Intelligible Intelligence: Deep XAI still more R&D than toolbox

Milan Kratochvil

Most architectural tradeoffs are hard. So is the one between the accuracy of Deep Machine Learning (ML) and explainability/transparency of explainable AI (XAI). Therefore, DARPA’s initial XAI program is expected to run through 2021.

Few explainability mechanisms have been extensively tested on humans, but current R&D indicates at least some (hybrid) tech to come in a couple of years.
Several well-tried ML technologies work their way through (in steps) from a first random solution to satisfactory ones, and where possible, to an optimal one:
·         Genetic Algorithms, by creating additional generations
·         Forests, by creating additional trees from the same data
·        Deep Neural Networks, by a (cost) function applied (in training) to outputs, based on how they differ from labeled data, and propagated back across all neurons/synapses, to adjust weights
·        Hybrids, by combination (the “new kid on the block”).
NASA Space Technology 5 antenna created by GA (source:
GA and Forests are more than semi-intelligible, by their nature. However, now that deep learning feeds big data through NN that consist of multiple hidden layers of neurons (DNN), it arrives at very accurate solutions to complex multidimensional problems, but at the same time at an inherent black box.
A part of my post from August is about random forests, which offer both more transparency than DNN do plus quite a degree of accuracy. Moreover, trees and NN are even cross-fertilized, to offer explainability without impeding accuracy, and there are already several flavors of this; to pick a handful:
·        adding extraction of simplified explainable models (e.g. trees) onto black-box DNN
·        local (instance-based) explanation of one use case at a time, with its input values, for example animating & explaining its path layer-by layer (like most test tools do).
·        soft trees, with NN-based leaf nodes, that perform better than trees induced directly from the same data
·        adaptive neural trees and deep neural decision forests that create trees (edges, splits, and leafs) , to outperform “standalone” NN as well as trees/forests that skip the combination.
Explainability, transparency, and V&V are absolutely essential to users’ reliance/confidence in mission-critical AI. Therefore, whichever path or paths take us to up-and-running products, XAI is welcome.

information om författaren:
Milan Kratochvil

Trainer at Informator, senior modeling and architecture consultant at Kiseldalen’s, main author: UML Extra Light (Cambridge University Press) and Growing Modular (Springer), Advanced UML2 Professional (OCUP cert level 3/3). Milan and Informator collaborate since 1996 on architecture, modelling, UML, requirements, rules/AI, and design. You can meet him at public courses (in English or Swedish) on AI, Architecture, and ML (T1913, in December or February), Architecture (T1101, T1430) or Modeling (T2715T2716).

Nyckelord: machine learning, AI