User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal; Bruno Dumas; Benoît Frenay

User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Adrien Bibal, Bruno Dumas, Benoît Frenay

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

13 Downloads (Pure)

Abstract

With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research is
done on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

Original language	English
Title of host publication	EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence
Place of Publication	Metz
Publication status	Published - 2019

Keywords

Machine learning
Interpretability
Guidelines

Access to Document

2019-Bibal-User_Experiment_Guidelines_for_Measuring_Interpretability_in_Machine_LearningFinal published version, 67.5 KB

Fingerprint Dive into the research topics of 'User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning'. Together they form a unique fingerprint.

Cite this

@inproceedings{25b098bd11124ba9bf3344c69c646289,

title = "User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning",

abstract = "With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.",

keywords = "Machine learning, Interpretability, Guidelines",

author = "Adrien Bibal and Bruno Dumas and Beno{\^i}t Frenay",

year = "2019",

language = "English",

booktitle = "EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence",

}

TY - GEN

T1 - User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

AU - Bibal, Adrien

AU - Dumas, Bruno

AU - Frenay, Benoît

PY - 2019

Y1 - 2019

N2 - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

AB - With the advent of high-performance black-box models, interpretability is becoming a hot topic today in machine learning. While a lot of research isdone on interpretability, machine learning researchers do not have precise guidelines for setting up user-based experiments. This paper provides well-established guidelines from the human-computer interaction community.

KW - Machine learning

KW - Interpretability

KW - Guidelines

M3 - Conference contribution

BT - EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence

CY - Metz

ER -