Artificial intelligence (AI) bias is known to be AI’s ability to present biased results based on human biases that were incorporated in the training data or AI algorithm, leading to unfair and distorted outcomes. Bias in AI is the reflection of the broader biases that exist in society. As we increasingly rely on AI systems for decision-making in various domains and industries, there is a growing imperative to prevent biases and ensure fair outputs.
At IFS Unleashed 2024, a global customer conference that brings together business leaders, technology experts and industry innovators, a women’s leadership panel including Baker Tilly’s Cindy Bratel, led a discussion on how AI can exhibit bias. Data lies at the heart of AI bias. Historical data used to train AI models often reflects societal inequalities. One example of this emerged in the talent management space, where historical data reflected a societal bias favoring a single demographic, mostly men, for promotion due to traditional working patterns. The bias in the algorithm leads to skewed outcomes that disadvantage underrepresented groups. The challenge extends beyond gender bias and segments like economic conditions, geographical locations and age, significantly impacting AI decision-making.
The role of transparency and governance in preventing AI biases
Considering the biases we encounter across various sectors, the importance of transparency in AI algorithms cannot be overstated. As most industries continue adopting AI and digital systems continue becoming more complex, the need for training models to justify their decision-making becomes paramount. In sectors where decisions can impact an individual’s life, like banking and insurance sectors, it is essential to challenge the status quo while ensuring data models are assessed rigorously. This involves the broader context of the decisions that are being made by AI, such as the implications of lending money to individuals with specific financial history.
The solution lies in governance and continuous monitoring. If the biases are not caught within the systems, it can impact business growth and affect the ability of individuals to participate in the economy and society. Today’s organizations need robust frameworks to oversee the training models and deployment of AI in their systems. This would not mean slowing down innovation, which is crucial to ensure growth, but to bring a balance between governance and digital evolution.
