Challenges in interpretability of additive models

Additive models aren't automatically interpretable just because they're simpler: While generalized additive models (GAMs) and neural additive models are marketed as "transparent" alternatives to black-box AI, the paper warns that transparency claims may be overstated. The authors argue for caution when assuming these models are truly interpretable or safe for high-stakes decisions.

Multiple mathematical solutions can hide behind the same model: The paper identifies "nonidentifiability" as a key challenge—meaning different combinations of model components can produce identical predictions. This creates a fundamental problem: even if you can see what the model is doing mathematically, you may not know which of several possible explanations is actually correct.

Interpretability requires more than just looking at the model structure: The authors challenge the assumption that simpler model architectures automatically lead to better human understanding. Just because a model's equations are visible doesn't mean practitioners can reliably interpret what the model has learned or trust its decisions.

Claims about safety-critical applications need scrutiny: The paper cautions against using interpretability as a justification for deploying additive models in high-stakes domains like healthcare or criminal justice without additional validation. The hidden complexities in these models mean they may not be as suitable for safety-critical applications as their proponents suggest.