We investigated France's mass profiling machine
lighthousereports.com
lighthousereports.com
The problem is that the CNAF deliberately targets small unintentional errors rather than large-scale intentional fraud.
Why that? Because fraud is harder to detect and to prove (you have to provide evidence it was intentional).
So smaller and poorer families are disproportionately affected by the controls, because the algorithm was designed to do so. No computational adjustment can fix that: it is the initial intent that is broken.
Source: https://www-laquadrature-net.translate.goog/2023/11/27/notat... (auto-translated in english)
The French welfare system is incredibly complex - see for instance this [0] simplified description of housing allowances which is 80 (!) pages long. This is not the most complex part of the system. With such a system, there are massive amounts of errors, both too-much-money-given and not-enough-money-given. The scale is so large that the French Court of Accounts refused to certify the CNAF accounts last year[1]: thoses errors represent about 7.5% of the CAF budget.
So basically the probability to have an error is just a function of how complex your situation is, and thus the "algorithm" targets more complex situations - change in your marital situation, having adult children (which may or may not need to be taken into account when applying for benefits depending on a bazillion variables), and so on, increases your probability to be targeted.
[0] https://www.ecologie.gouv.fr/sites/default/files/Brochure-ba...
[1] https://www.ccomptes.fr/fr/publications/certification-des-co...
France at least has a culture of constitutional reboots.
The more complex a system, the more skill and resources (ability, time, finances) it takes to navigate it.
Eventually, complexity serves to exclude everyone but people who are able to make a career out of pursuing benefits - who are also more likely to be fraudsters.
1. for any given budget, there is no Pareto-improving reallocation: if you want to give more money to someone, then the money has to come from someone else.
2. given the current complexity, there are a _lot_ of edge cases to account for. If you do not want to make any loser after a reform, and not have as many edge cases, then you'd need to pump a lot of money to a lot of people so that "edge case people" who become "average joe" do not lose out. See for instance the people who end up with less disposable income when their pension is raised (https://www.alternatives-economiques.fr/vrais-faux-gagnants-...) (!). See also the riffraff about the "montant net social" - now the exact income (which is basically net salary + a bunch of things your employer pay for you and are counted as income) you need to report on welfare application is written on pay slip. Nice simplification here, right? People who reported wrong income (only net salary generally) were upset that it was a plot to decrease welfare.
3. people genuinely genuinely love special cases. Hence the tradeoff for the government between adressing a special case but adding more complexity always end up with more special cases, more complexity.
Some examples: since housing is expensive, people want to help renters with cash payment (of course it can't be bundled with the basic income, it has to be its own benefit), but they also want some public housing with below market rents. Now you need to acocunt for the in kind benefit of having a below market rent in the rules of housing benefits if you want to be relatively just between those two populations.
Recently, the cash benefit for handicaped people computation was changed - it depends only on the receiver's income and not on household's income (the main argument was that household income as an input makes handicapped less autonomous on one hand, and decreases working incentives for them). Now this means that some benefits are computed at the individual level, other at the household level. Of course there is a transitory period where people can be grandfathered-in the old rules so as not to make any losers.
And so on and so forth.
Large families need their own benefits, because they have unique(tm) needs, you just can't make a per child benefit that just scale.
And so on and so forth.
Did I mention that you want to help overseas territories with special fiscal rules?
And so on and so forth.
I think the two most prominent examples of this were the two failed Macron reform: the first pension reform (universal public pension fund instead of several) and the basic income (revenu universel d'activité - basically a merger of APL+RSA+PA at least). Always a special category that lose out if they own complexity-inducing special case is ironed out.
4. because of points 1-4, no one understands anything and thus there is a strong suspicision that the government is here to rob you of [your benefits | your pension | etc] when there is a reform proposal.
This was explain to me by an accountant when I wondered why they wanted to have me fix what felt like I significant errors, mean while a big corporation was in the news for what was clearly tax fraud but the government was dragging they feet about it.
Corporate accounting is more complicated than family accounting. Even if the corporation isn't trying to do anything complicated!
Consequently, there are more edge cases and grey areas. As an accountant friend said to me, it's more like law than science -- knowing lots of laws and regulations, plus history, and deciding how to mostly correctly classify various things.
So something like trying to write the most efficient assembly algorithm possible, while Congress is modifying the ISA every year.
