Which practices are essential for selecting datasets to ensure responsible AI? (Choose two.)

Balancing datasets helps prevent bias by ensuring that no particular group or characteristic is disproportionately represented, which can lead to fairer and more accurate model predictions across different demographics.

Prioritizing large datasets without considering representativeness can exacerbate existing biases within the data, leading to models that perform poorly or unfairly for underrepresented groups.

While open-source datasets can be valuable, restricting dataset usage to *only* open-source options is not a primary practice for ensuring responsible AI, as proprietary datasets can also be ethically sourced and curated.

Ensuring inclusivity and diversity in dataset sources means gathering data from a wide range of individuals, contexts, and scenarios to accurately reflect the real world, thereby reducing the risk of bias and improving model generalizability.