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1 – 10 of 761Yadong Huang, Yueting Chai, Yi Liu and Xiang Gu
The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.
Abstract
Purpose
The purpose of this paper is to study the architecture of holographic personalized portal, user modeling, commodity modeling and intelligent interaction.
Design/methodology/approach
In this paper, the authors propose crowd-science industrial ecological system based on holographic personalized portal and its interaction. The holographic personality portal is based on holographic enterprises, commodities and consumers, and the personalized portal consists of accurate ontology, reliable supply, intelligent demand and smart cyberspace.
Findings
The personalized portal can realize the information acquisition, characteristic analysis and holographic presentation. Then, the intelligent interaction, e.g. demand decomposition, personalized search, personalized presentation and demand prediction, will be implemented within the personalized portal.
Originality/value
The authors believe that their work on intelligent interaction based on holographic personalized portal, which has been first proposed in this paper, is innovation focusing on the interaction between intelligence and convenience.
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Samuli Laato, Miika Tiainen, A.K.M. Najmul Islam and Matti Mäntymäki
Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a…
Abstract
Purpose
Inscrutable machine learning (ML) models are part of increasingly many information systems. Understanding how these models behave, and what their output is based on, is a challenge for developers let alone non-technical end users.
Design/methodology/approach
The authors investigate how AI systems and their decisions ought to be explained for end users through a systematic literature review.
Findings
The authors’ synthesis of the literature suggests that AI system communication for end users has five high-level goals: (1) understandability, (2) trustworthiness, (3) transparency, (4) controllability and (5) fairness. The authors identified several design recommendations, such as offering personalized and on-demand explanations and focusing on the explainability of key functionalities instead of aiming to explain the whole system. There exists multiple trade-offs in AI system explanations, and there is no single best solution that fits all cases.
Research limitations/implications
Based on the synthesis, the authors provide a design framework for explaining AI systems to end users. The study contributes to the work on AI governance by suggesting guidelines on how to make AI systems more understandable, fair, trustworthy, controllable and transparent.
Originality/value
This literature review brings together the literature on AI system communication and explainable AI (XAI) for end users. Building on previous academic literature on the topic, it provides synthesized insights, design recommendations and future research agenda.
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Markus Ketterl, Lars Knipping, Nadine Ludwig and Robert Mertens
Abstract
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Abstract
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