Unraveling AI Bias: Principles & Practices

Generative AI offers numerous benefits, including increased productivity and faster time-to-value, but also presents risks like bias, which can perpetuate societal inequalities. This podcast focuses on understanding different types of AI bias, outlining principles for avoiding it, and exploring methods to develop bias-free AI systems, including utilizing AI governance, balanced AI teams, and ongoing monitoring.

AI Bias Types

• 00:01:58 AI bias, or machine learning bias, can be algorithmic, where systems automatically produce unfair outcomes, or cognitive, reflecting human biases built into the system design. Examples include age bias in loan applications and recency bias impacting decision-making.

AI Governance

• 00:07:13 AI governance encompasses policies, practices, and frameworks to direct, manage, and monitor AI activities, ensuring responsible AI development and promoting fairness, equity, and inclusion. This approach safeguards customers, consumers, employees, and the enterprise from harmful biases.

Model Selection

• 00:08:41 When selecting learning models, especially between supervised and unsupervised models, stakeholders' diversity is key for supervised models. For unsupervised learning, tools like Google's AI Fairness 360 or IBM's OpenScale can help identify bias automatically.

Balanced AI Team

• 00:10:28 Creating a balanced AI team with members from diverse backgrounds, including innovators, creators, and consumers, is essential to ensure bias-free decision-making across all stages of AI development. This includes data selection and algorithm selection.

Data Processing

• 00:11:19 Bias can creep in during data processing stages such as pre-processing, inline processing, and post-processing, even when the initial data is bias-free. Constant awareness and careful consideration of these stages are necessary to prevent bias.