How to Control AI Bias

Are You Biased? Well, join the club! I engage in "wishful thinking" look for "social proof" and I may be susceptible to the occasional "halo effect" 😇 And it turns out there are around 188 different cognitive biases that can sneak into our thoughts every day. So with odds like that it's no surprise our AI systems might pick up a bad habit or two! AI is like a living thing - it learns & changes, and that means new biases can appear after the system is up and running.

Human bias is part of our nature, often keeping us safe, happy, and occasionally a bit self-delusional, which isn't necessarily harmful! So, our biases are best left alone - we're not for changing! But AI bias is a different story altogether because it can scale these biases across millions of decisions, affecting fairness, equality, and trust in critical systems leading to unintended consequences that impact society on a large scale.

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🔴 Key Challenge to Controlling AI Bias

One of the challenges in controlling bias is the shift from static to dynamic bias detection. AI systems are no longer static as they evolve through continuous learning. The key questions for developers & compliance professionals is how can we design AI systems that not only detect biases at the development stage but also continuously monitor and correct biases as they learn &adapt in real-time implementations?

📍 1. The first key aspect is continuous monitoring.Think of it like checking your GPS regularly to make sure you're still on the right path. Regularly checking AI systems helps us catch biases early and correct them before they cause harm. For example, in hiring, if an AI starts to favor certain types of resumes over time, continuous monitoring can catch this early.

📍2. Another critical element is using feedback loops. Imagine your GPS recalculating if you make a wrong turn. Feedback loops help AI adjust and fix biases using real-world human feedback. This is important because some biases aren't obvious in the data but show up when the AI is being used in real life situations.

📍3. Real-time data analysis is importantIt's like checking the weather forecast throughout the day not just once in the morning. AI needs to process and analyze data constantly to detect bias patterns. For example in healthcare, AI systems should continuously analyze patient data to ensure they're treating all patients fairly.

📍4. Data strategy includes diverse data and regular audits is key. This is like giving your AI a balanced diet and regular check-ups. Training AI on diverse data helps reduce bias and regular audits ensure the system stays aligned with ethical standards as society evolves.Critical systems in health, education, finance, etc. are moving to dynamic bias detection which is critical. Its like making sure your GPS doesn’t send you into a lake - we all need to keep an eye on AI to make sure it stays on the right path. hashtag#AIBiashashtag#RAIhashtag#BiasMonitoringhashtag#Orcawise

Originally published on LinkedIn:

https://www.linkedin.com/posts/kevinneary_aibias-rai-biasmonitoring-activity-7233112832869830656-fGjA?utm_source=share&utm_medium=member_desktop

KEVIN NEARY

Entrepreneur and speaker working on ethical and sustainable AI practices.

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