The architecture is moving toward opacity. The shift to large foundation models, reinforcement learning policies, and agentic systems in investment management has outpaced the vocabulary allocators use to evaluate managers. As models grow more capable, the link between input and decision grows more obscure, and the temptation to accept a confident narrative in place of a genuine explanation grows with it.
Post-hoc explanation tools have created a false sense of resolution. SHAP values, attention maps, and saliency methods produce outputs that look like explanations and are increasingly offered as such. Rudin’s warning applies directly: an explanation that is not faithful to the model is worse than no explanation, because it manufactures confidence the evidence does not support.
Allocators then need to dissect attribution from explanation. The question is not whether every sophisticated model must be simple, but whether the manager can provide a defensible account of how its decisions relate to the economic reasoning behind the strategy.
The most rigorous institutions already treat explanation as a standard rather than a courtesy. ADIA Lab’s investment in causal inference, including a $100,000 research award and a global challenge that drew nearly 2,000 researchers, reflects a view that understanding why a model decides is now part of the work.
CFA Institute’s Standard V(A) requires members to have a reasonable and adequate basis for investment recommendations, including an understanding of the assumptions and limitations of quantitative models. The ability to reconstruct and justify individual decisions can provide allocators with another way to assess that understanding.
