Effective Design in Human and Machine Learning: A Cognitive Perspective

Zheng, Robert; Greenberg, Kevin

doi:10.1007/978-3-319-90403-0_4

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…For example, as a first approach, human behavior prediction models have been developed, either when decision-making is affected by peer pressure or by the inference of human activities based on short videos [151]. The approach ranges from psychological perspectives to assessing decision-making abstraction in human beings, both in regular contexts [152] or with imperfect information [153]. • Uncertainty.…”

Section: Deep Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Deep Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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“…Indeed, the power of the "crowd" is often leveraged to create large-scale training sets for Machine Learning, by adopting Crowdsourcing [2], Human Computation [3] and Citizen Science [4] approaches. Moreover, knowledge in human cognitive processes may assist the design and implementation of Machine Learning, as claimed in [5]; however, the current popularity of black-box models hinders an effective human intervention because those approaches negatively impact on trustworthiness, interpretability and the discovery of hidden rules.…”

Section: Related Workmentioning

confidence: 99%

Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI

Celino¹

2020

Preprint

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eXplainable AI focuses on generating explanations for the output of an AI algorithm to a user, usually a decision-maker. Such user needs to interpret the AI system in order to decide whether to trust the machine outcome. When addressing this challenge, therefore, proper attention should be given to produce explanations that are interpretable by the target community of users.In this chapter, we claim for the need to better investigate what constitutes a human explanation, i.e. a justification of the machine behaviour that is interpretable and actionable by the human decision makers. In particular, we focus on the contributions that Human Intelligence can bring to eXplainable AI, especially in conjunction with the exploitation of Knowledge Graphs.Indeed, we call for a better interplay between Knowledge Representation and Reasoning, Social Sciences, Human Computation and Human-Machine Cooperation research -as already explored in other AI branches -in order to support the goal of eXplainable AI with the adoption of a Human-in-the-Loop approach.

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Effective Design in Human and Machine Learning: A Cognitive Perspective

Cited by 2 publications

References 32 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Who is this Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI

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