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AI and machine learning are everywhere. Most decisions affecting every aspect of our lives are being made based on anomalies, classifications, and predictions. Even governmental decisions such as where will new schools be built may consider an enormous amount of demographic, geographic, and socioeconomic data to determine exactly which land will house the school – and developers are using similar data to buy up the plots they think the governments will choose.

We’re in an interesting position here at Binah. We’re creating and building the tools that help companies get a complete and true picture of their data – beyond anomalies, predictions, and classifications – to drive better models. Binah clients can do everything from better managing foreign currency exchange rates to maximize profits, dramatically increase customer retention and predict customer churn to analyzing IoT data that can send early warnings to your doctor that you might be at high risk for a heart attack.

Binah’s technology can increase business efficiency by better eliminating the outliers that may skew data, such as removing the unusual loan defaults so they can create better rules for offering loans to a greater majority of people.

The benefits of these machine learning and AI-based algorithmic models are clear. The school will be built in the area that will benefit the most children who will become school aged over the next 15 years. Loan policies will be changed to increase the possibilities of more people benefiting from home ownership and banks being repaid.

However, we’re putting all our trust in the numbers. All these decisions are based on probabilities – and life isn’t as certain as that.

Take insurance, for example. You are completely healthy, workout three times a week, and have low cholesterol and a reasonable BMI for your age. You show no signs of heart disease whatsoever. However, you had to give your medical history when applying, and your father had a heart attack and bypass surgery while your mother has high cholesterol. When the insurance company’s algorithmic model is applied to you, you become a very high risk for heart disease, and your rates are adjusted accordingly.

Those algorithms got you a fantastic low-interest car loan – even if most of your savings are eaten by those enormous health insurance premiums.

Where are you the “norm” and where are you the “outlier”? How do you prove it, in what situation? The more we depend on machine learning and big data as a society, it may become harder to fight injustice. Of course, it isn’t so easy to fight injustice today…

Maybe our dependence on big data, machine learning, and AI will at least help some of us in some parts of our lives – and punish us for being the outlier – or even being related to the outlier - in others.

David Maman is CEO, CTO & Co-founder of Binah.ai, whose out-of- the-box data science solutions leverage signal processing combined with machine learning and AI to create better models and accelerate delivery of the right answers to critical business questions. www.binah.ai

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