Introduction
As companies increasingly adopt generative AI technologies, the need for effective governance mechanisms becomes paramount. These technologies, while transformative, are not without risks – the most significant being AI bias.
AI Bias: A Silent Threat
AI bias refers to the systematic errors that AI systems make when making predictions or decisions due to the prejudiced assumptions made during their training. This bias can lead to unfair outcomes, affecting individuals and groups disproportionately.
“AI bias is the silent threat in the room. It’s not just about inaccurate predictions; it’s about fairness, justice, and equality.” – Armel Néné
The Risks of AI Bias in Companies
AI bias can have far-reaching implications for companies. It can lead to skewed decision-making, resulting in unfair treatment of certain groups. This can harm a company’s reputation, lead to legal repercussions, and even impact the bottom line.
Real-world Examples of AI Bias
AI bias is not a theoretical concept; it manifests in real-world applications. For instance, Amazon trained an AI bot to crawl the web to find candidates for IT jobs using the resumes of its existing staff, which was overwhelmingly male. The application “learned” that males make the best IT employees⁵. Similarly, commercial facial-recognition systems that were trained overwhelmingly with light-skinned images badly misclassified dark-skinned subjects⁵.
Testing for AI Bias: An Essential Step
Testing for AI bias is a crucial part of AI governance. Companies can use a portfolio of technical tools and operational practices such as internal “red teams,” or third-party audits¹. They can also engage in fact-based conversations around potential human biases¹. This could take the form of running algorithms alongside human decision makers, comparing results, and using “explainability techniques” that help pinpoint what led the model to reach a decision¹.
“Governing AI isn’t just about compliance; it’s about earning trust. In the age of AI, trust is the new currency.” – Ruth Néné
Conclusion
As we navigate the AI revolution, governing generative AI in companies is no longer optional. It’s a strategic imperative. By understanding and mitigating the risks of AI bias, companies can harness the power of AI while ensuring fairness and transparency.
(1) Shedding light on AI bias with real world examples – IBM Blog.
(2) What Do We Do About the Biases in AI? – Harvard Business Review.
(3) Testing for bias in your AI software: Why it’s needed, how to do it.
(4) What do we do about the biases in AI? | McKinsey – McKinsey & Company.
(5) Bias in AI: What it is, Types, Examples & 6 Ways to Fix it in 2024.
(6) Research shows AI is often biased. Here’s how to make algorithms work ….
(7) AI Bias Examples: From Ageism to Racism and Mitigation Strategies.
(8) What is AI bias? [+ Data] – HubSpot Blog.
(9) There’s More to AI Bias Than Biased Data, NIST Report Highlights.
