A Framework for Evaluating Post Hoc Feature-Additive Explainers. (arXiv:2106.08376v2 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2106.08376

Many applications of data-driven models demand transparency of decisions,
especially in health care, criminal justice, and other high-stakes
environments. Modern trends in machine learning research have led to algorithms
that are increasingly intricate to the degree that they are considered to be
black boxes. In an effort to reduce the opacity of decisions, methods have been
proposed to construe the inner workings of such models in a
human-comprehensible manner. These post hoc techniques are described as being
universal explainers - …

arxiv framework post