Published 20 Jun 2024 8 minutes read
Last Updated 09 Dec 2024

Fairness and Bias in Artificial Intelligence Systems

From bias in AI models to ethical concerns in algorithm development, learn how AI makes and addresses these critical issues.

General

As artificial intelligence (AI) continues to weave itself into the fabric of our daily lives, the concepts of fairness and bias in Artificial Intelligence systems have come to the forefront of discussions. From finance to healthcare, the stakes are high as we rely more on algorithms to make critical decisions. This piece will dive into the complexity of fairness and bias in artificial intelligence, providing a clear understanding while addressing crucial points such as why fairness in AI systems is crucial and how we can work towards achieving it.

What Is Fairness in AI Systems?

Definition of Fairness: Key Concepts

When it comes to fairness in AI, the term “fairness” can be a bit slippery to define universally. Broadly, fairness means the impartial and just treatment or behavior without favoritism or discrimination. In the context of AI, fairness definitions often revolve around ensuring that AI systems do not produce outcomes that disadvantage any group unjustly. For instance, the Conference on Fairness, Accountability, and Transparency (FAccT) lays down various criteria to define and measure fairness. However striking a balance between different fairness definitions can be challenging, given the nuanced nature of fairness itself.

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