When cleaning the data, how can one ensure the data is not biased?
Ensuring that data cleaning processes are free from bias is a critical concern in the field of machine learning, particularly when utilizing platforms such as Google Cloud Machine Learning. Bias during data cleaning can lead to skewed models, which in turn can produce inaccurate or unfair predictions. Addressing this issue requires a multifaceted approach encompassing
In what ways can biases in machine learning models, such as those found in language generation systems like GPT-2, perpetuate societal prejudices, and what measures can be taken to mitigate these biases?
Biases in machine learning models, particularly in language generation systems like GPT-2, can significantly perpetuate societal prejudices. These biases often stem from the data used to train these models, which can reflect existing societal stereotypes and inequalities. When such biases are embedded in machine learning algorithms, they can manifest in various ways, leading to the
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Responsible innovation, Responsible innovation and artificial intelligence, Examination review