(Which isn't to say that loopholes don't exist, corporations don't abuse them, or corporate tax attorneys don't delay enforcement actions... but is to say that even in best case, family accounting is much simpler than corporate)
I don't think so in 2023. I worked several years in a tax agency and it was mainly a problem of "motivation". I have a friend who pursued "data warehouse" for 30 years there... nowadays you can crunch all the information and find patterns . I would even suggest that tax agencies should anonymize data and create data bounties to help them. In the same way DARPA creates cyberchallenges [1].
[1] https://www.darpa.mil/about-us/timeline/cyber-grand-challeng...
I assume when you're looking at forensic tax accounting, you're identifying present-year vs previous-year discrepancies?
Or is there enough required in filings to generate something like a complete shadow accounting for a company?
I think your observation is the greatest one: you don't target "Madoffs or SBFs" you just look at the low hanging fruit where you look at simple probabilistic causality A => B instead of, or at the same time, targetting big malicioous actors. Big and/or complex crimes and corruption are protected.
The irony is that many times the crimes commited by big actors could be simpler to analyze based on tax and financial information.
> The AI Incident Database is dedicated to indexing the collective history of harms or near harms realized in the real world by the deployment of artificial intelligence systems. Like similar databases in aviation and computer security, the AI Incident Database aims to learn from experience so we can prevent or mitigate bad outcomes.
Ie; if they detect you’ve made a mistake an and not claimed something you are eligible for. Or paid some tax that you are actually eligible to deduct/offset somehow, does it notify or automate the process of fixing that error?
I’m not opposed to the idea of automate to ensure compliance with the law / regulations. But enforcement should go both ways. The goal of automation of policy should be for the policy to be maximally effective. It shouldn’t just penalise those who incorrectly claim, but rather maximise eligible claims. And by that same measure, ensure those ineligible are rejected.
Obviously without any penalities, this would lead to over claiming, and reliance on the system to reject. But I think the system should be resilient enough to handle that.
While it’s not to such an extreme extent, there is a polar opposite attitude / policy towards entitlement of government benefits in Australia and New Zealand. In Australia you may be eligible for something, but it’s your responsibility to know that and claim it. And the government takes a ‘we defend the benefits from those who are ineligible’ attitude (more so if the Liberal party is in power, see Robo Debt scandal).
In New Zealand they take a ‘these benefits must go to those who are eligible’ attitude. And they model the extent to which they fulfil that objective based on how many claim vs how many their model states are eligible.
The outcome is that in NZ they call after you’ve had a baby to ensure you’re receiving additional tax benefits, services, etc. In Australia you’d only receive a call / letter to notify your benefits are being cut off, and there’s some difficult->impossible method of re-applying. Such as contacting a call centre that’s queue is full by 9:30 and stops accepting calls…
I see a lof of studies like this: »'Male drivers aged 18-25 leaving clubs at night' are a group that is overrepresented in police DUI (Driving Under the Influence of alcohol) checks.«
Stopping there is okay if you want to claim that any such targeting is wrongful. But the more useful information would be whether these groups are justly targeted.
The other part is how do you really measure efficacy of these systems? Unless you did some secret shopping, which in this case would be paying people to defraud public services occasionally, how would you really know how effective these systems were at reducing fraud costs to public services?
Where are you from?
Which is a convenient way for them not to track patterns of racial discrimination in their social systems.
https://www.hrw.org/news/2023/10/19/french-high-court-recogn....
Also racism is very well convoluted in Europe. American race hierarchies don't help.
Then use European race dynamics. Or even better, use local national race dynamics. But you can't do that if you don't collect data or make any effort to understand national race dynamics.
It absolutely does not follow that "Because American race dynamics =/= European race dynamics", therefore "there's no point in considering race dynamics in police action."
Worst case scenario, you spend tax money proving that people don't practice racism in France. That'd be a hell of a discussion point.
It's great these models are legible and open. Looks like civil service doing a job as well as possible.
The other half of the story is how, in practice these models are interpreted and what actions follow. Are further modification of the model a direct result of that and how iterative is it?
Doing that aggressively changes a bare "model" into an investigative tool, not simply a thresholding utility. Sorry if I missed it, but I don't remember reading anything about how it feeds back. But what I did read was unsettling - entering people's homes at random, quizzing neighbours, pretty fascist stuff. am I wrong?
I guess my point is that if you look at a bare data set, or even an algorithm, sure you can probably infer a lot about biases and intent that might be built in, but you can't see the bigger model within which this functions - and that's the real story.
