Algorithmic bias

In today’s digital age, where artificial intelligence (AI) and machine learning are becoming integral to societal functions, the phenomenon of algorithmic bias emerges as a major concern.

At its core, algorithmic bias is a reflection of systemic errors in the algorithm’s outcomes, originating from various sources such as biased training data, flawed feature selection, and even the very design of the algorithm itself. For instance, if an AI system is trained on historical data that inadvertently favors a particular demographic due to past prejudices, the algorithm will likely reproduce and amplify those biases, leading to skewed and potentially harmful results.

This concern escalates significantly when biased algorithms find applications in public domains. When public authorities utilize these AI tools, especially in critical decision-making scenarios, the embedded biases can perpetuate existing inequalities, undermining the very essence of fairness and justice. An example that brings this challenge to light is predictive policing, where algorithms predict crime patterns based on historical data. But if this data is rooted in biased policing practices of the past, the algorithms can further entrench these biases, leading to a vicious cycle of prejudice and discrimination.