By Anna Islamova

June 25, 2024 | minute read

The issue of AI bias isn’t just a moral quandary—it can be costly for businesses, leading to revenue losses, customer attrition, employee turnover, legal expenses, and damage to reputation.

A 2021 survey of 350 IT leaders revealed that 62% of companies experiencing AI bias reported losing revenue, while 61% reported losing customers.

Mitigating bias starts with understanding its sources: data, algorithms, human factors, and context. If left unchecked, these biases can create poor outcomes. The primary categories for introducing AI bias include:

  • Data Bias: High-quality, diverse data is essential. For example, cancer diagnosis data can be influenced by a patient’s skin tone, leading to inaccurate results.
  • Algorithmic Bias: Flawed algorithms can favor certain groups unfairly. Amazon’s AI recruiting tool, for example, favored men over women for technical roles. Many AI models are ‘black boxes,’ with complex algorithms that aren’t transparent.
  • Human Factor: Historical data contains biases, and developers can unintentionally embed their own biases into AI systems. Decisions about which problems to solve and how to interpret AI predictions contribute to bias.
  • Context of Usage: Using AI models in contexts they weren’t designed for can amplify biases. For instance, a credit scoring model from one country might not work fairly in another without considering local economic conditions.

AI can process data and identify patterns, but it doesn’t understand the human impact of its decisions. Technical fixes aren’t enough; human judgment and ethics are crucial, as is forming an AI Ethics Committee or similar oversight group within an organization.

Forbes Counsel Member Satyen Sangani says, “Companies need to train anyone who touches data—whether they’re managing it, producing it or consuming it—to be aware of introducing bias.”

Furthermore, says Mr. Sangani in his Forbes op-ed entitled, Curbing Unconscious Bias In AI, “This is no different from training scientists to avoid procedural bias when designing an experiment. The challenge with most AI is that the real world is rife with unpredictable and confounding variables.”

Mr. Sangani believes that companies that put in place checks and balances such as monitoring both inputs and outputs will benefit from the potential of AI without falling prey to hidden bias. This will be incumbent of visionary leaders who see the opportunity to ensure AI builds an inclusive, ethical, and safe world.