I appreciate making these notions more precise. Model splintering seems closely related to other popular notions in ML, particularly underspecification (“many predictors f that a pipeline could return with similar predictive risk”), the Rashomon effect (“many different explanations exist for the same phenomenon”), and predictive multiplicity (“the ability of a prediction problem to admit competing models with conflicting predictions”), as well as more general notions of generalizability and out-of-sample or out-of-domain performance. I’d be curious what exactly makes model splintering different. Some example questions: Is the difference just the alignment context? Is it that “splintering” refers specifically to features and concepts within the model failing to generalize, rather than the model as a whole failing to generalize? If so, what does it even mean for the model as a whole to fail to generalize but not features failing to generalize? Is it that the aggregation of features is not a feature? And how are features and concepts different from each other, if they are?
I appreciate making these notions more precise. Model splintering seems closely related to other popular notions in ML, particularly underspecification (“many predictors f that a pipeline could return with similar predictive risk”), the Rashomon effect (“many different explanations exist for the same phenomenon”), and predictive multiplicity (“the ability of a prediction problem to admit competing models with conflicting predictions”), as well as more general notions of generalizability and out-of-sample or out-of-domain performance. I’d be curious what exactly makes model splintering different. Some example questions: Is the difference just the alignment context? Is it that “splintering” refers specifically to features and concepts within the model failing to generalize, rather than the model as a whole failing to generalize? If so, what does it even mean for the model as a whole to fail to generalize but not features failing to generalize? Is it that the aggregation of features is not a feature? And how are features and concepts different from each other, if they are?