisionist powers like Vladimir Putin’s Russia have been stepping up their efforts to interfere in elections across the West. Rarely has the United States witnessed such brazen attacks on its political system; and rarely has the world seen such lows during peacetime….

How can all of this be happening in democracies, and what can be done about it?

On the first question, one hypothesis is that new digital technologies are changing the structural incentives for corporations, political parties, and other major institutions. Consider the case of corporations. The wealth of proprietary data on consumer preferences and behavior is producing such massive returns to scale that a few giants are monopolizing markets. In other words, markets are increasingly geared toward a winner-take-all game: multiple corporations can compete, but to the victor go the spoils.1

Electoral democracy is drifting in the same direction. The benefits of winning an election have become so large that political parties will stoop to new lows to clinch a victory. And, as with corporations, they can do so with the help of data on electoral preferences and behavior, and with new strategies to target key constituencies.

This poses a dilemma for well-meaning democratic parties and politicians. If a “bad” party is willing to foment hate and racism to bolster its chances of winning, what is a “good” party to do? If it sticks to its principles, it could end up ceding victory to the “bad” party, which will do even more harm once it is in office. A “good” party may thus try to forestall that outcome by taking a step down the moral ladder, precipitating a race to the bottom. This is the problem with any winner-takes-all game. When second place confers no benefits, the cost of showing unilateral restraint can grow intolerably high.

But this problem is not as hopeless as it appears. In light of today’s crisis of democracy, we would do well to revisit Václav Havel’s seminal 1978 essay “The Power of the Powerless.” First published as samizdat that was smuggled out of Czechoslovakia, the essay makes a simple but compelling argument. Dictatorships and other seemingly omnipotent forms of authoritarianism may look like large, top-down structures, but in the final analysis, they are merely the outcome of ordinary individuals’ beliefs and choices. Havel did not have the tools of modern economic theory to demonstrate his argument formally. In my new book The Republic of Beliefs, I show that the essence of his argument can be given formal structure using elementary game theory. This, in turn, shows that ordinary individuals have moral options that may be unavailable to the big institutional players….(More)”.

An Overview of National AI Strategies

Curated on Posted on by Stefaan Verhulst

Medium Article by Tim Dutton: “The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, China, Denmark, the EU Commission, Finland, France, India, Italy, Japan, Mexico, the Nordic-Baltic region, Singapore, South Korea, Sweden, Taiwan, the UAE, and the UK have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure.

This article summarizes the key policies and goals of each strategy, as well as related policies and initiatives that have announced since the release of the initial strategies. It also includes countries that have announced their intention to develop a strategy or have related AI policies in place….(More)”.

The Risks of Dangerous Dashboards in Basic Education

Curated on Posted on by Stefaan Verhulst

Lant Pritchett at the Center for Global Development: “On June 1, 2009 Air France flight 447 from Rio de Janeiro to Paris crashed into the Atlantic Ocean killing all 228 people on board. While the Airbus 330 was flying on auto-pilot, the different speed indicators received by the on-board navigation computers started to give conflicting speeds, almost certainly because the pitot tubes responsible for measuring air speed had iced over. Since the auto-pilot could not resolve conflicting signals and hence did not know how fast the plane was actually going, it turned control of the plane over to the two first officers (the captain was out of the cockpit). Subsequent flight simulator trials replicating the conditions of the flight conclude that had the pilots done nothing at all everyone would have lived—nothing was actually wrong; only the indicators were faulty, not the actual speed. But, tragically, the pilots didn’t do nothing….

What is the connection to education?

Many countries’ systems of basic education are in “stall” condition.

A recent paper of Beatty et al. (2018) uses information from the Indonesia Family Life Survey, a representative household survey that has been carried out in several waves with the same individuals since 2000 and contains information on whether individuals can answer simple arithmetic questions. Figure 1, showing the relationship between the level of schooling and the probability of answering a typical question correctly, has two shocking results.

