Bias and discrimination in AI can lead to what major issue?

Bias and discrimination in AI can lead to negative societal impacts and unfair outcomes because algorithms that are trained on biased data may perpetuate or even exacerbate existing inequalities. When AI systems reflect biases present in the training data, they can generate results that disadvantage certain groups based on race, gender, age, or other characteristics. This can lead to discriminatory practices in various fields, including hiring, law enforcement, and lending, resulting in unfair treatment of individuals.

The repercussions of such biased AI systems extend beyond the individuals directly affected; they can also undermine public trust in technology and institutions, leading to broader societal consequences. By recognizing the potential for negative outcomes, organizations can work towards more equitable and just applications of AI, ensuring that systems are developed with fairness in mind.

The other outcomes listed do not accurately capture the relationship between bias and discrimination in AI with societal implications, as they suggest improvements or efficiency that do not consider ethical ramifications.