Pre-problem families: predictive analytics and the future as the present

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Rosalind Edwards discusses how predictive analytics is seen as a way of identifying the risk of future problems in families.

Image courtesy of interviewee. March 27, 2024

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Ross Edwards discusses the implications of using digital administrative data records and predictive analytics in family welfare and child protection services. He explains that predictive analytics involves algorithmic analysis to identify which families may need intervention based on correlations between past behaviors and undesirable outcomes. This video video practice creates a "pre-pro space," where families are anticipated to face potential future problems even if they haven't occurred yet, resulting in a shift from reactive to proactive interventions.

The session highlights parents' views on predictive analytics, noting a lack of attention to their opinions and the ethical concerns this raises. Edward's research, conducted with colleagues, explores parents' assessments of data use in decision-making processes, revealing a mix of acceptance and concerns. While some parents see potential benefits in flagging individuals in distress, they also express strong apprehensions about the accuracy, fairness, and potential biases in the data used, particularly regarding racial disparities.

Parents expressed worry about the accuracy of records, the possibility of inaccurate predictions leading to unjust interventions, and concerns that families could be overlooked or wrongfully flagged. The presentation concludes with recommendations for clearer rights regarding personal data, mechanisms for reporting and correcting errors, and regular reviews of the predictive models' effectiveness. It emphasizes the need for public dialogue on the legitimacy of using administrative data to forecast family issues, advocating for a more ethical and transparent approach.

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Hi, my name is Ross Edwards

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and I'm gonna explain how the use

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of digital administrative data records

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and predictive analytics and family welfare

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and child protection services, how

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that's positioning families in a pre-pro space.

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I'm gonna talk about how parents themselves view this,

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and I'll be covering what predictive analytics is,

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why it's used, what a pre-pro space means.

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And I'm also gonna be telling you about parents' views

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and I'll say something about the implications for policy.

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So what is predictive analytics and why is family data

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and predictive analytics and important subject is

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because there's been a step change in the practice

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of assessing and monitoring families

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across many national contexts.

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Police and child protection authorities are turning

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to algorithmic analysis.

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They're doing that in an effort to forecast which

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families will need intervention, and that's

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because advocates for predictive data modeling argue

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that these algorithmic analysis enables streamlined,

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efficient, and objective decision making.

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Algorithms are a sequence of programmed instructions, rules

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and calculations.

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They're built so that they can model the tendency

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for correlations of particular characteristics

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to occur in families who have undesirable outcomes.

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Predictive surveillance takes place using algorithms,

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and it involves the collection

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and the linking together of separate sources

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of administrative data that's held on families

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and then that joined up.

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Data can be used

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to identify potential problem behaviors in the future

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through algorithmic processing digital sources

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of everyday administrative data on families from education,

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health benefits, housing, police, et cetera.

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They're all joined together

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and the modeling algorithm can automatically scan

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the merge data sets.

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It looks for families that match the patterns of factors

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that are indicated for problem families,

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and it does that in an effort to identify high, medium,

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or low risk of potential family problems.

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But it's not just an individual's family data that's used

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to forecast their own future actions.

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Data from all families is drawn into the predictive modeling

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net, net and it's other families behaviors

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that determine whether

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or not a family is deemed

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to pose a futurist to their children.

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So algorithms scan the merge data set looking

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for correlations that match patterns of factors in families

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where undesirable social

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and criminological outcomes have

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already happened in the past.

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And then this patterning is used to identify risk

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of harm occurring in other families in the future,

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and this creates what's called a pre

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Space for families.

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This sort of mass digital predictive monitoring

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that I've just described

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that involves a shift in timeframes.

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The established rationales

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for state intervention in families on the basis

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of what's happening is overturned.

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It becomes intervention in families on an anticipatory basis

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of what, what hasn't happened,

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but might sometime in the future.

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This is how families become pulled into a digital

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pre-pro space that's taking an imagined hypothetical future

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and treats it as if it's definite

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and parents are blameworthy

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and they're culpable in the present.

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The pre-pro space is a temporal paradox.

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It suggests simultaneously that something that hasn't

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yet occurred is a foregone conclusion.

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So that suppose future is bought into the present

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and acted upon.

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So how might parents feel about information on them

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and other families being used predictably?

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There's been very little attention

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to parents' views on this use of their administrative

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and other data services don't have to ask parents consent,

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so it's unlikely they know what's happening.

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My colleagues Val Giles and Sarah Gorin

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and I thought that was a serious ethical

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and democratic mission.

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So we researched parents' assessments

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and concerns about data analytic systems

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for service decision making,

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and we researched how comfortable they were about families

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being positioned in a predictive pre-pro space.

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We ran a representative survey and we also held group

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and individual discussions

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with parents about their views on predictive analytics.

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We did that as part of our research project,

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exploring parents' understandings

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of operational data linkage and use practices.

04:53.715 --> 04:56.695
The parents who took part in our research came from a

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diversity of social, racial and occupational backgrounds.

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Some parents had experienced family support

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and intervention services, but a lot of others haven't.

