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" ... Even with improved methods and algorithms for explaining AI models and predictions, two core issues must first be addressed in order to make legitimate progress towards interpretable, transparent AI: underspecification and misalignment. ... "
" ... The paper identifies the root cause of this behavior as underspecification in ML pipelines. Underspecification is a well understood and well documented phenomenon in ML, it arises due to the presence of more unknowns than independent linear equations expressable in a training set. The first claim in the paper is that underspecification in ML pipelines is a key obstacle to reliably training models that behave as expected in deployment. The second claim is that underspecification is ubiquitous in modern applications of ML, and has substantial practical implications. There is no easy cure for the underspecification. All deployed ML predictors using the current pipeline are tainted to some degree. ... "