32 research outputs found
Self-explaining AI as an alternative to interpretable AI
The ability to explain decisions made by AI systems is highly sought after,
especially in domains where human lives are at stake such as medicine or
autonomous vehicles. While it is often possible to approximate the input-output
relations of deep neural networks with a few human-understandable rules, the
discovery of the double descent phenomena suggests that such approximations do
not accurately capture the mechanism by which deep neural networks work. Double
descent indicates that deep neural networks typically operate by smoothly
interpolating between data points rather than by extracting a few high level
rules. As a result, neural networks trained on complex real world data are
inherently hard to interpret and prone to failure if asked to extrapolate. To
show how we might be able to trust AI despite these problems we introduce the
concept of self-explaining AI. Self-explaining AIs are capable of providing a
human-understandable explanation of each decision along with confidence levels
for both the decision and explanation. For this approach to work, it is
important that the explanation actually be related to the decision, ideally
capturing the mechanism used to arrive at the explanation. Finally, we argue it
is important that deep learning based systems include a "warning light" based
on techniques from applicability domain analysis to warn the user if a model is
asked to extrapolate outside its training distribution. For a video
presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& .Comment: 10pgs, 2 column forma
Defining a novel k-nearest neighbours approach to assess the applicability domain of a QSAR model for reliable predictions
Prediction of the datasets modelability for the building of QSAR classification models by means of the centroid based rivality index
Applicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions
Molecular docking for predictive toxicology
Molecular docking is an in silico method widely applied in drug discovery programs to predict the binding mode of a given molecule interacting with a specific biological target. This computational technique is today emerging also in the field of predictive toxicology for regulatory purposes, being for instance successfully applied to develop classification models for the prediction of the endocrine disruptor potential of chemicals. Herein, we describe the protocol for adapting molecular docking to the purposes of predictive toxicology