RULEX   EXPLAINABLE AI  

AUTOMATICALLY CREATE SELF-EXPLANATORY PREDICTIVE MODELS

No special data preparation
No “forensic mathematics”
No waiting for algorithm retrofits

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RULEX IS THE AI THAT TELLS YOU WHY™, AND IT IS HERE NOW!

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Business experts, not data scientists, build and use explainable predictive models

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Models are easily operationalized and integrated into existing applications

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Models are auditable and provable, and can be edited by business experts

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The only AI automatically compliant with GDPR Article 22 for automated decisions

 RULEX MOVES BEYOND 

TRADITIONAL BLACK BOX AI

The problem with conventional AI is very simple: it’s unexplainable.

Conventional AI relies on machine learning algorithms such as neural networks and others that have one key feature in common: they produce “black box” predictive models, meaning they’re mathematical functions that cannot be understood by people, even mathematicians.

Rulex’s unique, proprietary machine learning algorithms work differently. Rulex creates predictive models in the form of first-order conditional logic rules that can be immediately understood and used by everybody. Here an example of Rulex clear box predictive model.

IF (customer_province in {A, B, C, D} AND damage_class in {1} AND Number of days between policy start and date of accident <= 371
THEN Fraud = Yes
IF (customer_province in {E, B, C, F} AND Customer age > 48 AND Number of days between date of accident and complaint  > 1)
THEN Fraud = Yes
IF (customer_province in {G, H, I, J, K, L, M, N, B, O, P, Q, R, S})
THEN Fraud = No
IF (Number of days between date of accident and policy end <=  2)
THEN Fraud = No

RULEX XAI –  HOW IT WORKS 

Rulex’s core machine learning algorithm, the Logic Learning Machine (LLM), works in an entirely different way from conventional AI. Rather than producing a math function, it produces conditional logic rules that predict the best decision choice, in plain language that is immediately clear to process professionals. Rulex rules make every prediction fully self-explanatory.

And unlike decision trees and other algorithms that produce rules, Rulex rules are stateless and overlapping, meaning one rule can cover many cases, and many rules can cover a single case. This allows for fewer, simpler rules and provides broader coverage at the same time.

Rulex calculates the coverage and accuracy of each rule, making it easy to select the most effective decision rules.

Also, proven heuristic human rules can be added to the predictive model, allowing a seamless blend of human and artificial intelligence. Human rules are also rated for coverage and accuracy, allowing Rulex to easily evaluate the quality of the decision rules in use and reduce false positives.

THE ONLY GDPR-COMPLIANT AI SOLUTION

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The European Union’s General Data Protection Regulation (GDPR), which went into effect in 2018, strengthens data protection for consumers and harmonizes data security regulations within the European Union (EU).

It is well known that, as with previous data privacy regulations, the GDPR significantly increases compliance responsibilities for organizations that collect and analyze data about consumers. It requires companies to inform subjects about any personal data collection and processing and obtain their consent before collecting such data.

However, few people realize that the GDPR also imposes strict controls on the use of machine learning for automated decision-making.

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The GDPR grants EU citizens and residents the right “not to be subject to a decision…which is based solely on automated processing and which provides legal effects (on the subject).” This includes decisions concerning credit, employment, education, housing, and more. Experts characterize GDPR as a “right to an explanation.”

The GDPR threatens the use of traditional machine learning AI technology for automated decisions. Article 14 of GDPR states that when a company uses automated decision-making tools, it must provide “meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.”

In short, the GDPR prohibits the use of machine learning for automated decisions unless you can provide a clear explanation of the logic used to make each decision. This is simply not possible with conventional machine learning, which produces decisions through black box models that cannot be explained.  

GDPR: STRICT CONTROLS ON

AUTOMATED DECISION-MAKING

GDPR: STRICT CONTROLS ON

AUTOMATED DECISION-MAKING

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The GDPR grants EU citizens and residents the right “not to be subject to a decision…which is based solely on automated processing and which provides legal effects (on the subject).” This includes decisions concerning credit, employment, education, housing, and more. Experts characterize GDPR as a “right to an explanation.”

The GDPR threatens the use of traditional machine learning AI technology for automated decisions. Article 14 of GDPR states that when a company uses automated decision-making tools, it must provide “meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject.”

In short, the GDPR prohibits the use of machine learning for automated decisions unless you can provide a clear explanation of the logic used to make each decision. This is simply not possible with conventional machine learning, which produces decisions through black box models that cannot be explained.  

RULEX IS  GDPR-READY 

Rulex is the only AI that is compliant by design with the GDPR. First, it produces transparent, easily explainable AI models. Using a series of if-then statements, Rulex automatically produces self-explanatory logic for all decisions. Rulex rulesets make it possible to explain a decision directly to the customer or provide customer service agents with the ability to look up the reason for a decision.

5 CONSIDERATIONS FOR GDPR COMPLIANCE 

 

If you are currently using machine learning for profiling and automated decisions, and are subject to the GDPR, here are five things you need to do now:

Identify processes in your business that use profiling and automated decisions.

Inventory the machine learning models currently used.

Assess your existing models. Are they interpretable? Can you demonstrate to an auditor that they do not discriminate?

Assess your current machine learning techniques. Do they produce interpretable rules?

Develop a strategy for meeting compliance requirements in each stage of the machine learning workflow.

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