Providing Actionable Feedback in Hiring Marketplaces using Generative Adversarial Networks

Abstract

Machine learning predictors have been increasingly applied in production settings, including in one of the world's largest hiring platforms, Hired, to provide a better candidate and recruiter experience. The ability to provide actionable feedback is desirable for candidates to improve their chances of achieving success in the marketplace. Until recently, however, methods aimed at providing actionable feedback have been limited in terms of realism and latency. In this work, we demonstrate how, by applying a newly introduced method based on Generative Adversarial Networks (GANs), we are able to overcome these limitations and provide actionable feedback in real-time to candidates in production settings. Our experimental results highlight the significant benefits of utilizing a GAN-based approach on our dataset relative to two other state-of-the-art approaches (including over 1000x latency gains). We also illustrate the potential impact of this approach in detail on two real candidate profile examples.

Counterfactual Building and Evaluation via eXplainable Support Vector Data Description

“…Previous related works validated the proposed CEs with respect to explanations obtained with other local explainability methods, like Local Interpretable Model-agnostic Explanations (LIME) or Layer-Wise Relevance Propagation (LRP) [3], [8] or with respect to other state-of-the-art method for generation of CEs [4], [9], [11]. Often, the validation measure relies on verifying that the CE is correctly associated with its target outcome, based on the prediction of a classifier.…”

“…For example they can be generated in order to understand what are the changes in the characteristics of a medical image that lead to a certain diagnosis of pathology (e.g., [8]and [5]). Another possible use of counterfactuals recently proposed in the literature [11] concerns their application to provide actionable feedback (e.g., realistic changes in expected salary or increase in work experience word count) to candidates in a hiring marketplace in order to improve their profile.…”

“…Therefore, the search for realistic counterfactuals should be performed by perturbing only controllable variables. To our knowledge, the only attempt to force the generated CEs to have no change in terms of non-controllable characteristics was carried out by Nemirovsy et al [11] who developed a method to produce counterfactuals able to provide actionable feedbacks in realtime using Generative Adversarial Networks (GANs). However, in that case, feature immutability was imposed after the application of the counterfactuals search algorithm by setting the values of non-controllable features to the original values rather than to the values suggested by the counterfactuals search algorithm.…”

Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization

“…Counterfactual explanation methods can be subcategorized to those that incorporate latent space disentanglement (such as DISCOVER) and those that do not. Counterfactual explanation methods without disentanglement (Samangouei et al 2018, Eckstein et al 2021, Narayanaswamy et al 2020, Nemirovsky et al 2020, Shih et al 2020, Joshi et al 2018, can concurrently alter multiple image properties, thus generating less intuitive counterfactual explanations.…”

DocXClassifier: Towards a Robust and Interpretable Deep Neural Network for Document Image Classification

“…In recent years, a wide variety of approaches have been proposed that attempt to explain the predictions of black-box DL models [6,[20][21][22][23][24]. In the image domain, post-hoc attribution-based approaches [20][21][22] are the most popular, which generate feature importance maps to identify the areas in the image that were most important to the model's prediction.…”