Get Explainable Results With Entity Resolution
Do you know why explain results are critical for entity resolution? It’s because business users, data scientists and auditors all want to know why records matched or didn’t, as well as the details of how decisions were made. Watch this video with Senzing executive Will Layton talking about how explainability can help you and others understand and trust your entity resolution software.
It’s important that the results of your entity resolution system are easily explainable. If you don’t have full confidence in your results, it’s hard to feel comfortable making important decisions based on them. Plus, there will be times when you’ll need to explain why and how you made a business decision.
The explainability capabilities in Senzing® entity resolution help you verify and demonstrate in detail how data-driven decisions were made. With just a few clicks or keystrokes, you can clearly see why a match was made or not, as well as the details of how an entity evolved.
Try Senzing entity resolution for free. It’s easy to get started in just 15 minutes. Take a look at 3 Quick Ways to Explore Senzing Entity Resolution and see for yourself the power of Senzing AI-powered entity resolution.
Edited Video Transcript
Timestamps
0:00 Introduction
0:13 Explainability in Entity Resolution: Why, Why Not and How?
0:29 Explainability Provides an Audit Trail for Entity Resolution Decisions
0:52 Explainable Results Instills Confidence in Entity Resolution Results
Hi, this is Will from Senzing. When people are trying to run Senzing software, there’s a bunch of things that they like about Senzing, for example…
0:13 Explainability in Entity Resolution: Why, Why Not and How?
Everything’s explainable as to why or why not Senzing entity resolution software does something. In the case where multiple entities come together, we can ask the Senzing engine, why did they come together? Or, more importantly, we can ask, how did they come together? And Senzing will show us the data flow… show us the results.
0:29 Explainability Provides an Audit Trail
Now, by comparison, when you’re doing this with ARML (Arm Machine Learning), it may not necessarily be explainable. But when you’re using Senzing entity resolution, not only is it explainable, now you can take proof to lawyers or a court system if you need to, and they can see why and how decisions were made. It gives you the actual audit trail.
0:52 Explainable Results Instill Confidence in Entity Resolution Results
Senzing explainability tools not only help our customers have confidence in their results, but now when they share the results, the people they’re sharing them with gain confidence because they see how and why we do things in the data flows with our explainability tools.