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Tracking Mistakes in AI: Which Data Models can Deliver Biased Results, and the Ways and Means by Which Financial Institutions Can Correct for These Biases

Research and Markets
·2-min read

Dublin, Oct. 16, 2020 (GLOBE NEWSWIRE) -- The "Tracking Mistakes in AI: Using Vigilance to Avoid Errors" report has been added to's offering.

This latest research Report, Tracking Mistakes in AI: Use Vigilance to Avoid Errors, discusses modes in which data models can deliver biased results, and the ways and means by which financial institutions (FIs) can correct for these biases.

AI models reflect existing biases if these biases are not explicitly eliminated by the data scientists developing the systems. Constant monitoring of the entire operation is required to detect these shifts. The remedy for such lack of focus is training.

Highlights of the research note include:

  • A glossary of terms

  • The various modes in which data can evidence biases

  • Solutions

  • Prophylactic methods

  • The appeal - and danger - of shortcuts

Key Topics Covered:

1. Executive Summary

2. Introduction

3. Examples of Mistakes Already Seen In Market

  • Apple Card

  • Risk Assessment Scoring

  • Other Inappropriate Use Cases

4. Definition of "Errors and Mistakes" Used In This Report

5. Why AI Mistakes Must be Avoided

  • Why Mistakes May Become More Frequent

6. Data Management Becomes More Important

7. Problems in the Model

  • Feature selection errors

8. Inappropriate Use

9. Conclusion

10. Related Research

11. Endnotes

  • Glossary of Terms Used Frequently In this Report

  • Number of AI Solution Categories Reported By Large Banks, By Functional Area

  • Data Management and AI Must Work Together

Companies Mentioned

  • Apple

  • ProPublica

  • The Federal Reserve

  • The Verge

For more information about this report visit

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