First, the difference in the likelihood a person can answer a simple mathematics question correctly differs by only 20 percent between individuals who have completed less than primary school (<PS)—who can answer correctly (adjusted for guessing) about 20 percent of the time—and those who have completed senior secondary school or more (>=SSS), who answer correctly only about 40 percent of the time. These are simple multiple choice questions like whether 56/84 is the same fraction as (can be reduced to) 2/3, and whether 1/3-1/6 equals 1/6. This means that in an entire year of schooling, less than 2 additional children per 100 gain the ability to answer simple arithmetic questions.

Second, this incredibly poor performance in 2000 got worse by 2014. …

What has this got to do with education dashboards? The way large bureaucracies prefer to work is to specify process compliance and inputs and then measure those as a means of driving performance. This logistical mode of managing an organization works best when both process compliance and inputs are easily “observable” in the economist’s sense of easily verifiable, contractible, adjudicated. This leads to attention to processes and inputs that are “thin” in the Clifford Geertz sense (adopted by James Scott as his primary definition of how a “high modern” bureaucracy and hence the state “sees” the world). So in education one would specify easily-observable inputs like textbook availability, class size, school infrastructure. Even if one were talking about “quality” of schooling, a large bureaucracy would want this too reduced to “thin” indicators, like the fraction of teachers with a given type of formal degree, or process compliance measures, like whether teachers were hired based on some formal assessment.

Those involved in schooling can then become obsessed with their dashboards and the “thin” progress that is being tracked and easily ignore the loud warning signals saying: Stall!…(More)”.

Countries Can Learn from France’s Plan for Public Interest Data and AI

Curated on Posted on by Stefaan Verhulst

Nick Wallace at the Center for Data Innovation: “French President Emmanuel Macron recently endorsed a national AI strategy that includes plans for the French state to make public and private sector datasets available for reuse by others in applications of artificial intelligence (AI) that serve the public interest, such as for healthcare or environmental protection. Although this strategy fails to set out how the French government should promote widespread use of AI throughout the economy, it will nevertheless give a boost to AI in some areas, particularly public services. Furthermore, the plan for promoting the wider reuse of datasets, particularly in areas where the government already calls most of the shots, is a practical idea that other countries should consider as they develop their own comprehensive AI strategies.

The French strategy, drafted by mathematician and Member of Parliament Cédric Villani, calls for legislation to mandate repurposing both public and private sector data, including personal data, to enable public-interest uses of AI by government or others, depending on the sensitivity of the data. For example, public health services could use data generated by Internet of Things (IoT) devices to help doctors better treat and diagnose patients. Researchers could use data captured by motorway CCTV to train driverless cars. Energy distributors could manage peaks and troughs in demand using data from smart meters.

Repurposed data held by private companies could be made publicly available, shared with other companies, or processed securely by the public sector, depending on the extent to which sharing the data presents privacy risks or undermines competition. The report suggests that the government would not require companies to share data publicly when doing so would impact legitimate business interests, nor would it require that any personal data be made public. Instead, Dr. Villani argues that, if wider data sharing would do unreasonable damage to a company’s commercial interests, it may be appropriate to only give public authorities access to the data. But where the stakes are lower, companies could be required to share the data more widely, to maximize reuse. Villani rightly argues that it is virtually impossible to come up with generalizable rules for how data should be shared that would work across all sectors. Instead, he argues for a sector-specific approach to determining how and when data should be shared.

After making the case for state-mandated repurposing of data, the report goes on to highlight four key sectors as priorities: health, transport, the environment, and defense. Since these all have clear implications for the public interest, France can create national laws authorizing extensive repurposing of personal data without violating the General Data Protection Regulation (GDPR) which allows national laws that permit the repurposing of personal data where it serves the public interest. The French strategy is the first clear effort by an EU member state to proactively use this clause in aid of national efforts to bolster AI….(More)”.

China’s Aggressive Surveillance Technology Will Spread Beyond Its Borders

Curated on Posted on by Stefaan Verhulst

Daniel Benaim and Hollie Russon Gilman at Slate: “…What happens when these technologies migrate to bigger, more fractious societies? This won’t happen overnight—and the financial and logistical obstacles to broad implementation are significant. But there is every reason to think that in a decade or two, if not sooner, authoritarians and would-be strongmen in places like Turkey, Hungary, Egypt, or Rwanda will seek these tools and use them to thwart civil society and crush dissent in ways that weaken democracy globally.

