Algorithmic Bias and Explainability: Two Sides of the Same Coin

 

Article 3: Algorithmic Bias and Explainability: Two Sides of the Same Coin

Introduction: The Bias Blind Spot

Algorithms do not exist in a vacuum. They learn from data — and data reflects society, with all its inequalities. This creates algorithmic bias, when AI outputs reflect unfair stereotypes or systemic discrimination.

But bias can only be identified and mitigated if we have explainability. In other words: you cannot fix what you cannot see.

Defining Bias and Explainability Together

  • Algorithmic Bias: Systematic errors in AI decision-making that disadvantage individuals or groups.

  • Explainability: The ability to understand and articulate the reasoning behind algorithmic outcomes.

They are two sides of the same coin: bias is the disease, explainability is the diagnostic tool.

Case Study: Amazon’s Hiring Algorithm

In 2018, Amazon scrapped an AI recruitment tool after it was found to downgrade résumés that included the word “women’s” (e.g., “women’s chess club captain”). Why? The algorithm had been trained on past hiring data dominated by men.

Without explainability, this bias might have gone undetected. Transparency allowed Amazon to identify the flaw and shut down the system.

Why Bias and Explainability Are Intertwined

  1. Detection: Explainability reveals patterns of discrimination.

  2. Correction: Bias can be reduced through retraining, dataset balancing, or algorithm redesign.

  3. Trust: Public confidence in AI depends on visible fairness.

Critical Thinking Lens

Ask: If an AI system consistently disadvantages one group, but no one can explain why, is it ethical to keep using it?

This frames explainability not only as a technical safeguard but as a moral obligation.

Governance Implications

Counterpoint: The “Neutral AI” Myth

Some argue algorithms are neutral. But as Cathy O’Neil, author of Weapons of Math Destruction, famously noted: “Algorithms are opinions embedded in code.” Explainability makes those opinions visible.

Takeaway

Algorithmic bias and explainability cannot be separated. Without transparency, bias hides. With transparency, fairness becomes possible.

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