Over the past year, we've seen a shift in interest around machine learning and deep neural networks in particular. Until now, tool and technique development has been driven by excitement over the remarkable capabilities of these models. Currently, though, there is rising concern that these models could cause unintentional harm. For example, a model could be trained inadvertently to make profitable credit decisions by simply excluding disadvantaged applicants. Fortunately, we're seeing a growing interest in ethical bias testing that will help to uncover potentially harmful decisions. Tools such as lime, AI Fairness 360 or What-If Tool can help uncover inaccuracies that result from underrepresented groups in training data and visualization tools such as Google Facets or Facets Dive can be used to discover subgroups within a corpus of training data. We've used lime (local interpretable model-agnostic explanations) in addition to this technique in order to understand the predictions of any machine-learning classifier and what classifiers (or models) are doing.
Over the past year, we've seen a shift in interest around machine learning and deep neural networks in particular. Until now, tool and technique development has been driven by excitement over the remarkable capabilities of these models. Currently though, there is rising concern that these models could cause unintentional harm. For example, a model could be trained to make profitable credit decisions by simply excluding disadvantaged applicants. Fortunately, we're seeing a growing interest in ethical bias testing that will help to uncover potentially harmful decisions. Tools such as lime, AI Fairness 360 or What-If can help uncover inaccuracies that result from underrepresented groups in training data and visualization tools such as Google Facets or Facets Dive can be used to discover subgroups within a corpus of training data. However, this is a developing field and we expect standards and practices specific to ethical bias testing to emerge over time.
