Absolutely! Machine learning models can inherit biases from the training data, which can lead to unfair or discriminatory outcomes. To ensure fairness, we can apply techniques like data augmentation, reweighting, or regularization to balance the representation of different groups in the dataset. Additionally, evaluating and auditing models for bias, and making adjustments as necessary, can help promote fairness in the decision-making process.

Yes, machine learning models can exhibit biases. These biases can arise from biased training data or algorithms that unintentionally capture and perpetuate biases present in society. Addressing biases requires a multi-faceted approach, including careful dataset curation to minimize bias, algorithmic improvements to reduce biased decision-making, and transparency in model evaluation and deployment. It's crucial that we proactively work towards understanding and mitigating biases to ensure fair and equitable machine learning systems.