Enterprise use of foundation models and bias remediation
It is crucial for companies to avoid using biased models in various industries, as this can result in harm, unethical decisions, and legal and reputational risks. Biases can be introduced in AI models at different stages of the ML pipeline, including data collection, preprocessing, model training, and evaluation. To ensure that foundation models are suitable for enterprise use, companies can take various measures, such as implementing fair data practices, including diverse representation in data, regular model monitoring, and audit trails. Fine-tuning and customizing foundation models to specific use cases through prompt engineering can reduce bias and improve accuracy. Moreover, companies should adopt responsible AI principles and undergo ongoing education and training to ensure the ethical and unbiased deployment of AI systems.
To reduce bias in foundation models, there are several techniques and approaches that can be used...