Already there are reports that Zimbabwe, for example, is turning to Chinese firms to implement nationwide facial-recognition and surveillance programs, wrapped into China’s infrastructure investments and a larger set of security agreements as well, including for policing online communication. The acquisition of black African faces will help China’s tech sector improve its overall data set.

Malaysia, too, announced new partnerships this spring with China to equip police with wearable facial-recognition cameras. There are quiet reports of Arab Gulf countries turning to China not just for the drone technologies America has denied but also for the authoritarian suite of surveillance, recognition, and data tools perfected in China’s provinces. In a recent article on Egypt’s military-led efforts to build a new capital city beyond Cairo’s chaos and revolutionary squares, a retired general acting as project spokesman declared, “a smart city means a safe city, with cameras and sensors everywhere. There will be a command center to control the entire city.” Who is financing construction? China.

While many governments are making attempts to secure this information, there have been several alarming stories of data leaks. Moreover, these national identifiers create an unprecedented opportunity for state surveillance at scale. What about collecting biometric information in nondemocratic regimes? In 2016, the personal details of nearly 50 million people in Turkey were leaked….

China and other determined authoritarian states may prove undeterrable in their zeal to adopt repressive technologies. A more realistic goal, as Georgetown University scholar Nicholas Wright has argued, is to sway countries on the fence by pointing out the reputational costs of repression and supporting those who are advocating for civil liberties in this domain within their own countries. Democracy promoters (which we hope will one day again include the White House) will also want to recognize the coming changes to the authoritarian public sphere. They can start now in helping vulnerable populations and civil society to gain greater technological literacy to advocate for their rights in new domains. It is not too early for governments and civil society groups alike to study what technological and tactical countermeasures exist to circumvent and disrupt new authoritarian tools.

Seven years ago, techno-optimists expressed hope that a wave of new digital tools for social networking and self-expression could help young people in the Middle East and elsewhere to find their voices. Today, a new wave of Chinese-led technological advances threatens to blossom into what we consider an “Arab spring in reverse”—in which the next digital wave shifts the pendulum back, enabling state domination and repression at a staggering scale and algorithmic effectiveness.

Americans are absolutely right to be urgently focused on countering Russian weaponized hacking and leaking as its primary beneficiary sits in the Oval Office. But we also need to be more proactive in countering the tools of algorithmic authoritarianism that will shape the worldwide future of individual freedom….(More)”.

Buzzwords and tortuous impact studies won’t fix a broken aid system

Curated on Posted on by Stefaan Verhulst

The Guardian: “Fifteen leading economists, including three Nobel winners, argue that the many billions of dollars spent on aid can do little to alleviate poverty while we fail to tackle its root causes….Donors increasingly want to see more impact for their money, practitioners are searching for ways to make their projects more effective, and politicians want more financial accountability behind aid budgets. One popular option has been to audit projects for results. The argument is that assessing “aid effectiveness” – a buzzword now ubiquitous in the UK’s Department for International Development – will help decide what to focus on.

Some go so far as to insist that development interventions should be subjected to the same kind of randomised control trials used in medicine, with “treatment” groups assessed against control groups. Such trials are being rolled out to evaluate the impact of a wide variety of projects – everything from water purification tablets to microcredit schemes, financial literacy classes to teachers’ performance bonuses.

Economist Esther Duflo at MIT’s Poverty Action Lab recently argued in Le Monde that France should adopt clinical trials as a guiding principle for its aid budget, which has grown significantly under the Macron administration.

But truly random sampling with blinded subjects is almost impossible in human communities without creating scenarios so abstract as to tell us little about the real world. And trials are expensive to carry out, and fraught with ethical challenges – especially when it comes to health-related interventions. (Who gets the treatment and who doesn’t?)

But the real problem with the “aid effectiveness” craze is that it narrows our focus down to micro-interventions at a local level that yield results that can be observed in the short term. At first glance this approach might seem reasonable and even beguiling. But it tends to ignore the broader macroeconomic, political and institutional drivers of impoverishment and underdevelopment. Aid projects might yield satisfying micro-results, but they generally do little to change the systems that produce the problems in the first place. What we need instead is to tackle the real root causes of poverty, inequality and climate change….(More)”.

