Squark is Not a Black Box

No, It’s Not a Black Box: The Reality of Squark’s Explainable and Transparent No-Code Predictive AI Automation

A Black Box is a dark cube of mystery.  Some random number generator created by Rube Goldberg. A machine into which the gearing is not visible.  It’s opaque and enigmatic.  While it may sound clever to make that assertion about machine learning and AI, it’s a bit daft to be honest with you.  When I started doing BI, people wouldn’t believe the reports could possibly match the database, so you’d do some SQL and show them the matching numbers.  Doubt is healthy.  Skepticism out of a lack of awareness of what’s happening today in machine learning and AI isn’t helpful.

So please let me assert: Squark is not a black box.

We’ve heard this claim made more than a few times about the grand automation driven by Squark’s intelligence.  But that’s not an accurate statement; however clever or smart it may sound.  Squark simply isn’t a black box. Here’s why:

  1. You know the data going in.  Since you either manually or programmatically connect to the systems and files you use everyday wherever they exist, you know what the data is.  Whether in ServiceNow, Hubspot, Salesforce, Google, Snowflake, SQL Server, Domo, and more, you know what the data is.
  2. You know the data prep and feature engineering we do.  We make it clear how we have improved your data, whether through imputation, transformation, or other clever methods.  A result of Squark is the ability to review what we did to your data when we automatically prepared it.
  3. You know the algorithms we use.  Squark provides what algorithm and the resultant model metrics, so you can understand the detail performance of the model, including full validated and cross-validated results and other details.
  4. You get deep AI explainability that makes lucid the answer to the question “why.”  Transparent results that identify what drives predicted outcomes from your historical data are easy to understand across the dataset and for each individual prediction. Helpful data visualizations are created to further guide comprehension.
  5. You can export the model code.  Squark enables full review of the model code exported in a variety of formats.

In summary, with Squark you know the data going in, what’s done to prepare, the algorithms used, the drivers of the prediction, and can even review the model code, so there is no black box, only predictive power.  Squark is the best no-code automation of data science on the market, and you simply need to want the light to shine in, to see it bright as a sunny day.