Authored by Cindy M. Bratel
CHICAGO, IL / ACCESSWIRE / January 15, 2025 / 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.
How can businesses avoid bias?
The path forward requires continuous vigilance. Organizations must be prepared to pause or reevaluate AI systems that do not align with ethical standards or business values. Regular testing and monitoring should be encouraged as AI evolves and context changes over time.
Building diverse teams can emerge as a game changer in developing unbiased AI solutions. The focus on building such a team requires a shift from purely technical considerations to include broader skill sets and perspectives. This approach ensures that AI systems are developed with consideration for potentially diverse users and their needs.
Education and awareness at all levels within an organization are crucial in addressing bias. One of the ways to do this is by implementing mandatory training programs for employees as well as leadership teams. There is a growing recognition that sector knowledge combined with AI competency promotes ethical and diverse solutions.
How can Baker Tilly help
IFS Cloud integrates AI technology to help businesses like yours unlock new opportunities by turning data into smarter, faster and more reliable decisions. Baker Tilly is proud to be the No. 1 Top 20 - IFS Certified Partner. The team of Value Architects believes an 'industry-first' approach allows us to deliver maximum value to our customers with efficient and uncomplicated IFS solutions. Contact a Baker Tilly specialist to learn more.
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SOURCE: Baker Tilly
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