Governmentsā€™ use of automated decision-making systems reflects systemic issues of injustice andĀ inequality

By Joanna Redden, Associate Professor, Information and Media Studies, Western University, Canada

In 2019, former UN Special Rapporteur Philip Alston said he was worried we were ā€œstumbling zombie-like into a digital welfare dystopia.ā€ He had been researching how government agencies around the world were turning to automated decision-making systems (ADS) to cut costs, increase efficiency and target resources. ADS are technical systems designed to help or replace human decision-making using algorithms.

Alston was worried for good reason. Research shows that ADS can be used in ways that discriminate, exacerbate inequality, infringe upon rights, sort people into different social groups, wrongly limit access to services and intensify surveillance.

For example, families have been bankrupted and forced into crises after being falsely accused of benefit fraud.

Researchers have identified how facial recognition systems and risk assessment tools are more likely to wrongly identify people with darker skin tones and women. These systems have already led to wrongful arrests and misinformed sentencing decisions.

Often, people only learn that they have been affected by an ADS application when one of two things happen: after things go wrong, as was the case with the A-levels scandal in the United Kingdom; or when controversies are made public, as was the case with uses of facial recognition technology in Canada and the United States.

Automated problems

Greater transparency, responsibility, accountability and public involvement in the design and use of ADS is important to protect peopleā€™s rights and privacy. There are three main reasons for this:

  1. these systems can cause a lot of harm;
  2. they are being introduced faster than necessary protections can be implemented, and;
  3. there is a lack of opportunity for those affected to make democratic decisions about if they should be used and if so, how they should be used.

Our latest research project, Automating Public Services: Learning from Cancelled Systems, provides findings aimed at helping prevent harm and contribute to meaningful debate and action. The report provides the first comprehensive overview of systems being cancelled across western democracies.

Researching the factors and rationales leading to cancellation of ADS systems helps us better understand their limits. In our report, we identified 61 ADS that were cancelled across Australia, Canada, Europe, New Zealand and the U.S. We present a detailed account of systems cancelled in the areas of fraud detection, child welfare and policing. Our findings demonstrate the importance of careful consideration and concern for equity.

Reasons for cancellation

There are a range of factors that influence decisions to cancel the uses of ADS. One of our most important findings is how often systems are cancelled because they are not as effective as expected. Another key finding is the significant role played by community mobilization and research, investigative reporting and legal action.

Our findings demonstrate there are competing understandings, visions and politics surrounding the use of ADS.

a table showing the factors influencing the decision to cancel and ADS system
There are a range of factors that influence decisions to cancel the uses of ADS systems. (Data Justice Lab), Author provided

Hopefully, our recommendations will lead to increased civic participation and improved oversight, accountability and harm prevention.

In the report, we point to widespread calls for governments to establish resourced ADS registers as a basic first step to greater transparency. Some countries such as the U.K., have stated plans to do so, while other countries like Canada have yet to move in this direction.

Our findings demonstrate that the use of ADS can lead to greater inequality and systemic injustice. This reinforces the need to be alert to how the use of ADS can create differential systems of advantage and disadvantage.

Accountability and transparency

ADS need to be developed with care and responsibility by meaningfully engaging with affected communities. There can be harmful consequences when government agencies do not engage the public in discussions about the appropriate use of ADS before implementation.

This engagement should include the option for community members to decide areas where they do not want ADS to be used. Examples of good government practice can include taking the time to ensure independent expert reviews and impact assessments that focus on equality and human rights are carried out.

a list of recommendations for governments using ADS systems
Governments can take several different approaches to implement ADS systems in a more accountable manner. (Data Justice Lab), Author provided

We recommend strengthening accountability for those wanting to implement ADS by requiring proof of accuracy, effectiveness and safety, as well as reviews of legality. At minimum, people should be able to find out if an ADS has used their data and, if necessary, have access to resources to challenge and redress wrong assessments.

There are a number of cases listed in our report where government agenciesā€™ partnership with private companies to provide ADS services has presented problems. In one case, a government agency decided not to use a bail-setting system because the proprietary nature of the system meant that defendants and officials would not be able to understand why a decision was made, making an effective challenge impossible.

Government agencies need to have the resources and skills to thoroughly examine how they procure ADS systems.

A politics of care

All of these recommendations point to the importance of a politics of care. This requires those wanting to implement ADS to appreciate the complexities of people, communities and their rights.

Key questions need to be asked about how the uses of ADS lead to blind spots because of the way they increase the distancing between administrators and the people they are meant to serve through scoring and sorting systems that oversimplify, infer guilt, wrongly target and stereotype people through categorizations and quantifications.

Good practice, in terms of a politics of care, involves taking the time to carefully consider the potential impacts of ADS before implementation and being responsive to criticism, ensuring ongoing oversight and review, and seeking independent and community review.

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.

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.  

Problem-solving for Problem-solving: Data Analytics to Identify Families for Service Intervention

Presentation British Sociological Association Annual Conference 2021

Project PI Ros Edwards presented findings from our project at the British Sociological Association annual conference 2021.

The paper, Problem-solving for Problem-solving: Data Analytics to Identify Families for Service Intervention looks at the way that the promise of technological fixes in the family policy field has set up a set of dependencies between public services and data analytic companies, entrenching a focus on individual families as the source of social problems rather than social conditions.

Watch Ros’ presentation.

The paper that was the basis of Ros’s presentation has now been published in Critical Social Policy

Would you like to take part in our research?

We are looking for parents to take part in the project. We want to know your views and experiences of the way that information about families is collected and used by policy-makers and service providers. 

There are two ways you can take part:

  1. As part of a group discussion – If you are a parent (of at least one child aged 16 or under) you can take part in an online group discussion that will last about 45 minutes.
  2. In a one-to-one discussion -If you are a parent (of at a least one child aged 16 or under) and have had contact with family support services (this may be childrenā€™s social work services, early years or a voluntary organisation that supports families) you can take part in an individual discussion with us that will last about 45 minutes, either online or by phone.

All group and individual participants will receive a Ā£25 e-voucher in thanks for their time and trouble, and we can provide a top-up voucher for participants using pay-as-you-go.

The research has ethical approval from the University of Southampton.

If you would like to receive further information or talk about the possibility of participating in the research, please contact Sarah Gorin, University of Southampton at s.j.gorin@soton.ac.uk