The current state of AI development is characterized by a lack of transparency and accountability, with users expected to trust complex systems that are not easily understood. Deep learning models are developed in secret, relying on opaque datasets and vague documentation, creating a legal and ethical quagmire. Insufficient attention to ethical design results in a 'train now, apologize later' culture that threatens innovation. Emphasizing open source practices by making code, model parameters, and training data openly available is vital for fostering innovation and ensuring alignment with human values while addressing systemic biases present in AI.
Today, most AI is being built on blind faith inside of black boxes. It requires users to have on unquestioning belief in something neither transparent nor understandable.
The industry is moving at warp speed, employing deep learning to tackle every problem, training on datasets that few people can trace, and hoping no one gets sued.
We don't need more hype. We need systems where ethical design is foundational.
To some extent, the entire process of training is nothing but computing the billions of micro-biases that align with the contents of the training dataset.
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