Explaining An Individual Prediction

A flowchart connecting questions about explaining individual AI predictions to two main techniques: Feature Attributions for detailed insights, leading to impacts on Model and Prediction Transparency; and Counterfactuals for simpler explanations, leading to potential impacts on Recourse and Human-in-the-loop Decision Making (though utilizing feature attributions can potentially lead to these impacts as well). The chart indicates the alignment of each technique with specific needs for clarity and understanding in AI predictions.
      Diagram created using Mermaid.js code written by Areal Tal, 2023.

 

Are you able to clearly explain to stakeholders the rationale behind how predictions are made?
When you encounter an unexpected prediction, are you able to easily trace the reasoning that led to the prediction?

What

Discover the main influences on a single prediction and how sensitive the model's output is to changes in each feature.

How

Local Attributions

Techniques like LIME and SHAP highlight the significance of each feature in predictions.

Counterfactual Explanations

Demonstrate how minor adjustments in inputs could alter model outcomes, offering insight into decision sensitivity.

                                       Image created by Ari Tal using an iPython Notebook, 2024

When

This section outlines key scenarios where it is useful to explain AI predictions:

  • High-stakes Decisions: When outcomes significantly affect lives such as resource allocation or legal implications.
  • Enabling Human-in-the-loop Decision-Making: When the user needs to understand why a prediction was made in order to act.
  • Incorrect/Unexpected Predictions: When uncovering reasons behind incorrect or unexpected outcomes.
  • Fairness Checks: Explaining a prediction can be useful in checking that the model is using proper rationale - i.e. that the model is not biased, is not using inappropriate features, and is not using features inappropriately. Remember, explaining AI decisions is just one part of validating fairness. It helps spot potential issues, but it's not enough by itself (Deck, 2023).
  • Non-technical Stakeholder Communication: Due to the fact that counterfactuals are easy to understand, they are suitable for presenting to stakeholders regardless of their technical literacy.

 

However, it might not always be necessary to delve into predictions:

  • Low Impact Predictions: When predictions have minimal consequences.
  • No Human Interaction: In situations where the prediction integrates seamlessly into another system, like many product recommendations.

Potential Impacts

  • Model Behavior Insight: Unraveling individual predictions offers insights into the larger picture of overall model behavior. Especially for unexpected but correct predictions, understanding the rationale aids developers in deciphering model intricacies.
  • Error Rectification and Model Debugging: Grasping the logic behind incorrect predictions can direct developers towards necessary model modifications.
  • End-User Clarity: Transparency in specific decisions caters to end-users' needs to discern the model's reasoning.
  • Human-AI Collaboration: Arming users with prediction rationale that can guide more informed actions (by allowing the users to utilize both the output and the rationale in making decisions rather than just arming them with the output). This can potentially lead to a corrected prediction or other improved results.
  • Recourse: Identify strategies for users to alter undesired outcomes and rectify incorrect outcomes. Counterfactual explanations are particularly useful for this.
  • Accountability: By making the AI's decision-making process transparent, we can help ensure that it can be held to defined standards. This promotes unbiased decisions by allowing others to confirm the model isn't using inappropriate reasoning. And in turn, this increased transparency leads to stakeholders being able to trust that the decision was made for the right reasons.

Transition to Next Section

Through local attributions and counterfactual explanations, we have equipped ourselves with a magnifying glass to scrutinize single predictions. These techniques fortify our understanding of specific outcomes, enhancing transparency and trust. However, to understand how this model behavior was formed, we turn to illustrative examples. These methods provide a context for model behavior, showing us how particular data instances inform the overall model logic and reveal systematic strengths and vulnerabilities.