Why go for a configuration based format over a library though? Something like Streamlit or Dash offers more control and lends itself better to interactive exploration.
A library is for coding in the classical sense. Declaring in YAML is a very light form of coding I would say, much more accessible to non-engineers. A simple dashboard in YAML is so much more readable to a human than some python code that uses a library. This is mainly a audience question.
If there's a use case in the future, we could consider also offering a library. But if you just want to vibe code dashboards in actual code, there's plenty out there already that will give you JS code (predominantly).
When I have seen instances of this, it's usually because there is another variable. Example from wikipedia:
>A common example of Simpson's paradox involves the batting averages of players in professional baseball. It is possible for one player to have a higher batting average than another player each year for a number of years, but to have a lower batting average across all of those years. This phenomenon can occur when there are large differences in the number of at bats between the years.
The per-year values aren't weighted in the combined total average.
In regulated industries and government they aren't going to be allowed to process your appeal with just AI, so there's a cost asymmetry there that favors the "ddos"ers.
It's not really about the dollar amount of fraud, but maintaining public support for benefits schemes. If people feel that benefits aren't going strictly to the intended recipients the sense of fairness that underlies the system is eroded.
There's also the issue that if you don't counter fraud it will accelerate as organized crime jumps on the bandwagon.
> If people feel that benefits aren't going strictly to the intended recipients the sense of fairness that underlies the system is eroded.
And in an ideal world, you then say "that will literally cost you more money than not doing it", and the argument ends.
In practice, gatekeeping on government programs is primarily pushed for by people who don't think the programs should exist at all. The Venn diagram of the set of people who will withdraw their support for programs without such gatekeeping, and the set of people who mostly don't support such programs in the first place, is close to a circle.
In reality, enterprises are happy to offload even risky tasks to others as long as they get some contractual guarantees about their data. Would they like more choice in who to buy from? Yes, but not enough to in-house such a specific discipline.
You're not wrong that there's an element of self-defense. What makes it harder to accept is the lack of effort or willingness to de-escalate the conflict.
There's still inequality in someone else receiving a false positive approval from the AI while another gets rejected. Equal treatment and stochastic systems don't mix.
The law is already a stochastic system and it's designed to eventually receive a result that is correct most of the time. It doesn't try to be 100% correct. Not having it be 100% correct does not cause society to fall over.
Exactly. Not to mention that there is no reason a well-calibrated AI system cannot be Pareto-optimal over the human-only system.
For example if you are interested in bias, an AI system can be backtested; new policies applied on old data, and whatever metric for inequality you are investigating can be measured.
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