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For the most part, parents from all backgrounds

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and experience, they could see merit in algorithm

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applications to administrative data that

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because it could identify FA individual families

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with difficulties in the present

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who might mistreat their children.

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As one of 'em has said, I think if you're struggling,

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it's probably good to be flagged up

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because obviously the kids are probably at risk.

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But there was a recurrent

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and major theme of assertions

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that the past is not predictive.

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Here parents had major concerns that centered

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around accuracy and fairness, stereotyping and prejudice

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and determinism and its consequences.

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Parents didn't have a lot of confidence in the accuracy

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and fairness of the sources of administrative data

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that predictive analytics draws upon.

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They discussed examples of recorded information

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that was based on biased assumptions and judgmental views.

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Black parents had especially strong concerns about

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unjustified racialized labeling, parents and children,

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but either where information is flawed,

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data errors often stay on parents' administrative records.

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So has the potential for that biased information to be fed

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and reed endlessly into algorithmic data analysis

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and become part of a vicious cycle

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that magnifies in justices in predictive modeling.

06:37.585 --> 06:41.285
The issue of data analytics anticipating problems when in

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fact a family not only doesn't have the issues

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but wouldn't go on to develop them in the future.

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That's known as false positives and false positives.

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Worry parents, as one of them said,

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I think it's quite dangerous to use someone else's situation

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and try and predict someone else because you could be right,

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but you could be massively wrong.

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Parents were also bothered about families being passed over

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by predictive analytics

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because they didn't fit the data profile

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of dysfunctional families

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that the algorithm was working with.

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That's known as false negatives.

07:15.465 --> 07:18.435
Parents often mentioned affluent middle class families.

07:18.435 --> 07:22.665
In this respect, the idea of positioning parents

07:22.765 --> 07:24.825
and children in a pre-pro space

07:25.445 --> 07:27.465
and pulling together the past of the future

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into the present in a deterministic way

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that made parents quite indignant.

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They talked about the potential damage that could be caused

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by the surveillance and investigation

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and maybe children being removed from parents

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and taken interstate care.

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Some parents mentioned dystopian scenarios like terminated

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two stuff and big brother watching.

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They'd said that to convey their discomfort.

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But parents could also be skeptical about how families

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who are pulled into pre-pro space, were going

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to receive interventions in the face of austerity

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and diminish funding for service provision

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and staff shortages.

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In the UK and many other places,

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there have been severe cuts to public spending.

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It's one of the parents pointed out

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the reality is services have been cut till.

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There's nothing there to say that you are going to put

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that information into an algorithm to identify needs.

08:24.575 --> 08:26.975
When there are no services for them to access,

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there's no help they can have

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that feels really disingenuous.

08:33.085 --> 08:35.225
So what ought to happen about authorities use

08:35.445 --> 08:36.465
of predictive modeling

08:36.845 --> 08:38.625
and the pre-pro space, it creates,

08:39.925 --> 08:44.185
at the very least parents' apprehensions about the atri

08:44.205 --> 08:47.425
and prejudice data that can be fed into predictive models

08:48.315 --> 08:50.375
and their worries about the determinism

08:50.515 --> 08:52.455
and accuracy of predictive analytics

08:52.555 --> 08:54.575
and the very least that should be taken seriously.

08:55.785 --> 08:57.575
We've called for three main provisions.

08:58.785 --> 09:00.285
One is that there should be clear

09:00.505 --> 09:02.685
and enforceable rights for individuals

09:03.105 --> 09:06.725
to view the personal data that's held about them online.

09:07.225 --> 09:08.965
In administrative databases,

09:10.295 --> 09:14.195
two parents should have a proper system that allows them

09:14.395 --> 09:16.955
to report errors in the information held about them

09:17.465 --> 09:21.075
with speedy investigations and correction of data errors.

09:22.105 --> 09:25.435
Finally, there needs to be a regular independent review

09:25.695 --> 09:28.035
and publication of the accuracy

09:28.295 --> 09:30.555
and the utility of the predictive models

09:30.985 --> 09:33.315
that are being applied to administrative data.

09:35.065 --> 09:38.385
Ultimately though, we need a serious public conversation

09:38.445 --> 09:42.305
to take place about the legitimacy of state construction

09:42.605 --> 09:46.105
of a pre-pro space using administrative data

09:46.285 --> 09:47.585
that's held on families.

