Explanation
Imagine training a sniffer dog to only find a specific type of truffle. Even if other equally delicious truffles are present, the dog will ignore them, focusing solely on what it has been trained to seek.
AI bias is similar. It arises when AI systems make consistently unfair or discriminatory decisions due to flawed data or algorithms. This means the AI favours one outcome over another without a valid reason.
This bias can creep in at any stage, from the initial data collection to the algorithm design and even the way the results are interpreted. Often, it happens unintentionally, reflecting existing societal biases or simply the limitations of the data used to train the AI.
AI Bias has the potential to perpetuate societal inequalities at scale and with great speed.
Addressing AI bias is crucial for ensuring fairness, accuracy, and ethical AI deployment.
Examples
Consumer Example
Consider a facial recognition system that struggles to accurately identify individuals with darker skin tones.
This happens because the AI was primarily trained on datasets featuring lighter skin tones, creating a bias that affects its performance for certain demographic groups.
This illustrates how AI bias can lead to real-world discrimination and exclusion.
Business Example
Imagine a recruitment AI that consistently favours male candidates over female candidates for technical roles.
This could stem from historical hiring data reflecting a male-dominated workforce, leading the AI to perpetuate this imbalance.
Such bias can result in a less diverse and innovative workforce, ultimately impacting the company's success. Businesses need to be aware that they might be breaking anti-discrimination laws with biased AI.