Tag: data linkage

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Drawing parallels – the processing of data about children in education and social care

By Sarah Gorin, Ros Edwards and Val Gillies

During our research, we have been learning more about the ways that Government agencies such as health, social care and education collect, process and join up information about families. Schools, like other Government agencies collect and process an increasing volume of information about children. Data is collected for administrative purposes, such as: monitoring attendance, attainment, progress and performance; for safeguarding children; and to promote and support education and learning.

Information about children is not only captured by the school for their own and purposes determined by the Government, but also by private educational technology (EdTech) companies who gather data on children via their use of apps, that may be free to download, and recommended by teachers as promoting learning. These companies may sell on information for marketing or research purposes. Since the pandemic the use of EdTech has grown exponentially, meaning the data being gathered on children both through schools and by EdTech providers is greater still, raising the stakes in terms of the protection of childrenā€™s personal data.

A new report by The Digital Futures Commission (DFC) ā€˜Education Data Reality: The challenges for schools in managing childrenā€™s education dataā€™ examines the views of professionals who work in or with schools on the procurement of, data protection for, or uses of digital technologies in schools. The report describes the range of EdTech used in schools and the complex issues that managing it presents.

In a blog about the report, the main author Sarah Turner highlights four key issues that constrain childrenā€™s best interests:

  • The benefits of EdTech and the data processed from children in schools are currently not discernible or in childrenā€™s best interests. Nor are they proportionate to the scope, scale and sensitivity of data currently processed from children in schools.
  • Schools have limited control or oversight over data processed from children through their uses of EdTech. The power imbalance between EdTech providers and schools is structured in the terms of the use they signed up to and exacerbated by external pressure to use some EdTech services.
  • There is a distinct lack of comprehensive guidance for schools on how to manage EdTech providersā€™ data practices. Nor is there a minimum standard for acceptable features, data practices and evidence-based benefits for schools to navigate the currently fragmented EdTech market and select appropriate EdTech that offers educational benefits proportionate to the data it processes.
  • Patchy access to and security of digital devices at school and home due to cost and resource barriers means that access to digital technologies to deliver and receive education remains inequitable.

The report is focused on the processing of education data about families, however there are many interesting parallels with the findings from our project on the way data about families is collected, processed and used by local authorities:

  • Firstly, there is a lack of evidence about the benefits of the use of digital technologies in both schools and in local authorities and a lack of understanding about the risks to childrenā€™s data privacy.
  • There is a lack of government guidance for schools as there is for local authorities about the digital technologies that they employ, meaning that organisations are left individually responsible for ensuring that they are compliant with General Data Protection Regulation (GPPR).
  • Schools, like local authorities are time, resource and expertise poor. Often neither have the data protection expertise to understand and consider the risks versus the benefits of data processing for childrenā€™s best interests.
  • There is a lack of transparency in how data is collected, handled and processed by Government agencies as well as third parties who gain access to data about families, either through children using their apps for educational purposes or through local authorities employing them for the development of predictive analytics systems.
  • Public awareness and understanding about how data is collected and processed and the risks of data sharing to childrenā€™s privacy are low and are not well understood by parents and children.

We welcome this new report by the Digital Futures Commission and hope that it stimulates more discussion and awareness amongst professionals and families.

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Childrenā€™s visibility, vulnerability and voice in official statistics and their use

By Sarah Gorin, Ros Edwards and Val Gillies

Throughout our project we have been looking at parental social licence for the linking together of Government data about familiesā€™ lives across areas such as health, education and social care. Whilst our research focus has been on parents, it is also important we listen to childrenā€™s views. A vast amount of data is collected about children across Government and non-Government agencies, yet it would seem children and young people are rarely asked what they consider to be acceptable uses of their personal information. It is important that children are given this opportunity, under Article 12 of the UN Convention on the Rights of the Child, that requires that childrenā€™s views should be heard and considered on all matters that affect them.

 A recent report ā€˜Visibility, Vulnerability and Voiceā€™ by The Office for Statistics Regulation (an independent body that regulates the use of official statistics) has drawn attention to the importance of including children and young people in official statistics.

