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
Dublin, Oct. 16, 2020 (GLOBE NEWSWIRE) -- The "Tracking Mistakes in AI: Using Vigilance to Avoid Errors" report has been added to ResearchAndMarkets.com'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 https://www.researchandmarkets.com/r/78apnt
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