LAUNCH
                   
WITH CONFIDENCE

RELIABLE AI

Address ML Bias & Reliability issues with our Firewall built around Model Robustness, Fairness, Explainability, and Auditability.

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OUR TEAM HAS WORKED WITH

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...and more

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AI For Good
Innovation Factory
2021 Winner

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Global Silicon Valley
Bootcamp II

2021 Finalist

AI-Driven B2C companies need to address ML Risk...

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Facebooks ranks last in consumer trust because of poor ML practices. Zillow lost $6B and fired 25% of it's staff because of faulty ML assumptions. Models are brittle break easily if market conditions change. 

ML is extremely powerful, but complex. This complexity leads to risk, which if unmitigated, can have serious consequences.

And consumers WANT more reliable AI. The winning companies of the future will be responsible producers of AI

Despite 80% of executives being concerned about ML Risk, only 27% can get the significant internal resources required.

Most companies also lack internal expertise to even know where to begin.

This is on top of the fact that it is extremely difficult, if not impossible, to capture your 1st party model bias yourself... very tricky finding issues in the system you built.

...but can't.

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Today, guardrails against bad ML are nonexistent.

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Nearly every company is launching models into the wild with little regard for fairness to address bias, or robustness to survive in a real and changing world, or even basic explainability for stakeholders.

Less than 20% of companies even monitor their models in production.

It's all about offline accuracy, which clearly isn't and shouldn't be the only yardstick for ML models.

MEET WIZARD.AI

Mitigate ML Risk & launch Reliable ML with confidence in

3 Easy Steps

. . .

1. MODEL
ROBUSTNESS

Once you've finished modelling, simply use our SDK to check your model's robustness.

We essentially PenTest your model, just as you would a new piece of software, to uncover your data+model vulnerabilities and risks that would be exploited in production.

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2. MODEL
FAIRNESS & EXPLAINABILITY

Next, we utilize our 3rd party fairness dataset and cutting-edge XAI techniques to crack open black box models to find the drivers of model decisions, highlighting potential biases while also providing powerful insights into your customers, models, and data.

All this crucial information is packaged into a report and sent to all interested parties.

3. MODEL
AUDITABILITY

Finally, we constantly audit your models in production to ensure they adhere to Reliable AI principles, and act accordingly the moment they fail.

Remember: Reliable ML practices do not start until a model is in production!

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