Titlebook: Human and Machine Learning; Visible, Explainable Jianlong Zhou,Fang Chen Textbook 2018 Springer International Publishing AG, part of Spring

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2D Transparency Space—Bring Domain Users and Machine Learning Experts Togethercy space which integrates domain users and ML experts together to make ML transparent. We identify typical Transparent ML (TML) challenges and discuss key obstacles to TML, which aim to inspire active discussions of making ML transparent with a systematic view in this timely field.

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Effective Design in Human and Machine Learning: A Cognitive Perspective. A framework was proposed to advance the practice of machine learning focusing on transfer of knowledge in human deep learning with respect to the relations between human cognitive processes and machine learning.

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Perturbation-Based Explanations of Prediction Models as their advantages and disadvantages. We illustrate practical issues and challenges in applying the explanation methodology in a business context on a practical use case of B2B sales forecasting in a company. We demonstrate how explanations can be used as a what-if analysis tool to answer relevant business questions.

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Group Cognition and Collaborative AItion with humans: conversational grounding and theory of mind. These concepts are somewhat different from those already discussed in AI research. We outline some new implications for collaborative AI, aimed at extending skills and solution spaces and at improving joint cognitive and creative capacity.

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Do I Trust a Machine? Differences in User Trust Based on System Performanceceive the accuracy of the system and adjust their trust accordingly. The results also show notable differences between two groups of users and indicate a possible threshold in the acceptance of the system. This important learning can be leveraged by designers of practical systems for sustaining the desired level of user trust.

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Trust and Transparency in Machine Learning-Based Clinical Decision Supporte trust in automation, but is hard to achieve in practice. This chapter discusses the clinical and technology related factors that influence clinician trust in automated systems, and can affect the need for transparency when developing machine learning-based clinical decision support systems.

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Jianlong Zhou,Fang ChenCreates a systematic view of relations between human and machine learning from the perspectives of visualisation, explanation, trustworthiness and transparency.Explores human aspects in machine learni

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