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
Why is machine learning important?
Machine Learning (ML) is a pivotal subset of Artificial Intelligence (AI) that has garnered significant attention and investment due to its transformative potential across various sectors. Its importance is underscored by its ability to enable systems to learn from data, identify patterns, and make decisions with minimal human intervention. This capability is particularly important in
What are the key ethical considerations and potential risks associated with the deployment of advanced machine learning models in real-world applications?
The deployment of advanced machine learning models in real-world applications necessitates a rigorous examination of the ethical considerations and potential risks involved. This analysis is important in ensuring that these powerful technologies are used responsibly and do not inadvertently cause harm. The ethical considerations can be broadly categorized into issues related to bias and fairness,
- Published in Artificial Intelligence, EITC/AI/ADL Advanced Deep Learning, Responsible innovation, Responsible innovation and artificial intelligence, Examination review