This is part 13 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.
View all posts in this series or explore the Crossfactors Framework
Few AI algorithms are perfect. Algorithmic Bias is the degree to which an algorithm systematically deviates from such an ideal.
It’s concept #13 in my series on 101 Ways to Screw Things Up with AI.
What is it
Algorithmic bias describes the tendencies of an algorithm to produce results that are systematically skewed or unbalanced. It is distinct, but often related, to data bias.
Why It Matters
Decisions influenced by algorithmic bias can be harmful as they may perpetuate discrimination or inequality. They may be difficult to detect and address. Not all algorithmic bias result in ethical concerns. Algorithmic design decisions can also lead to algorithmic bias, independently from data bias.
Real-World Examples
In 2019, Apple was accused of gender bias when its Apple Card algorithm systemically offered lower credit to women when compared to men of the same financial means, even for cases where spouses shared the same assets.
Key Dimensions
It’s important to remember that not all algorithmic bias is not always caused by data.
Abstraction bias - lumping categories or feature engineering can lead to bias.
Temporal bias - rare events can cause bias when the model assumes that stable conditions will persist.
Interaction bias - ascribing frequency to correctness.
Threshold bias - when thresholds on a measured quantity results in a binary decision.
Take-away
Algorithmic bias can quickly lead to harmful outcomes, but data bias is not always the cause.