Explainable AI: current status and future directions
Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of
Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (eg …
Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (eg …
A survey of the state of explainable AI for natural language processing
Recent years have seen important advances in the quality of state-of-the-art models, but this
has come at the expense of models becoming less interpretable. This survey presents an …
has come at the expense of models becoming less interpretable. This survey presents an …
The fundamentals of Artificial Intelligence in medical education research: AMEE Guide No. 156
Abstract The use of Artificial Intelligence (AI) in medical education has the potential to
facilitate complicated tasks and improve efficiency. For example, AI could help automate …
facilitate complicated tasks and improve efficiency. For example, AI could help automate …
The past, present, and prospective future of xai: A comprehensive review
With the increasing growth and availability of data, Artificial Intelligence (AI) based black-box
models have shown significant effectiveness to solve real-world and mission-critical …
models have shown significant effectiveness to solve real-world and mission-critical …
Let's make your request more persuasive: Modeling persuasive strategies via semi-supervised neural nets on crowdfunding platforms
Modeling what makes a request persuasive-eliciting the desired response from a reader-is
critical to the study of propaganda, behavioral economics, and advertising. Yet current …
critical to the study of propaganda, behavioral economics, and advertising. Yet current …
Uncovering latent biases in text: Method and application to peer review
Quantifying systematic disparities in numerical quantities such as employment rates and
wages between population subgroups provides compelling evidence for the existence of …
wages between population subgroups provides compelling evidence for the existence of …
Causal effects of linguistic properties
We consider the problem of using observational data to estimate the causal effects of
linguistic properties. For example, does writing a complaint politely lead to a faster response …
linguistic properties. For example, does writing a complaint politely lead to a faster response …
Deep multi-modal structural equations for causal effect estimation with unstructured proxies
S Deshpande, K Wang, D Sreenivas… - Advances in Neural …, 2022 - proceedings.neurips.cc
Estimating the effect of intervention from observational data while accounting for
confounding variables is a key task in causal inference. Oftentimes, the confounders are …
confounding variables is a key task in causal inference. Oftentimes, the confounders are …
Exploring how feedback reflects entrustment decisions using artificial intelligence
Context Clinical supervisors make judgements about how much to trust learners with critical
activities in patient care. Such decisions mediate trainees' opportunities for learning and …
activities in patient care. Such decisions mediate trainees' opportunities for learning and …
Riker: Mining rich keyword representations for interpretable product question answering
This work studies product question answering (PQA) which aims to answer product-related
questions based on customer reviews. Most recent PQA approaches adopt end2end …
questions based on customer reviews. Most recent PQA approaches adopt end2end …