PaymentsJournal
No Result
View All Result
SIGN UP
  • Commercial
  • Credit
  • Debit
  • Digital Assets & Crypto
  • Digital Banking
  • Emerging Payments
  • Fraud & Security
  • Merchant
  • Prepaid
PaymentsJournal
  • Commercial
  • Credit
  • Debit
  • Digital Assets & Crypto
  • Digital Banking
  • Emerging Payments
  • Fraud & Security
  • Merchant
  • Prepaid
No Result
View All Result
PaymentsJournal
No Result
View All Result

IBM Helps Eliminate Bias in Facial Recognition Training, But Other Faults May Remain

By Tim Sloane
June 28, 2018
in Analysts Coverage
0
11
SHARES
0
VIEWS
Share on FacebookShare on TwitterShare on LinkedIn
IBM

IBM

In a blog yesterday IBM announced that it is releasing two new facial image datasets as part of an effort to establish machine learning training data that is unbiased relative to “. . .skin tones, genders, and ages . . .”:

“1) One of the biggest issues causing bias in the area of facial analysis is the lack of diverse data to train systems on. So, this fall, we intend to make publicly available the following dataset as a tool for the technology industry and research community:

  • A dataset of annotations for over 1 million images to improve the understanding of bias in facial analysis being built by IBM Research scientists. Images will be annotated with attributes, leveraging geo-tags from Flickr images to balance data from multiple countries and active learning tools to reduce sample selection bias. Currently, the largest facial attribute dataset available is 200,000 images so this new dataset with a million images will be a monumental improvement.

  • An annotation dataset for up to 36,000 images – equally distributed across skin tones, genders, and ages, annotated by IBM Research, to provide a more diverse dataset for people to use in the evaluation of their technologies. This will specifically help algorithm designers to identify and address bias in their facial analysis systems. The first step in addressing bias is to know there is a bias — and that is what this dataset will enable.”

A better dataset is certainly a step in the right direction but other issues can also impact the accuracy of facial recognition associated with skin tone and race. For example, the device’s combination of lens, sensor, lighting and angle, combined with skin tone, can impact what the final image for evaluation looks like. This suggests that if no standard for clarity is established some training may be needed using the images perceived by the device itself; a more complex and expensive effort.

Overview by Tim Sloane, VP, Payments Innovation at Mercator Advisory Group

 

11
SHARES
0
VIEWS
Share on FacebookShare on TwitterShare on LinkedIn
Tags: Facial RecognitionIBM

    Get the Latest News and Insights Delivered Daily

    Subscribe to the PaymentsJournal Newsletter for exclusive insight and data from Javelin Strategy & Research analysts and industry professionals.

    Must Reads

    supply chain payments

    The Payment Process: The Supply Chain’s Most Overlooked Cyber Risk

    July 17, 2025
    Navigating Global Fintech Regulations Through Strategic Regulatory Arbitrage

    Navigating Global Fintech Regulations Through Strategic Regulatory Arbitrage

    July 16, 2025
    AI Is Turning Accounts Receivable Into a Strategic Powerhouse

    AI Is Turning Accounts Receivable Into a Strategic Powerhouse

    July 15, 2025
    Embedded Finance

    Embedded Finance: Bringing Payments Under a Single Umbrella

    July 14, 2025
    Making Real-Time Payments a Reality

    Fulfilling the Promise: Making Real-Time Payments a Reality

    July 10, 2025
    mortgage

    The Rich Benefits of In-House Payment Systems

    July 9, 2025
    digital cards

    Beyond Plastic: Why Digital Cards Are the Future

    July 8, 2025
    What Premium Card Overhauls by Chase and Amex Reveal About the Credit Card Market

    What Premium Card Overhauls by Chase and Amex Reveal About the Credit Card Market

    July 7, 2025

    Linkedin-in X-twitter
    • Commercial
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Digital Banking
    • Commercial
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Digital Banking
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    • About Us
    • Advertise With Us
    • Sign Up for Our Newsletter
    • About Us
    • Advertise With Us
    • Sign Up for Our Newsletter

    ©2024 PaymentsJournal.com |  Terms of Use | Privacy Policy

    • Commercial Payments
    • Credit
    • Debit
    • Digital Assets & Crypto
    • Emerging Payments
    • Fraud & Security
    • Merchant
    • Prepaid
    No Result
    View All Result