Understanding Predictive Analytics in Family Services

00:00:00 - Hi, my name is Ross Edwards and I'm gonna explain how the use of digital administrative data records and predictive analytics and family welfare and child protection services, how that's positioning families in a pre-pro space. I'm gonna talk about how parents themselves view this, and I'll be covering what predictive analytics is, why it's used, what a pre-pro space means. And I'm also gonna be telling you about parents' views and I'll say something about the implications for policy.
00:00:36 - and predictive analytics and important subject is because there's been a step change in the practice of assessing and monitoring families across many national contexts. Police and child protection authorities are turning to algorithmic analysis. They're doing that in an effort to forecast which families will need intervention, and that's because advocates for predictive data modeling argue that these algorithmic analysis enables streamlined,
00:01:10 - Algorithms are a sequence of programmed instructions, rules and calculations. They're built so that they can model the tendency for correlations of particular characteristics to occur in families who have undesirable outcomes. Predictive surveillance takes place using algorithms, and it involves the collection and the linking together of separate sources of administrative data that's held on families
00:01:39 - Data can be used to identify potential problem behaviors in the future through algorithmic processing digital sources of everyday administrative data on families from education, health benefits, housing, police, et cetera. They're all joined together and the modeling algorithm can automatically scan the merge data sets. It looks for families that match the patterns of factors that are indicated for problem families,
00:02:14 - or low risk of potential family problems. But it's not just an individual's family data that's used to forecast their own future actions. Data from all families is drawn into the predictive modeling net, net and it's other families behaviors that determine whether or not a family is deemed to pose a futurist to their children. So algorithms scan the merge data set looking
00:02:44 - where undesirable social and criminological outcomes have already happened in the past. And then this patterning is used to identify risk of harm occurring in other families in the future, and this creates what's called a pre Space for families. This sort of mass digital predictive monitoring that I've just described that involves a shift in timeframes. The established rationales for state intervention in families on the basis

Parents' Concerns in Predictive Analytics Use

00:03:14 - It becomes intervention in families on an anticipatory basis of what, what hasn't happened, but might sometime in the future. This is how families become pulled into a digital pre-pro space that's taking an imagined hypothetical future and treats it as if it's definite and parents are blameworthy and they're culpable in the present. The pre-pro space is a temporal paradox.
00:03:46 - yet occurred is a foregone conclusion. So that suppose future is bought into the present and acted upon. So how might parents feel about information on them and other families being used predictably? There's been very little attention to parents' views on this use of their administrative and other data services don't have to ask parents consent, so it's unlikely they know what's happening. My colleagues Val Giles and Sarah Gorin
00:04:17 - and democratic mission. So we researched parents' assessments and concerns about data analytic systems for service decision making, and we researched how comfortable they were about families being positioned in a predictive pre-pro space. We ran a representative survey and we also held group and individual discussions with parents about their views on predictive analytics. We did that as part of our research project,
00:04:48 - of operational data linkage and use practices. The parents who took part in our research came from a diversity of social, racial and occupational backgrounds. Some parents had experienced family support and intervention services, but a lot of others haven't. For the most part, parents from all backgrounds and experience, they could see merit in algorithm applications to administrative data that
00:05:20 - with difficulties in the present who might mistreat their children. As one of 'em has said, I think if you're struggling, it's probably good to be flagged up because obviously the kids are probably at risk. But there was a recurrent and major theme of assertions that the past is not predictive. Here parents had major concerns that centered around accuracy and fairness, stereotyping and prejudice and determinism and its consequences.
00:05:52 - and fairness of the sources of administrative data that predictive analytics draws upon. They discussed examples of recorded information that was based on biased assumptions and judgmental views. Black parents had especially strong concerns about unjustified racialized labeling, parents and children, but either where information is flawed, data errors often stay on parents' administrative records.

Concerns Over Predictive Analytics in Family Services

00:06:25 - and reed endlessly into algorithmic data analysis and become part of a vicious cycle that magnifies in justices in predictive modeling. The issue of data analytics anticipating problems when in fact a family not only doesn't have the issues but wouldn't go on to develop them in the future. That's known as false positives and false positives. Worry parents, as one of them said,
00:06:55 - and try and predict someone else because you could be right, but you could be massively wrong. Parents were also bothered about families being passed over by predictive analytics because they didn't fit the data profile of dysfunctional families that the algorithm was working with. That's known as false negatives. Parents often mentioned affluent middle class families. In this respect, the idea of positioning parents
00:07:25 - and pulling together the past of the future into the present in a deterministic way that made parents quite indignant. They talked about the potential damage that could be caused by the surveillance and investigation and maybe children being removed from parents and taken interstate care. Some parents mentioned dystopian scenarios like terminated two stuff and big brother watching. They'd said that to convey their discomfort. But parents could also be skeptical about how families
00:07:59 - to receive interventions in the face of austerity and diminish funding for service provision and staff shortages. In the UK and many other places, there have been severe cuts to public spending. It's one of the parents pointed out the reality is services have been cut till. There's nothing there to say that you are going to put that information into an algorithm to identify needs. When there are no services for them to access,
00:08:29 - that feels really disingenuous. So what ought to happen about authorities use of predictive modeling and the pre-pro space, it creates, at the very least parents' apprehensions about the atri and prejudice data that can be fed into predictive models and their worries about the determinism and accuracy of predictive analytics and the very least that should be taken seriously. We've called for three main provisions.
00:09:00 - and enforceable rights for individuals to view the personal data that's held about them online. In administrative databases, two parents should have a proper system that allows them to report errors in the information held about them with speedy investigations and correction of data errors. Finally, there needs to be a regular independent review and publication of the accuracy and the utility of the predictive models

Public Dialogue on State Data Use Needed

00:09:46 - Ultimately though, we need a serious public conversation to take place about the legitimacy of state construction of a pre-pro space using administrative data that's held on families.

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Pre-problem families: predictive analytics and the future as the present

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