Paper Info
Title
Improving Automation Transparency: Addressing Some of Machine Learning's Unique Challenges.
Abstract
A variety of factors can affect one’s reliance on an automated aid. Some of these factors include one’s perception of the system’s trustworthiness, such as perceived reliability of the system or one’s ability to understand the system’s underlying reasoning. A mismatch between the operator’s perception and the true capabilities and characteristics of the system can lead to inappropriate reliance on the tool. This improper use of the system can manifest as either underutilization of the technology or complacency resulting from over-trusting the system. Increasing an automated tool’s transparency is one approach that enables the operator to more appropriately rely on the technology. Transparent automated systems provide additional information that allows the user to see the system’s intent and understand its underlying processes and capabilities. Several researchers have developed frameworks to support the design of more transparent automation. However, these frameworks may not fully consider the particular challenges to transparency design introduced by automation that leverages machine learning. Like all automation, these systems can benefit from transparency. However, artificial intelligence poses new challenges that must be considered when designing for transparency. Unique considerations must be made in terms of the type, and amount or level of transparency information conveyed to the user.
Year
Venue
Field
2018
HCI
Transparency (graphic),Computer science,Trustworthiness,Automation,Operator (computer programming),Artificial intelligence,Perception,Machine learning
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
11
2
Name
Order
Citations
PageRank
Corey K. Fallon120.71
Leslie M. Blaha2436.51