+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)


Tracking Mistakes in AI: Using Vigilance to Avoid Errors

  • ID: 5157754
  • Report
  • October 2020
  • Region: United States
  • 15 Pages
  • Mercator Advisory Group
AI and Machine Learning Can Help FIs Avoid Risk - but They Have Risk of Their Own


  • Apple
  • ProPublica
  • The Federal Reserve
  • The Verge

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.

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 solutions can unwittingly go astray,” comments Tim Sloane, the Report’s author and director of the Emerging Technology Advisory Service and its VP Payments Innovation. “Applying AI to issues that can have large negative social consequences should be avoided. One example of this is using AI to implement the business plan of social networks Facebook, YouTube, and others, as presented in the documentary “The Social Dilemma.” The documentary contends that social networks have optimized AI to drive advertising revenue at the expense of the individual and society. To drive revenue, social networks build psychographic models for each user to predict exactly which content will best engage that user.”

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
Note: Product cover images may vary from those shown


  • Apple
  • ProPublica
  • The Federal Reserve
  • The Verge

1. Executive Summary2. 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 Use9. Conclusion10. Related Research11. Endnotes
Figure 1: Glossary of Terms Used Frequently In this Report
Figure 2: Number of AI Solution Categories Reported By Large Banks, By Functional Area
Figure 3: Data Management and AI Must Work Together
Note: Product cover images may vary from those shown


  • Apple
  • ProPublica
  • The Federal Reserve
  • The Verge
Note: Product cover images may vary from those shown