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Assorted greeting cards that don’t sound generic and like robot vomit

Assorted greeting cards that don’t sound generic and like robot vomit

Assorted greeting cards that don’t sound generic and like robot vomit

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Keenas Recycled Tote Bag - Hand-Drawn Australian Design

Keenas Recycled Tote Bag - Hand-Drawn Australian Design

Keenas Recycled Tote Bag - Hand-Drawn Australian Design

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Blog Post

SHAP seems like a better tool than it is an acronym

LousUnitry

Systems Consultancy

June 21, 2026

So I've been playing with SHAP (SHapely Additive

I've been playing with SHAP (SHapley Additive Explanations... doesn't that roll off the tongue and into the heart, eh?) modelling lately as a way to crack open the black box and start defining, with a bit ofconfidence, what is actually promoting system change or like... what is contributing to system stagnation.

It shows whether effort is genuinely supporting equitable outcomes, or paint the roses red. Paint the roses white at the cost of the sanity of workers and benefit of community.
Unlike ANOVA or MANOVA, SHAP isn't primarily concerned with whether a relationship exists or whether a result is statistically significant. Because you can kinda see that.

But in an age of increasing scarcity- having a mechanism to consider the relative contribution of variables, the interplay between them, and how those relationships influence different outcomes in a predictive context might be nice. Or you know, super frustrating when it's stepped over for the guy from golf's approach. Atleast you tried.

The crux is though, rather than asking, "Is there a relationship?", SHAP is more interested in asking, "How much is this factor contributing, and how is it interacting with everything else?"
This becomes particularly useful in human services, where multiple variables often represent different manifestations of the same underlying circumstance. Traditional statistical approaches can struggle with multicollinearity, requiring the analyst to reduce, combine or exclude variables. SHAP allows us to examine how explanatory weight is distributed across these related factors and how they combine to influence predictive outcomes.
From a service design perspective, it creates an opportunity to consider cross-functional contribution across a system is fair and would help, no matter how strong the protest. Outcomes rarely emerge from a single factor acting in isolation. They reflect interactions between states across domains.
For me, it fits naturally alongside state-space and systems thinking approaches. *chefs kiss* My faviourites.

Rather than treating risk or vulnerability as static characteristics, it allows us to examine how different factors gain or lose influence as an individual or cohort moves between states over time.
One of the more interesting aspects in it's function is the notion of borrowing strength across related variables. Where multiple factors contain overlapping information, predictive models can leverage those shared signals to improve performance. This is good for our sector because data quality is usually ... not the best.

This provides a way of examining how that predictive strength is distributed throughout the model. The result is not simply an understanding of which variables matter, but how clusters of variables collectively contribute to an outcome.
The catch is that SHAP explains model behaviour, not clear cut reality, it's interpretative. What gets measured, how it gets recorded, what gets omitted, and which populations are overrepresented all shape the resulting outputs.
You can see this tension in predictive justice tools like the one just introduced by NSW police.They've got this offender prediction recidivist model that's like an orwellian hellscape adventure park...

This thing essentially minority reports you with CCTV if you're one shade from a strong cup of earl grey. I mean, the outputs may appear objective while reproducing existing structural inequities. Conversely, intervention-focused frameworks such as Ontario's domestic violence risk assessment for IPV place greater emphasis on dynamic risk factors and intervention planning. The question shifts from "Who is risky?" to "What is driving the risk, and what can we reasonably do about it?"
For me, that's where SHAP becomes genuinely interesting. The value isn't that it predicts. Plenty of things predict. The value lies in providing another lens through which to reason about complex service systems, identify leverage points, and understand which interacting conditions appear to be contributing most heavily to change, persistence or decline.

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Assorted greeting cards that don’t sound generic and like robot vomit

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Big Sticker, Julia?!

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