The report provides a framework for considering the needs of children and young people in the development of official statistics that they have named the ā€˜3Vā€™sā€™ framework and suggests seeing statistics about children and young people with 3 lenses: that of ā€˜Visibilityā€™, making statistics on children and young people available; ā€˜Vulnerabilityā€™, ensuring collection and analysis of data about children who are vulnerable to poorer outcomes and ā€˜Voiceā€™, ensuring statistics reflect the views of children and young people and they are given a voice in how their data is used.

In considering childrenā€™s ā€˜Voiceā€™ the Office for Statistics Regulation reflect that all official statistics producers should:

  • Seek the views of children and young people themselves rather than relying on proxies from adults.
  • Consider, and respond to, the data needs of children and young people.
  • Involve children and young people in the development of statistics for and about them.
  • Ensure children and young people have a voice around how their data are used in official statistics and in research using the data underpinning them.

Whilst the report focuses on the need to involve children and young people in the development of official statistics, the same also applies more broadly to the development of policy around the use of data. A report by DigitalDefendMe,ā€˜The Words We Use in Data Policyā€™ considers the way children are framed in data policy and the lack of representation or engagement with children about their views. We welcome these reports and the focus and commitment to improving opportunities for children and young people to be involved in developments in the way their data is linked together and used.

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Using Artificial Intelligence in public services – does it breach peopleā€™s privacy?

By Ros Edwards, Sarah Gorin and Val Gillies

As part of our research, we recently asked parents what they thought about the use of data linkage and predictive analytics to identify families to target public services.

They told us that they didnā€™t trust these processes. This was particularly the case among marginalised social groups. In other words, the groups of parents who are most likely to be the focus of these AI identification practices are least likely to see them as legitimate. Now a new report by the United Nations High Commissioner of Human Rights, Michelle Bachelet highlights major concerns about the impact of artificial intelligence, including profiling, automated decision-making and machine-learning, upon individualsā€™ right to privacy. 

The report makes a number of recommendations, including a moratorium on the use of AI systems that pose a serious risk to human rights and the banning of social scoring of individuals by Governments or AI systems that categorise individuals into groups on discriminatory grounds.

The right to privacy in the digital age: report (2021) builds on two previous reports by the High Commissioner looking at the right to privacy in the digital age and incorporates views of international experts at a virtual seminar, as well as responses to the High Commissioners call for input into the report from member states, including the U.K.

It examines the impact of digital systems such as artificial intelligence in four sectors, including public services. Artificial intelligence is used in public services such as social care, health, police, social security and education in a range of ways, such as decision-making about welfare benefits and flagging families for visits by childrenā€™s social care services.

Concerns are expressed about the linking together for example of large health, education and social care data sets with other data held by private companies, such as social media companies or data brokers who, the report says, may gather information outside protective legal frameworks. The involvement of private companies in the construction, development and management of public sector data systems, also means they can gain access to data sets containing information about large parts of the population.

There are additional concerns about the potential inaccuracy of  historic data and the implications of that for future decision-making. The report states that these systems unequally ā€œexpose, survey and punish welfare beneficiariesā€ and that conditions are imposed on individuals that can undermine their autonomy and choice.

A digital welfare fraud detection system was banned by a court in the Netherlands, ruling that it infringed individualsā€™ right to privacy. The system provided central and local authorities with the power to share and analyse data that were previously kept separately, including on employment, housing, education, benefits and health insurance, as well as other forms of identifiable data. The tool targeted low-income and minority neighbourhoods, leading to de facto discrimination based on socioeconomic background.

The recommendations in the report include:

  • using a human rights based approach
  • ensuring legislation and regulation are in line with the risk to human rights, with sectors including social protection to be prioritised
  • development of sector specific regulation requirements
  • drastic improvements to efforts regarding transparency, including use of registers for AI that contain key information about AI tools and their use, informing affected individuals when decisions are being or have been made automatically or with the help of automation tools, and notifying individuals when the personal data they provide will become part of a data set used by an AI system.

