Bias & Accountability
Addressing bias and ensuring fairness in AI systems is a practical and moral duty.
Mitigating bias helps build trust, comply with regulations, avoid harm, and enhance the overall performance and impact of AI technologies. While addressing biases in AI is something all responsible developers, companies, and policymakers can and should be doing, you can contribute to a collective effort to promote fair and unbiased AI practices by staying informed and being proactive.
What Makes an AI System Biased?
Imagine you have a robot friend who helps you decide what movie to watch based on your preferences. This robot learns from the movies you’ve liked before and suggests new ones it thinks you’ll enjoy.
Now, think about that same robot learning from lots of friends, not just you. If most of the people it learns from are fans of action movies, the robot might become especially good at suggesting action movies but not so great at recommending romantic comedies or crime dramas.
That doesn’t sound too harmful until you consider AI is being used for far more than Netflix recommendations. AI biases can lead to unfair decisions in real-world applications, such as with hiring and lending. Biased AI systems can create feedback loops that perpetuate and amplify existing biases. For example, biased recommendation systems can reinforce stereotypes and filter out diverse perspectives. Addressing bias is crucial to breaking these feedback loops.
How to protect yourself and others from biased AI systems
For tips on how to address bias in ChatGPT, check out OpenAI’s Educator FAQ “Is ChatGPT Biased?”.