Is it a persecutory investigative tool?
Like Hicks said we could use cruise missiles to drop food into the mouths of hungry people... a benefit system that could identify people who are struggling (of which crime is an indicator), and, I dunno, give them some money and help? That would be preachy, and quite possible imho.
This is how the system should function, but then you get public outcry when the worst of us are found abusing the system of subsidies while perpetrating their crimes. I think specifically of Marc Dutroux here.
On a side note, after the mass influx of Ukrainian refugees and wide public support, it is much more obvious that those in the greatest need tend to demand less, not more.
I have limited resources for investigating fraud. I need to allocate those resources efficiently. If profile A is more likely to commit fraud than profile B, why would I allocate an equal amount of resources for investigating fraud between them?
I'm going on a hunting trip and have limited time and resources for hunting. I need to plan my trip accordingly. If deer are more abundant for hunting in Colorado, why would I go looking for them in the Sahara?
Am I wrong to look for deer in Colorado rather than the Sahara? Am I biased for investigating fraud where it's more likely to occur? Am I evil for investigating robberies where they're reported?
I have little time and resources on this planet and want to allocate my time and resources as efficiently as I can in order to do the most Good. Why would I allocate my time and resources inefficiently?
No one thinks this. People that are worried are worried __because__ you don't need AI. AI is just more powerful tools.
- Enables significant increase in surveillance
- Enables highly specific targeted action (i.e. micro-targeted advertising, but not necessarily ads)
- Enables said action to be performed at high speeds with low latency and for cheap.
- Obfuscates decision making processes making it harder to point to who is doing wrong and even how they are doing wrong (because burden of proof is on those being wronged. More black box = more better)
- Enables easier deflection and laziness as one can say it is just math and pretend that math is objective and not subject to usage. You can deflect claims of targeting specific groups because you do not use a variable explicitly defining those groups but you use some or all of the strongly correlating variables.
There's plenty more here too. Of course this isn't all even necessary. I do work in ML and I do have a passion for it. These tools can be great and do a lot to make human lives better. I really do think with them we are capable of doing amazing feats such as reaching a mostly post-scarce society within our lifetimes. You could have them grow the food, pick the food, transport the food, stock the shelves, be the cashiers, and even transport the people (or products) to their desired locations. We could liberate most humans of most labor. That is on the horizon. But even such an amazing outcome which would allow humans to be more human than they've ever had the ability to be in all of history -- allowing them to pursue arts, sciences, community development, and all sorts of things without needing to worry about sustaining one's survival via one's labor. The transition to this post scarce world is not just technological challenges as those jobs won't be homogeneously displaced and as we must adapt to this post world. But I'm sure everyone would absolutely love robot butlers (that are non-sentient. Sentient machines shouldn't be forced laborers, but that's a whole other conversation because sentience isn't required for this. Narrow AI can do many of these already at high competency levels). So even the purely good side is still treacherous.
I'm just saying, we need to think deep and carefully. The nuance is not just digging in the weeds, it is the critical aspect. Ignoring it isn't "good enough" when ignoring the details is what specifically leads to trouble. But it's common to hear that these are claims of pedanticism.
By requesting welfare money, you agree that the government can investigate you.
Governments own lawyers advised them that their plans were illegal, in advance.
First-hand experience here. Doesnt work like that.
How does an investigation equal abuse?
It does not in theory but in practice that is the vehicle. You have no idea what Ive been through lately via KYC in France. I was worried Id never see my money while they were making a fool out of me. It's the vehicle and if one's not a discrimination target indeed it's invisible.
I've been there several times.
I needed to provide ample of documents that took time to assemble and I needed to take part on in person hearings, once in a town 200 km away from my residence.
Yes, they pay for the travel and by law my employer needs to provide me extra vacation for those days, but being the guy who needs to take 2 more days again doesn't necessarily boost your career and I'm sure my family would had appreciated if I spend the weekends with them instead of reading up legalisation.
Neither case I've done anything wrong and I was always very cooperative.
I broke, when I was fined for tax evasion, presented evidence on 3 hearings that they were wrong and after they revoked the fine, I still needed to pay interest as according to law I should had paid the fine while the appeal was in progress and since I didn't, I need to pay a surcharge for the delay. Given the entire case was a huge government error I could had appeal against the surcharge, but I didn't want to go through 3 more hearings.
You can absolutely bankrupt entire families both financially and emotionally with this shit.
