Generating transparency where none exists: just how are data analytics used in children’s services?

By Val Gillies, Ros Edwards and Sarah Gorin 

The Government’s public consultation on changes to the data protection framework emphasise the importance of public trust and transparency. But when, as part of our research, we tried to establish basic facts about the extent to which local authorities are linking and analysing data on children and families, we hit a brick wall.

Our research is aiming to provide a clearer picture of what parents think about the ways information about them and their children may be linked together and used by local councils. An important part of this work has been speaking directly to parents to see how much support for and trust in this type of activity there is. Alongside these valuable and enlightening conversations, we have also been trying to map the state of play among British local authorities and to find out exactly which authorities are doing what when it comes to operational data linking and matching and the application of predictive analytics to families’ data. 

The Government’s declared commitment is to roll out a ‘world class nationwide digital infrastructure and  ‘unlock the power of data’, but there is currently no central record available of which authorities are doing what. 

Freedom of information requests

To try to find out, we submitted Freedom of Information requests to 220 UK Local Authorities in the UK.  The 149 English councils participating in the ‘Troubled Families Programme’ (now called the Supporting Families Programme) must, by necessity link and analyse datasets to ‘identify’ ‘troubled’ households and claim payment-by-results from central government. Yet only 76 responded that they used data analytics. The remainder claimed that their systems did not meet our definition or responded with a straight ‘no’ to all our questions about their use. 

English councils claiming to be outside our criteria responded in a vague and evasive way. For example, some responded ‘no’ when asked about their engagement with data analytics either by positioning family work as separate from children’s services or by using the term ‘data matching’ instead. Further investigation established that many of these councils do in fact use systems with predictive analytic capacity. 

For example, Achieving for Children, a social enterprise providing services for children in several local authorities, responded to our FoI that beyond ‘some basic data monitoring….we do not currently use nor have we previously used any data analytics, predictive analytics and or artificial intelligence systems to assist with this work’. Yet they have used business intelligence technologies on a range of projects using predictive analytics/algorithms, as noted on the UK Authority Digital Data and Technology for the Public Good website.

Side-stepping terms

Our FoI research also showed that Councils side-stepped the term algorithm and the concept of AI. Even where they engaged in predictive analytics they denied they were using algorithms – it’s hard to envisage one without the other given the tools they were employing. 

We received a lot of incomplete and insufficient information and information that was irrelevant. A number of councils claimed exemption from the FoI on cost grounds or commercial confidentiality. Where we followed up with more carefully worded requests, we received ambiguously worded replies. 

Some local authorities were more forthcoming and open, listing various tools and companies used to conduct their data analytics.  Microsoft Business Intelligence the most common tool cited. Dorset County Council has a case study on the Local Government Association website of how the tool can be used to ‘ enable local professionals to identify potential difficulties for individual children before they become serious problems’. Our FoI established the council plans to make greater use of AI in the future. 

Our analysis of the responses we received and the information we have sourced from elsewhere, points to a clear shift in service priorities away from early intervention for parental education towards child protection and crime prevention. Earlier focus on linking parenting skills to social mobility is now muted, with rationales for data innovation focusing almost exclusively on the pre-emption of problems rather than on the maximisation of children’s future potential. 

Our findings around children’s services have been reinforced by work by Big Brother Watch which has published a detailed analysis of the use of hidden algorithms by councils who use trackers to identify disadvantaged households in order to target them for interventions. The organisation found one of the biggest players in this area, Xantura, to be particularly secretive. 

Public trust 

A wide range of data ‘solutions’ are drawn on by local authorities to classify, flag, target and intervene in disadvantaged families and their children. Yet parents are not generally informed of how their information is being used and for what purpose. As we have shown it is difficult even for us as researchers to establish.

From our work here, it is hard to see how public trust and transparency will be achieved from the opaque, ambiguous and even evasive base that our FoI request endeavours revealed. 

Freedom of Information Requests on the Use of Data Analytics in Childrens Services: Generating Transparency is research by Val Gillies and Bea Gardner, with Ros Edwards and Sarah Gorin. 

Using Artificial Intelligence in public services – does it breach people’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 didn’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 individuals’ 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 children’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 “expose, survey and punish welfare beneficiaries” 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 individuals’ 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 Commissioner’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. 

Question marks over data analytics for family intervention

by Ros Edwards, Sarah Gorin and Val Gillies

The National Data Strategy encourages the UK’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 it’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.  

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 ‘problem-solving’ 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 authorities’ 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 ‘real time’ present administrative data on families as it comes in.  In turn, this provides foresight, helping local authorities into the future – 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 families’ 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.  

Running focus groups with parents in a Covid-19 setting – how will we do it?

In this second project blog, the research team reflect on how Covid-19 and the restrictions it has placed on all our lives, has led to methodological, ethical and practical challenges in working with focus groups on parental buy-in for linking and analysing data about families. They outline the challenges they face and how they’re adapting their approach. 

For the next stage of our project, we’re conducting focus groups to explore how particular social groups of parents understand and talk about their perspectives on data linkage and predictive analytics.  Back in early 2020, we were optimistic about the possibility of being able to conduct these groups face-to-face by the time we reached this stage of our research.  Now though, it’s clear we’ll need to move online, and we’ve been thinking about the issues we’ll face and how to deal with them.

