The complexity of AI systems necessitates a nuanced understanding of their layers, addressing safety and ethical alignment with human values throughout the AI lifecycle.
To build AI systems that are accurate and fair, ensuring the quality, diversity, and fairness of training data is a foundational step crucial for reliability.
Transparency in algorithm tuning and decision-making processes is essential to prevent the 'black box' phenomenon in AI systems, allowing for continuous improvements and accountability.
The accuracy of AI inferences is vital, as inaccurate outputs can lead to significant errors and harmful consequences, thus emphasizing the importance of reliability in AI systems.
Collection
[
|
...
]