Don't worry about it when I ask your friends and coworkers about your internet browsing habits.
And oh, after all that I'll not tell anyone "Sorry, we had a mistake in the algorithm and DeathArrow had nothing to do with it at all".
> An important limitation of our approach is that we do not know the relationship between the different variables that we analysed. For example, having a child between 12 and 18 (which increases risk scores) is likely correlated to being above 34 (which decreases risk scores).
Doesn't France publish demographic data that would help determine these relationships?
Anyway, it was a good read and many things were very clearly explained, which I appreciated.
1. https://www.tandfonline.com/doi/abs/10.1080/09720502.2010.10... [1051 citations]
2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4888898/ [1032 citations]
(also I have no idea whether it's related, but ... my very sparse knowledge of this field just regurgitates the Statmodeling blog (by Andrew Gelman and co.), which is basically something something monte carlo + hierarchical models + calibration... but of course it's easy to shout buzzwords.)
This isn't the end of the story. In situations where interpretability really matters or you have the time/resources, it's possible to mostly prevent this by building the model slowly and diagnosing multicollinearity issues as they arise. Perhaps you purposefully leave out variables because you see they are already highly correlated with existing ones and leave the model weights unstable. It requires exhaustive analysis and necessarily involves subjectivity, though. The researchers here could have done something like this, it's certainly done in industry for the aforementioned financial regulatory environments.
Non-linear models like trees don't have multicollinearity problems because they implicitly model interactions between variables with the branching behavior. Instead the problem is interpretation, it's hard to sum up all those branches in a way that is intuitive and meaningful.
CNAF is not a competitive employer.
French public service is essentially split in two: the State, and the various "Social Security" bodies which manages most of the welfare system (among them, the Caisse Nationale des Allocations Familiales which manages part of social & family welfare).
The "State" side has a very competitive track for stats/econ/ML employees, namely the national statistics office members (INSEE). As the name does _not_ indicate, they work everywhere in the government, not just at INSEE.
So if you're good at stats and in public service, the odds you end up in the CNAF team instead of the statistiscal office of the Social Ministry is about zero.
Hence SAS. Hence logit. And so forth.
Benefits fraud is a big problem in France, and this investigation shows an agency doing its job properly.
Corporate tax fraud is 27 billions
Social security contribution fraud is estimated at 14 billions (to compare to 200millions for the share of social security fraud)
Income tax fraud is 17 billions (and wages are automatically declared, so this fraud is mostly on non-work income).
But for some reason when my neighbour told me she'd been lying about not being able to work for the last 12 years so she could collect Jobseeker's allowance (basically "unemployment insurance" for those in the US) it really stung and felt like I, personally, was paying for her.
I got a lot more conservative after I moved to the countryside and realized how casual fraud and tax evasion were (I was routinely asked to pay for several-thousand-euro jobs in cash, and tradespeople balked at me for wanting a receipt)
Weirdly, social contribution fraud and income tax fraud ('travail au noir') is present in really big cities (mostly because the cost of getting caught is quite low compared to the margin of construction business in Paris/Lyon), and in rural areas, because no one ever check (it's changing, slowly).
>> felt like I, personally, was paying for her.
You also helped found Vlc, and probably half of the French startups that exist today. I agree that this is not fair (I do not ask for insurance when I'm in between jobs, because I have the means to ignore it now), but also, it will last two year at most.
I think the main difference is that when I hear my neighbour committing fraud, I think "well why the hell am I not doing that too?", but when I hear about a big company doing it, I don't feel like I could be doing the same.
Funny enough, I _did_ recently move to the Netherlands and found myself as the director of both an Irish and a Dutch company at the same time. I briefly looked in to Base Erosion and Profit Shifting (Dutch Sandwich, etc.) but suffice it to say as the only employee of both of these companies I didn't have the scale to make it worth the trouble.
I sat through a presentation on a national govt AML machine learning system, they discovered after building it that the training data was all wrong. Luckily it hasn’t been put into production yet.
I vote that machine learning practitioners shall henceforth refer to the softmax formula as the "squasher function". It's so much more descriptive, kudos to the French!
They need an algorithm to do that? As far as I know this is expected behavior of normal human beings. Geniuses all over the board.
We all know the core of the issue is the following balance:
- expecting money to circulate in a sane society, namely where there is not a massive amount of BS jobs, that to give a decent living to all households, is just utopia. Thinking otherwise is actually dangerous.
