Responsible AI Guidelines

AI systems can cause a variety of fairness-related harms, including harms involving people’s individual experiences with AI systems or the ways that AI systems represent the groups to which they belong. Prioritizing fairness in AI systems is a sociotechnical challenge.
Responsible AI Guidelines suggest the best way to build fairness, interpretability, privacy, and security into these systems.


1. Error Analysis:

Error analysis is a fairly very important steps in the whole ML lifecycle, this helps in identifying the behaviour of model and also correct it as needed at different level.
Error could be because of many reason, let’s start with input

Input/Data: Accelerated framework, helps to identify if there is any error associated with data by checking

• Missing data
• Ensuring appropriate train and test split
• Augmenting data

Feature Selection:
Another level of error may occur because of inappropriate feature selection or data leak

• Accelerated ML aims to determine data leak by applying different framework which are common in practice (Yet to be implemented)
• It also aims to determine outliers so because of outliers the model should not get impacted (Yet to be implemented)
• Accelerated ML also aims to determine model drift which is basically change in the data distribution of train and test on production, which may result into wrong prediction (yet to be implemented)

Others:

Other area where the model could go wrong would be performance metric, basically which performance metric is good for what scenario, e.g. if we have imbalance data then simple accuracy as performance metric might not be good as this might favor the dominant class if our model is simply predicting dominant class as class label.

Also Accelerated ML, aims to compare baseline model with new model to determine if the new model is better than base model or not. (yet to be implemented)