Did you know that a recent study by McKinsey & Company highlighted that 84% of organizations are concerned about bias in their AI algorithms? However, there's a solution to this problem. Upholding best practices can significantly mitigate biases in AI for enterprises, particularly given the challenges posed by compliance and the rapid dissemination of information through digital media.
In this E42 Blog post, we delve into an array of best practices to mitigate bias and hallucinations in AI models. A few of these best practices include:
Model optimization: This practice focuses on enhancing model performance and reducing bias through various optimization techniques
Understanding model architecture: This involves a deep dive into the structure of AI models to identify and rectify biases
Human interactions: This emphasizes on the critical role of human feedback in the training loop in ensuring unbiased AI outcomes
On-premises large language models: This practice involves utilizing on-premises LLMs to maintain control over data and model training

Did you know that a recent study by McKinsey & Company highlighted that 84% of organizations are concerned about bias in their AI algorithms? However, there's a solution to this problem. Upholding best practices can significantly mitigate biases in AI for enterprises, particularly given the challenges posed by compliance and the rapid dissemination of information through digital media. 

In this E42 Blog post, we delve into an array of best practices to mitigate bias and hallucinations in AI models. A few of these best practices include: 

Model optimization: This practice focuses on enhancing model performance and reducing bias through various optimization techniques 
Understanding model architecture: This involves a deep dive into the structure of AI models to identify and rectify biases 
Human interactions: This emphasizes on the critical role of human feedback in the training loop in ensuring unbiased AI outcomes 
On-premises large language models: This practice involves utilizing on-premises LLMs to maintain control over data and model training