David shares how aligning model explanations with human reasoning improves performance without sacrificing accuracy. The debate between post hoc explainability and changing training methods is explored, highlighting the trade-offs between objectives. Delving into single neuron analysis, the discussion touches on the challenges of explainability at different layers of neural networks.
