Hold My Algorithm!

Today I had a lovely Chatty Chat with CGPT, I call it CG for short, and they use it/they pronouns, just so you know!

So I asked, CG what are ways to reduce bias in Generative AI. Here is where the convo took us...

1. One way to reduce bias in generative AI is to ensure that the training data used to develop the model is diverse and representative of the real-world population. This includes making sure that the data includes samples from a wide range of races, genders, ages, and backgrounds. Looking closely at the AI team, it needs to be composed of all dimensions of diversity and lenses. Generative AI is designed to be inclusive and equitable for all users.
2. Pre-processing techniques such as data augmentation and cleaning can help reduce bias in generative AI. Data augmentation involves creating additional training examples from existing data, which can help to create a more diverse and representative dataset. Data cleaning involves identifying and removing any problematic or biased data points from the training set.
3. Fairness constraints can be incorporated into the generative AI model to ensure that the output is fair and unbiased. For example, constraints can be placed on the output to ensure that it does not contain any discriminatory language or stereotypes.
4. Algorithmic auditing involves analyzing the output of the generative AI model to identify any biases or unfairness. This can help to identify any problematic areas in the model and allow for improvements to be made.
5. Regularization techniques can be used to penalize the generative AI model for producing biased output. This encourages the model to produce output that is more fair and unbiased. Generative AI models can be audited regularly to ensure that they remain unbiased and fair over time. This can involve monitoring the model's outputs and adjusting the training data or algorithm as needed to prevent bias from creeping in.

Even though there is no single solution to weed out bias in generative AI, it requires a combination of techniques and methods to ensure that the system is trained on unbiased data and produces unbiased results. We need to continue bringing STEM education and its access to all dimensions of diversities and intersectionalities. Progress and innovation do not live in homogeny.