June 12
1.0
Section Text 1.1
Quantum

Explainable AI Using Expressive Boolean Formulas

By: Elton Zhu and Serdar Kadioglu
Share
QuantumArtificial Intelligence

Amazon Quantum Solutions Lab and FCAT proposed and implemented an interpretable machine learning model for Explainable AI (XAI) based on expressive Boolean formulas.

When

Thursday, April 27, 2023

9:00 a.m. – 10:00 a.m. ET

Where

Zoom

Meeting ID: 994 3158 6099
Passcode: 253444

  • Facebook.
  • Twitter.
  • LinkedIn.
  • Print

The Challenge

Most of today’s machine learning (ML) methods are complex, and their inner workings are difficult to understand and interpret. Yet, in many applications, explainability is desirable or even mandatory due to industry regulations. While there are interpretable machine learning models out there, some of them are not expressive enough. Models such as decision trees could get deep and difficult to interpret very easily. Finding an expressive rule with low complexity, but high accuracy seems like an intractable optimization problem.

The Impact

Explainable AI models can be used in many areas of the firm, such as creating interpretable rules to understand why certain customers signed up for a product while others did not. The rules can lead to high level insights and help business owners improve their products.

The Outcomes

We successfully implemented our XAI model, and benchmarked it on a few public datasets for credit, customer behavior, and medical conditions. Our model is generally competitive with other classifiers. Our XAI model can potentially be powered by special purpose hardware or quantum devices for solving Quadratic Unconstrained Binary Optimization (QUBO). The addition of QUBO solvers reduces the number of iterations and could lead to a speedup.

The Deep Dive

FCAT researchers proposed the model based on expressive Boolean formulas. The Boolean formula defines a rule according to which input data are classified. Such a formula can include any operator that can be applied to one or more Boolean variables, such as And and AtLeast. For further details on this project, read the full paper here.

  • Facebook.
  • Twitter.
  • LinkedIn.
  • Print
Views expressed are as of the date indicated, based on the information available at that time, and may change based on market or other conditions. The opinions provided are those of the author and not necessarily those of Fidelity Investments or its affiliates. Fidelity and any other third parties are independent entities and not affiliated. Mentioning them does not suggest a recommendation or endorsement by Fidelity.
 
1084542.1.0
close
Please enter a valid e-mail address
Please enter a valid e-mail address
Important legal information about the e-mail you will be sending. By using this service, you agree to input your real e-mail address and only send it to people you know. It is a violation of law in some jurisdictions to falsely identify yourself in an e-mail. All information you provide will be used by Fidelity solely for the purpose of sending the e-mail on your behalf.The subject line of the e-mail you send will be "Fidelity.com: "

Your e-mail has been sent.
close

Your e-mail has been sent.

This website is operated by Fidelity Center for Applied Technology (FCAT)® which is part of Fidelity Labs, LLC (“Fidelity Labs”), a Fidelity Investments company. FCAT experiments with and provides innovative products, services, content and tools, as a service to its affiliates and as a subsidiary of FMR LLC. Based on user reaction and input, FCAT is better able to engage in technology research and planning for the Fidelity family of companies. FCATalyst.com is independent of fidelity.com. Unless otherwise indicated, the information and items published on this web site are provided by FCAT and are not intended to provide tax, legal, insurance or investment advice and should not be construed as an offer to sell, a solicitation of an offer to buy, or a recommendation for any security by any Fidelity entity or any third-party. In circumstances where FCAT is making available either a product or service of an affiliate through this site, the affiliated company will be identified. Third party trademarks appearing herein are the property of their respective owners. All other trademarks are the property of FMR LLC.


This is for persons in the U.S. only.


245 Summer St, Boston MA

© 2008-2024 FMR LLC All right reserved | FCATalyst.com


Terms of Use | Privacy | Security | DAT Support