Questions we’re grappling with include:

  • What might moving online mean for how we recruit participants? 
  • How can we best organise groups and engage parents with the project? 
  • How can we develop content for online groups that will firstly, encourage parents to contribute and enjoy the research process, and secondly, be relevant to our research endeavour?

What will moving online mean for recruiting participants?

Our intention was – and still is, to hold focus group discussions with homogenous groups of parents, to explore the consensus of views on what is and isn’t acceptable (social licence) in joining together and using parents’ administrative records.

We’re using the findings from our earlier probability-based survey of parents to identify social groups of parents whose views stand out. These include home-owning parents in professional and managerial occupations, who have stronger social licence, and mothers on low incomes, Black parents, and lone parents and parents in larger families living in rented accommodation, who tend to have weak or no social licence.

Our original plan was to recruit participants for our focus groups by contacting local community and interest groups, neighbourhood networks, services such as health centres and schools, workplaces and professional associations.  We still plan to do this, but we’re concerned that the pandemic is placing huge pressures on community groups, services for families and businesses and we may need to be prepared that helping us to identify parents to participate in research may not be a priority or, as with schools, appropriate.

So we’ve also been considering recruitment through online routes, such as advertising on relevant Facebook groups; using Twitter and putting advertisements on websites likely to be accessed by parents. It’ll be interesting to see if these general reach-outs get us anywhere.

An important aspect of recruitment to our study is how to include marginalised parents.  This can be a conundrum whether research is face-to-face or online.  Face-to-face we would have spent quite a bit of time establishing trust in person, which is not feasible now.  Finding ways to reach out and convince these parents to participate is going to be an additional challenge. Our ideas for trying to engage these parents include the use of advertising via foodbanks, neighbourhood support networks and housing organisations.

And there’s the additional problem for online methods, revealed in inequalities of online schooling, of parents who have limited or no online access. Further, Covid-19 is affecting parents living in poverty especially and we don’t want to add to any stress they’re likely to be under.

Enticing affluent parents working in professional and managerial occupations to participate may also be difficult under the current circumstances.  They may be juggling full-time jobs and (currently) home schooling and feeling under pressure.  Either way, marginalised or affluent, we think we’ll need to be flexible, offering group times in evenings and at weekends for example. 

How should we change the way we organise groups and engage parents with the project? 

We know from reading the literature that online groups can face higher drop-out rates than face-to-face.  Will the pandemic and its potential effect on parent’s’ physical and mental health mean that we face even higher drop-out rates?  One strategy we hope will help is establishing personal links, through contacting participants and chatting to them informally before the focus group takes place.

We’ve been mulling over using groups containing people who know each other, for example if they’re members of a community group or accessed through a workplace, and groups that bring together participants who are unknown to each other.  Because we’re feeling a bit unsure about recruitment and organisation, we’ve decided to go down both routes as and when opportunities present themselves.  We’ll need to be aware of this as an issue when we come to do the analysis though.

We’re also thinking to organise more groups and have fewer participants in each group than we would have done face-to-face (after all, we’re not going to be confined by our original travel and venue hire budget).  Even in our online research team meetings we can cut across and interrupt each other, and discussion doesn’t flow in quite the same way.  Reading  participants’ body language and non-verbal cues in an online focus group is going to be more difficult.  Smaller numbers in the group may help a bit, but it can still be difficult to see everyone if, for example, someone is using a mobile phone.  We’ll just have to see how this goes and how best to handle it.

There’s also a dilemma about how many of the project team to involve in the focus groups. We’ll need to have a team member to facilitate the group, but previous research shows it might be useful to have at least one other to monitor the chat and sort out any technical issues. But with a group as small as 4-6 participants will that seem off putting for parents? It’s all hard to know so may be a case of trying it in order to find out!

What should we consider in developing content that’s engaging for parents and relevant to our research?

What we’ll miss by holding our group discussions online is the settling in and chatting and putting us and our participants at ease – how are you, would you like a drink, there’s some biscuits if you want, let me introduce you to … and so on.  We don’t think that we can replicate this easily.  

But we’ve been pondering our opening icebreaker – should we ask something like….

‘If you could be anywhere else in the world where would you be?’

or

‘What would be the one thing you’d pack in a lockdown survival kit?’ 

And we’re also planning to use a couple of initial questions that use the online poll function.  Here’s an instance where we think there’s an advantage over in-person groups, because participants can vote in the poll anonymously. 

After that, we’ll be attempting to open up the discussion to focus on the issues that are at the heart of our research – what our participants feel is acceptable and what’s not in various scenarios about the uses of data linkage and predictive analytics.

Ensuring the well-being of parents after focus groups is always important, but with online groups may be harder if the participants are not identified through community groups in which there’s already access to support. We plan to contact people after groups via email but it’s hard to know if parents would let us know even if groups presented issues for them. We have also given some thought to whether we could use online noticeboards for participants to post any further comments they may have about social licence after they’ve had time to reflect, but do not know realistically if they would be used.

It’ll be interesting to see if the concerns we’ve discussed here are borne out in practice, and our hopeful means of addressing them work.  And also, what sort of challenges arise for our online focus group discussions that we haven’t thought of in advance!

If you have any ideas that might help us with our focus groups, please do get in touch with us via datalinkingproject@gmail.com