Logo: IBM

The Ethics Of AI: How To Avoid Harmful Bias And Discrimination

While the potential existential threat of artificial intelligence is well publicized, the threat of biased machine learning models is much more immediate. Customer insights (CI) pros must learn how to identify and prevent harmful discrimination in their models, or businesses will suffer reputational, regulatory, and revenue consequences.

Biased Models Are Bad For Businesses
Most harmful discrimination is unintentional, but that won’t stop regulators from imposing fines or values-based consumers from taking their business elsewhere.

Data Determines Discrimination
CI pros must defend against algorithmic or human bias seeping into their models while cultivating the helpful bias these models identify to differentiate between customers.

Ensure Your Models Are FAIR
CI pros should strive to create models that are fundamentally sound, assessable, inclusive, and reversible to protect against harmful bias.

Download now!

You can withdraw your marketing consent at any time by sending an email to NETSUPP@us.ibm.com. Also you may unsubscribe from receiving marketing emails from IBM by clicking the unsubscribe link in each such email.
More information on IBM processing of your personal data can be found in the IBM Privacy Statement. By submitting this form, I acknowledge that I have read and understand the IBM Privacy Statement.

Your e-mail address is used to communicate with you about your registration, related products and services, and offers from select vendors. Refer to our Privacy Policy for additional information.