Towards evaluating and eliciting high-quality documentation for intelligent systems

11/17/2020
by   David Piorkowski, et al.
0

A vital component of trust and transparency in intelligent systems built on machine learning and artificial intelligence is the development of clear, understandable documentation. However, such systems are notorious for their complexity and opaqueness making quality documentation a non-trivial task. Furthermore, little is known about what makes such documentation "good." In this paper, we propose and evaluate a set of quality dimensions to identify in what ways this type of documentation falls short. Then, using those dimensions, we evaluate three different approaches for eliciting intelligent system documentation. We show how the dimensions identify shortcomings in such documentation and posit how such dimensions can be use to further enable users to provide documentation that is suitable to a given persona or use case.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/20/2021

Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)

More recently, Explainable Artificial Intelligence (XAI) research has sh...
research
01/25/2023

Explainable AI does not provide the explanations end-users are asking for

Explainable Artificial Intelligence (XAI) techniques are frequently requ...
research
08/03/2020

Enhancing autonomy transparency: an option-centric rationale approach

While the advances in artificial intelligence and machine learning empow...
research
10/22/2018

Towards a context-dependent numerical data quality evaluation framework

This paper focuses on numeric data, with emphasis on distinct characteri...
research
09/14/2020

Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations

This paper systematically derives design dimensions for the structured e...
research
05/21/2021

Object-Process Methodology for Intelligent System Development

Development of the new artificial systems with unique characteristics is...
research
06/18/2019

Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services

Recent advances in artificial intelligence (AI) and machine learning (ML...

Please sign up or login with your details

Forgot password? Click here to reset