A roadmap for restoring trust in Big Data

Curated on Posted on by Stefaan Verhulst
Mark Lawler et al in the Lancet: “The fallout from the Cambridge Analytica–Facebook scandal marks a significant inflection point in the public’s trust concerning Big Data. The health-science community must use this crisis-in-confidence to redouble its commitment to talk openly and transparently about benefits and risks and to act decisively to deliver robust effective governance frameworks, under which personal health data can be responsibly used. Activities such as the Innovative Medicines Initiative’s Big Data for Better Outcomes emphasise how a more granular data-driven understanding of human diseases including cancer could underpin innovative therapeutic intervention.
 Health Data Research UK is developing national research expertise and infrastructure to maximise the value of health data science for the National Health Service and ultimately British citizens.
Comprehensive data analytics are crucial to national programmes such as the US Cancer Moonshot, the UK’s 100 000 Genomes project, and other national genomics programmes. Cancer Core Europe, a research partnership between seven leading European oncology centres, has personal data sharing at its core. The Global Alliance for Genomics and Health recently highlighted the need for a global cancer knowledge network to drive evidence-based solutions for a disease that kills more than 8·7 million citizens annually worldwide. These activities risk being fatally undermined by the recent data-harvesting controversy.
We need to restore the public’s trust in data science and emphasise its positive contribution in addressing global health and societal challenges. An opportunity to affirm the value of data science in Europe was afforded by Digital Day 2018, which took place on April 10, 2018, in Brussels, and where European Health Ministers signed a declaration of support to link existing or future genomic databanks across the EU, through the Million European Genomes Alliance.
So how do we address evolving challenges in analysis, sharing, and storage of information, ensure transparency and confidentiality, and restore public trust? We must articulate a clear Social Contract, where citizens (as data donors) are at the heart of decision-making. We need to demonstrate integrity, honesty, and transparency as to what happens to data and what level of control people can, or cannot, expect. We must embed ethical rigour in all our data-driven processes. The Framework for Responsible Sharing of Genomic and Health Related Data represents a practical global approach, promoting effective and ethical sharing and use of research or patient data, while safeguarding individual privacy through secure and accountable data transfer…(More)”.

Satellites can advance sustainable development by highlighting poverty

Curated on Posted on by Stefaan Verhulst

Cordis: “Estimating poverty is crucial for improving policymaking and advancing the sustainability of a society. Traditional poverty estimation methods such as household surveys and census data incur huge costs however, creating a need for more efficient approaches.

With this in mind, the EU-funded USES project examined how satellite images could be used to estimate household-level poverty in rural regions of developing countries. “This promises to be a radically more cost-effective way of monitoring and evaluating the Sustainable Development Goals,” says Dr Gary Watmough, USES collaborator and Interdisciplinary Lecturer in Land Use and Socioecological Systems at the University of Edinburgh, United Kingdom.

Land use and land cover reveal poverty clues

To achieve its aims, the project investigated how land use and land cover information from satellite data could be linked with household survey data. “We looked particularly at how households use the landscape in the local area for agriculture and other purposes such as collecting firewood and using open areas for grazing cattle,” explains Dr Watmough.

The work also involved examining satellite images to determine which types of land use were related to household wealth or poverty using statistical analysis. “By trying to predict household poverty using the land use data we could see which land use variables were most related to the household wealth in the area,” adds Dr Watmough.

Overall, the USES project found that satellite data could predict poverty particularly the poorest households in the area. Dr Watmough comments: “This is quite remarkable given that we are trying to predict complicated household-level poverty from a simple land use map derived from high-resolution satellite data.”

A study conducted by USES in Kenya found that the most important remotely sensed variable was building size within the homestead. Buildings less than 140 m2 were mostly associated with poorer households, whereas those over 140 m2 tended to be wealthier. The amount of bare ground in agricultural fields and within the homestead region was also important. “We also found that poorer households were associated with a shorter number of agricultural growing days,” says Dr Watmough….(More)”.