Model Multiplicity Opportunities Concerns And Solutions Youtube

Model Multiplicity Opportunities Concerns And Solutions Youtube Model multiplicity: opportunities, concerns, and solutions emily black, manish raghavan and solon barocas. By demonstrating that there are many different ways of making equally accurate predictions, multiplicity gives model developers the freedom to prioritize other values in their model selection process without having to abandon their commitment to maximizing accuracy.

Model Multiplicity Opportunities Concerns And Solutions In such cases, the mode predictor best achieves these goals: it is the model that minimizes multiplicity compared to the model distribution $\mathcal {m}$. 10 recent work has shown that, beyond stabilizing model predictions, mode aggregation also results in more stable model explanations, and thus suggests that models which return the mode over. In this work, we investigate how to take advantage of the flexibility afforded by model multiplicity while addressing the concerns with justifiability that it might raise. keywords: model multiplicity, predictive multiplicity, procedural multiplicity, fairness, discrimination, recourse, arbitrariness. Recent scholarship has brought attention to the fact that there often exist multiple models for a given prediction task with equal accuracy that differ in their individual level predictions or aggregate properties. this phenomenon—which we call model multiplicity—can introduce a good deal of flexibility into the model selection process, creating a range of exciting opportunities. by. Model multiplicity: opportunities, concerns, and solutions people subject to the model to seek recourse. we also show that model multiplicity has.