Avoiding AI Bias Amplification: 4 Actions You Can Take
Image source: Ideogram
This article explores the nature of the current AI models and how the humans behind it shaped into being, as well as 4 actionable steps we can take as users of the AI.
As a summary: bias is not merely a technological flaw; it is, rather, an echo of human cognition that resonates throughout various systems. Our tendency to stereotype and label individuals is hardwired into the intricate ways we process information. Consequently, when algorithms are trained on human-generated data, these inherent tendencies are unfortunately replicated in their outputs. AI systems, including generative models, inevitably inherit the same blind spots and biases as their creators, posing significant ethical implications. Making this issue even more challenging and preoccupying is the use of training data that is frequently not representative of the wider population. This data often contains historical inequalities or societal stereotypes, perpetuating injustice and reinforcing outdated notions in the technology we rely upon.
