AI Risks | Strategy | Project Management | Bias | Fairness

Navigating the Bias Blind Spot

The AI Risks series

This series of 23 articles examines the set of AI risks identified by the Massachusetts Institute of Technology (MIT), to offer actionable strategies to help organizations mitigate their potential pitfalls.

Unfair representation and discrimination

As humans program AI systems and provide training data, the inherent biases present in these inputs can lead to the creation of AI models that perpetuate and exacerbate discriminatory practices and beliefs.

Image by Midjourney, concept and prompt by Fabio Turel

Potential ripple effects and secondary risks

The risk of unfair discrimination and misrepresentation in AI can lead to a range of secondary risks and ripple effects, including:

• Deepening of existing social and economic inequalities: the reinforcement of harmful stereotypes and the perpetuation of discriminatory practices through AI can exacerbate the marginalization of certain groups, widening the gap between advantaged and disadvantaged populations and contribute to the escalation of social and political tensions.
• Erosion of public trust: when AI systems are perceived as biased, it…