- one of the bad sides of human nature: some "nasty" and/or really "hard" "essential" jobs have to be done, and most people will try to avoid them (many won't be able to do them anyway). Not to mention, for a "developped" country, the level of efficacy is very high and only a small amount of people rotating on those jobs (but they need to build experience anyway) is actually "needed" to make things work properly.
and on top of (or beneath) all this there's the question of model performance, calibration, etc. and cost-benefit of this whole investigation department.
as in the recent case of California welfare fraud ~90% (or more) was perpetrated by organized crime groups. (which is absolutely not surprising, after all it requires a big operation to file hundreds of thousands of completely fake but seemingly-valid claims.) their claim that their model is targeting ~600 or more EUR/month frauds, which covers ~98% of the cases (based on some past historic data), but there's no mention of how much sense does this make. (it's quite likely that they could get ~90% of fraud value with drastically less effort spent on investigating those who are already likely to turn out to be rightful recipients, etc.)
so all in all there ought to be a very strong bias against the classic "looking for the keys under the streetlamp" mistake, which in this case means there's likely a real negative cost-benefit to spending most investigative resources on those who are already very likely to be rightful recipients of welfare.
(not to mention the usual completely avoidable - and thus totally infuriating and idiotic - discontinuity problems, like single parent with a kid just turned 18, or whatever the limit is, suddenly there's a huge drop in income, huge motivation to try to smudge the numbers, and somehow get more months of payments, etc.)
of course, hard data is always welcome, I'm just a regular keyboard warrior
"Basic controllers" will prefer to control not dangerous people (assuming they can tell them appart), to avoid to expose themselves to obvious danger. Because dealing with organized crime requires a whole other apparatus: the justice departement with the help of "services" (ahem...). Usually, those have to deal with the real nasty: killers, human traffickers, etc. They are very limited in human work force with often not enough means to do that work: so the "could you nuke those people because they are frauding the welfare system", ahem, maybe once in a while for posture. That makes me think about the movie and music industry asking for state-grade mass-monitoring of internet for their own benefit (in my country, they got that, hence you cannot trust the gov at not resisting such unreasonable lobbying).
To try to explain the shadows around this: with fraud and organized crime, beautiful ideological ideas do not work anymore: this is the hard reality, and it is often ugly. Only one thing has to really monitored: if the reality induced bias-es are not significantly self-maintaining/increasing that reality with some sort of feedback loop, presuming it is even possible to quantify that... Everybody wishes to never be on the bad sides of those reality induced bias-es. That's for the the "good" part, the "nasty" part is when it is not reality induced bias-es anymore but transforms into something ideological.
> The CNAF trains its models on households who were randomly investigated as part of the Paiement à bon droit Fraudes (PBDF), an annual survey
- It’s wrongly presented as a tool against fraud: it’s a tool to identify complex cases that are more likely to need human review. It is presented as unfair because files with mistakes can lead to asking people to pay back the money they have received in error, but it’s a big moral step to see that as the state abusing its power.
- There’s no attempt to match that risk to the actual complexity of cases and decide if the model is fair. It feels unprofessional not to connect the model with observations and bias: a model where the Police makes arrest perpetuates bias because it doesn’t take crimes without arrests in un-patrolled neighborhoods. If that were the case, the article would focus on the weight in training of a sample audited at random, or about the value of families suspected to be confused that were not (less than zero).
The argument “they are targeting confused people in need of help rather than big fraud cases” doesn’t make much sense: those are never very large sums. Significant fraud cases would have to be spread around many recipients told how to abuse the system, and from whom fraudsters collect.
It is fair to make that point against large tax frauds, but
1. That's a different administration and
2. You don’t want a model estimating if the recipient is confused. Fraudsters are not confused; they know exactly what they are doing and why.
But no, this time, it was about the welfare state doing its job, doing it mostly well, with details that were more impressive than I anticipated, and also opening its data to external analysts to continue improve it.
That was quite an emotional ride !
It also shows a bias towards single familly with low income, adding pressure to families already in state of precarity. It doesn't mean it's intentional but it is still an issue.
The fact that there are no proper audit of those algorithms before allowing those to be used looks quite bad in my opinion. This can be quite impactful on people's life.
Which bit is the one you're impressed with ? The part where you personally will be fine?
Please keep in mind that this algorithm doesn't actually punish anybody for wrongdoing, it's just a starting point for a human-led investigation.
