Algorithmic biases refer to the unfair and unintended favoritism embedded in computer programs. Imagine these programs as decision-making tools, like sorting through resumes or recommending products. The trouble arises when the data used to train these algorithms contains hidden biases. For instance, if historical hiring data shows a preference for certain demographics, the algorithm learns and replicates these biases, perpetuating inequality. It's not the fault of the computer but rather a reflection of societal prejudices present in the data it learns from.

The impact of algorithmic biases can be significant, leading to discrimination in various domains, from lending and hiring to criminal justice. When designers create algorithms without careful consideration for bias, the algorithms can reinforce and even exacerbate existing social inequalities. It's crucial to address these biases systematically, focusing on both the data used for training and the design of algorithms themselves. By ensuring that the process is transparent, inclusive, and continuously monitored, we can strive to create algorithms that are fair, just, and free from unintended discrimination.