Whitebox Machine Learning
What Is Whitebox Machine Learning?
Whitebox machine learning algorithms give us not just a result but also clearly readable rules. Users receive the decision the algorithm has reached, as well as a breakdown of the process followed to reach it.
The concept of transparency is key in machine learning. Consider, for instance, fraud prevention. You feed some information into a machine learning model – in this case, what you know about a potential customer. “Are they legitimate or a scammer?”, you ask.
Every machine learning model will give you an answer to that – a risk score in the case of fraud prevention, explaining how likely this person is to be a scammer. But will it tell you why and how it reached that conclusion?
If it does, it’s a whitebox machine learning model. If it doesn’t, it’s the opposite: a blackbox machine learning model.
In other words, whitebox machine learning allows you to look into the inner workings of the ML model.
In addition to the output you get, you also receive a decision tree that details exactly which parts of the input were taken into account, how each factor was weighed, what was ignored and so on.
How Does Whitebox Machine Learning Work?
Let’s look at how it works in practice.
- Machine learning involves feeding huge amounts of example data into an algorithm and letting it self-learn from it.
- Following this training stage, we then provide real-world data as input.
- Every machine learning algorithm will give us an output – a decision or result – for it.
However, a whitebox system will accompany that result with an explanation of how it was reached, in the form of decision trees, weighted factors and so on.
Why Is Whitebox Machine Learning Important?
Unlike most machine learning models out there, which tend to be blackbox, whitebox machine learning offers valuable insight into the process used to reach its output. This thought process is presented to the user in the form of decision trees.
There are three main benefits to this:
- The user knows exactly how this complicated decision was reached, so they can verify the result or even the thought process.
- Whitebox machine learning allows us to tweak and adapt decision processes, optimizing and improving the output at will, where needed.
- Humans have the final say on the results, so accuracy is high.
This is all because of the transparency of whitebox machine learning.
As a trainer or user of a whitebox system, you can run tests to see whether the decision tree works for you or even use a sandbox to fine-tune as needed.
This helps us understand scores, adjust approve/decline thresholds – and is valuable when conducting manual reviews.
How Can Whitebox Machine Learning Fight Fraud?
Sophisticated whitebox algorithms are considered central to the fight against fraud for more and more businesses around the world – both in terms of fraud prevention and fraud detection.
- Machine learning algorithms study an organization’s data and learn from historical results and cases. This initial training stage combines pre-set decision trees with personalized, organization-specific needs and circumstances.
- Human-readable rule suggestions are presented in the form of decision trees.
- The user can choose to adjust these rules according to their understanding of the questions the algorithm will be called to answer.
- From there, the algorithm starts fighting fraud in earnest, using these rules to assign a fraud score – with accuracy estimates – to every transaction.
- Presented with the risk score, the weighted factors and decision tree, fraud analysts can better understand the score when conducting manual reviews.
- Throughout, the user can go back in and tune further, constantly improving the results by adjusting approve/decline thresholds.
If you’re looking for more detail and technical elaboration, consider reading the breakdown of how whitebox machine learning works at SEON, in our open documentation section.
Why Choose Whitebox Over Blackbox Machine Learning?
Industry insiders consider whitebox machine learning vastly superior to blackbox machine learning – and once you know their differences, it’s not hard to see why:
Whitebox machine learning is adaptable, transparent and humans have the final say in the results.
Because it doesn’t just tell you what but also why, it differs from blackbox algorithms in several ways:
|Whitebox machine learning||Blackbox machine learning|
|Provides process visualizations||Does not explain internal processes|
|Allows for testing and tweaking||Locks you in|
|More accurate||Less accurate|
|Humans have the final say||Humans only see results|
|Requires more human involvement||Works unsupervised|
|More likely to catch historical patterns||More likely to identify new patterns|
|Answers “what?” and “why?”||Answers “what?”|
|Gives users full control||Limits control by not being transparent|
Clearly, a whitebox approach is ideal in fighting fraud and in other applications of machine learning.
However, this is not to say that blackbox should always be avoided. Thanks to its increased speed and ability to flag new and unusual behaviors as suspicious, it can be valuable as a secondary tool, in certain situations.
Depending on the organization’s industry and historical fraud data, certain vendors will recommend or suggest:
- a set of industry-preset rules
- a set of machine-learning rules
- a set of custom rules
Because all of these are transparently presented in a whitebox setting, and visualized in the form of decision trees, users will get a clear overview of how scores are calculated for each setting – and will be able to adjust the weight of each rule.
Even once the system is approved and set in motion, human risk analysts can always have the final say with a whitebox system.
This is why whitebox machine learning means you’re never at the mercy of the algorithms.
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