No quick solutions exist; fairness itself remains hard to define precisely.
There’s no easy fix coming. Beyond technical difficulties, researchers struggle with defining what is fair. We need tools to check for biases and missing data in our models. We also need the right incentives to check for drift and update algorithms as society changes. Drift happens when datasets become biased over time even if initially unbiased, because society and beliefs evolve. Machine learning and AI will be powerful forces for good, but only if the training data is correct and fair.
— It’s The Data, Stupid! Why AI Might Get It Wrong. · Forbes