With concerns about the risks to the human rights of individuals and families about the use of data linkage and predictive analytics, it is vital to pay heed to the UN High Commissionerā€™s call for a moratorium. Public authorities need to pay meaningful attention to the lack of social legitimacy for AI, as evidenced in our research, and to ask themselves if the risk of further distrust and disengagement from already marginalised social groups, and consequences for a cohesive and equal society, is worth it. 

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Question marks over data analytics for family intervention

by Ros Edwards, Sarah Gorin and Val Gillies

The National Data Strategy encourages the UKā€™s central and local government to team up with the private sector to digitally share and join up records to inform and improve services. One example of this is the area of troublesome families, where itā€™s thought that the use of merged records and algorithms can help spot or pre-empt issues by intervening early. But there are questions over this approach and this is something our project has been looking into. In our first published journal article, we have been examining the rationales presented by the parties behind data analytics used in this context to see if they really do present solutions. Ā 

The application of algorithmic tools is a form of technological solution; based on indicators in the routinely collected data, in an effort to draw out profiles, patterns and predictions that enable services to target and fix troublesome families.  But local authorities often need to turn to commercial data analytic companies to build the required digital systems and algorithms.

In our paper we analysed national and local government reports and statements, and the websites of data analytic companies, addressing data linkage and analytics in the family intervention field.  We looked in particular at rationales for and against data integration and analytics.  We use a ā€˜problem-solvingā€™ analytic approach, which focuses on how issues are produced as particular sorts of problems that demand certain sorts of solutions to fix them.  This helps us to identify a double-faceted chain of problems and solutions.  

Seeking and targeting families

Families in need of intervention and costing public money are identified as a social problem and local authorities given the responsibility of fixing that problem. Local authorities need to seek out and target these families for intervention. And it is experts in data analytics that, in turn, will solve that identification problem for them.  In turn companies are reliant on citizens being turned into data (datafied) by local authorities and other public services.

We identified three main sorts of rationales in the data analytic companies promotion of their products that will solve local authoritiesā€™ problems: the power of superior knowledge, harnessing time, and economic efficiency.

Companies promote their automated data analytics products as powerful and transformational.  They hand control of superior, objective and accurate, knowledge to local authorities so that they can use profiling criteria to identify families where there are hidden risks, for intervention.  And their systems help local authority services such as social care and education collaborate with other services like health and the police, through data sharing and integration.

Data analytics is presented as harnessing time in the service of local authorities as an early warning system that enables them quickly to identify families as problems arise.  It is the provision of an holistic view based on existing past records that local authorities hold about families, and the inputting of ā€˜real timeā€™ present administrative data on families as it comes in.  In turn, this provides foresight, helping local authorities into the future ā€“ predicting which families are likely to become risks in advance and acting to pre-empt this, planning ahead using accurate information.  

Another key selling point for data analytics companies is that their products allow economic efficiency.  Local authorities will know how much families cost them, and can make assured decisions about where to put or withdraw resources of finances and staffing.  Data analytic products produce data trails that cater for local authorities to prepare Government returns and respond to future central Government payment-by-results initiatives, maximising the income that can be secured for their constrained budgets.

Questions to be asked

But there are questions to be asked about whether or not data linkage and analytics does provide powerful and efficient solutions, which we consider in our article.  Concerns have been raised about the errors and bias in administrative records, resulting in unfair targeting of certain families. 

Particular groups of parents and families are disproportionately represented in social security, social care and criminal justice systems, leading to existing social divisions of class, race and gender built into the data sets.  For example, there is evidence that racial and gender profiling discriminations are built into the data, such as the inclusion of young Black men who have never been in trouble in the Metropolitan Police Gangs Matrix.  And automated modelling equates socio-economic disadvantage with risk of child maltreatment, meaning that families are more likely to be identified for early intervention just because they are poor.  On top of that, studies drawing on longitudinal data are showing that the success rates of predictive systems are worryingly low. 

All of which raise a more fundamental question of whether or not algorithms should be built and implemented for services that intervene in familiesā€™ lives.  In the next stage of our research, we will be asking parents about their views on this and on the way that information about families is collected and used by policy-makers and service providers.