Counterfactual Explanations for Machine Learning: Challenges Revisited

06/14/2021
by   Sahil Verma, et al.
82

Counterfactual explanations (CFEs) are an emerging technique under the umbrella of interpretability of machine learning (ML) models. They provide “what if” feedback of the form “if an input datapoint were x' instead of x, then an ML model's output would be y' instead of y.” Counterfactual explainability for ML models has yet to see widespread adoption in industry. In this short paper, we posit reasons for this slow uptake. Leveraging recent work outlining desirable properties of CFEs and our experience running the ML wing of a model monitoring startup, we identify outstanding obstacles hindering CFE deployment in industry.

READ FULL TEXT
research
06/07/2021

Amortized Generation of Sequential Counterfactual Explanations for Black-box Models

Explainable machine learning (ML) has gained traction in recent years du...
research
08/25/2020

Counterfactual Explanations for Machine Learning on Multivariate Time Series Data

Applying machine learning (ML) on multivariate time series data has grow...
research
10/01/2021

Multi-Agent Algorithmic Recourse

The recent adoption of machine learning as a tool in real world decision...
research
10/20/2020

Counterfactual Explanations for Machine Learning: A Review

Machine learning plays a role in many deployed decision systems, often i...
research
03/08/2023

"How to make them stay?" – Diverse Counterfactual Explanations of Employee Attrition

Employee attrition is an important and complex problem that can directly...
research
09/14/2020

The Role of Individual User Differences in Interpretable and Explainable Machine Learning Systems

There is increased interest in assisting non-expert audiences to effecti...
research
01/17/2022

Principled Diverse Counterfactuals in Multilinear Models

Machine learning (ML) applications have automated numerous real-life tas...

Please sign up or login with your details

Forgot password? Click here to reset