Personally, I'd be more interested to know how a human-led investigation works. Is the process fair and reasonable? Do they go after poor people for minor problems with their following of the rules or do they go after them harshly? Is there a presumption of innocence and do they have an opportunity to appeal and have a fair shake in arguing their case?
Will they use they spend the same effort to ensure nobody goes hungry because of a benefit they're entitled to but aren't claiming ...? I doubt it
——
Going after welfare claimants is wildly popular in the UK thanks to years of propaganda on TV dedicated to shows about people cheating the system. There is a widespread view that anyone claiming benefits is lazy and a cheat. This gives the departments carte Blanche to introduce inhumane claims processes, algorithms etc
> It also pointed out that some investigations started by the model conclude that the CNAF actually owes the beneficiary money.
What do they actually do with that data in those circumstances?
It’s a completely different endeavour to look for fraud and to look for under-claimed benefits. I can’t believe the same project by the same people would be equally concerned with both tasks
The fact that street junkie vs Google engineer have a different propensity in mugging elderly ladies is definitely partially explaine by variable household_income is not economic discrimination.
You’d have a problem if you did regressions on a sample selected by the results of previous regressions, but this is not the case. And in this case you are looking for a biased sample of high-risk individuals.
I don't know about France, but I assume it is similar to my country Poland where one can sue the state very easily with minimal expense. My parents did when I was a child and we were quite poor (self represented and all that).
The laws and structure of society ENSURE this is the outcome of any hierarchical capital/property based system.
So it’s not the algorithm that’s the problem. The problem is having a system like this to begin with.
I’m not sure what you mean by this, do you have any examples?
Redlining was a racist social bias embedded in the city infrastructure planning and implementation process.
Or
“slaves are legally 3/5th of a human” was encoded into the US constitution
By encoding into the legal structure a social bias, and then using that legal precedent as the goal state, implemented via orthogonal behavior measures, you are creating a measurement system that will enforce whatever goal condition the law encoded , including any biases that were encoded in the original lawmaking process
Agree that it dehumanizes citizens though.
I am less familiar with French laws and you agree that there are likely laws which encoded bias.
So we agree that the examples I used of bias encoding are invariant to the source and you accept the premise
So the only thing you response does is give a snide jab at what you perceive as relative foundational inequities in the law.
Was that your intent? To simply shit on America?
Wouldn't a more accurate headline be something like "Here is the math behind how France's fraud detection system works"?
and the training data is all overpayments, even if it is not fraud
We all feel sympathy for poor disabled people who have a harder lot in life than most. But investigating a poor disabled person for fraud is only really a problem if the investigation is malicious, biased, unfair, disrupts their life, presumes their guilt, etc. How are these investigations actually run? Are they run fairly or not? That's what I'm really curious about.
So personally, I think you're conflating 2 different things. Simply a basic check for fraud in and of itself is a separate thing from a malicious investigation that's run horribly.
PS: Let's not forget that many poor people pay various kinds of taxes. Being a wise steward of their tax dollars and trying to fight fraud is not in and of itself anti-poor disabled people.
This system targets these people and not other types of fraud which are a larger source of fraud in terms of lost money. This correlates strongly with political discourse, in terms of who is accused of being "problematic".
Let's keep in mind that this algorithm isn't in and of itself punishing people. It's just the starting point for a human-led investigation. Isn't that a proper application of technology?
My bigger concern is that any human-led investigation is fair and reasonable and doesn't go hog-wild trying to punish people for minor misunderstandings of the rules and policies that are in place. (Somebody in this thread said that the rules just for how housing allowances can be used are like 80 pages long, so it's probably the case that it's pretty much impossible for any random citizen to know all of these rules)
> other types of fraud which are a larger source of fraud in terms of lost money
Surely you're not suggesting that welfare fraud shouldn't be investigated so long as say Military-Industrial-Complex fraud or Big-Pharma fraud is even greater?
No, I'm talking about other types of fraud effecting the social system funds. Some of them by individuals, some of them by companies.
> Isn't that a proper application of technology?
It's hard to apply technology properly when you don't start with good intents.
Are we looking at the same page?
>For vulnerable recipients who struggle to navigate the complex rules of the French social security system, the risk of their benefits being reclaimed or terminated is high — even if they unintentionally committed mistakes.
Welfare-driven countries should deleverage their economies and depromote price growth until they no longer need to pay welfare on mass scale due to people being able to afford housing without much strain.