Attention in Machine Learning (ML) refers to a mechanism that allows a model to focus selectively on certain parts of the input data, rather than treating all elements equally. This helps the model prioritize the most relevant information for the task. For example, in the sentence “Cat on a mat,” the attention mechanism might focus on the words “cat” and “mat,” assigning them more weight, while giving less attention to the word “on.” By doing so, the model can better understand the meaning of the sentence.

This selective focus can also be used to ensure fairness and accountability in AI models. If the attention mechanism focuses disproportionately on certain features, such as gender or race, it may indicate bias in the model’s decision-making process. Recognizing this allows developers to retrain or adjust the model to address such biases.

Attention mechanisms are crucial for enhancing the transparency of AI systems, especially in high-risk areas like healthcare, criminal justice, and law. By revealing which features the model prioritizes, attention helps stakeholders understand how decisions are made, making the AI more interpretable. This increased transparency supports the development of responsible AI systems that are fair, ethical, and aligned with sociel values, ensuring that they operate with accountability and transparency.

Source

“Attention is All You Need” by Vaswani et al. (2017)

“Fairness and Abstraction in Sociotechnical Systems” by Selbst et al. (2019)