Data Science

Erasing bias in emerging technologies – 3 considerations

Ribbons of data around a cuboid frame: Human input into artificial intelligence processes can lead to data and algorithmic bias.

Human input into artificial intelligence processes can lead to data and algorithmic bias. Image: Unsplash/DeepMind

Ivor Horn
Chief Health Equity Officer, Google
Kulleni Gebreyes
US Consulting Health Care Sector Leader and Chief Health Equity Officer, Deloitte
Share:
The Big Picture
Explore and monitor how Data Science is affecting economies, industries and global issues
A hand holding a looking glass by a lake
Crowdsource Innovation
Get involved with our crowdsourced digital platform to deliver impact at scale
Stay up to date:

Data Science

Listen to the article

Have you read?

Don't miss any update on this topic

Create a free account and access your personalized content collection with our latest publications and analyses.

License and Republishing

World Economic Forum articles may be republished in accordance with the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License, and in accordance with our Terms of Use.

The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:
Data ScienceArtificial IntelligenceEmerging TechnologiesInequality
Share:
World Economic Forum logo
Global Agenda

The Agenda Weekly

A weekly update of the most important issues driving the global agenda

Subscribe today

You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy.

Air pollution monitors may be a goldmine for biodiversity data. Here's how

Orla Dwyer

June 8, 2023

1:26

About Us

Events

Media

Partners & Members

  • Join Us

Language Editions

Privacy Policy & Terms of Service

© 2023 World Economic Forum