scholarly journals Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning

2016 ◽  
pp. 357-376 ◽  
Author(s):  
Sebastian Robert ◽  
Sebastian Büttner ◽  
Carsten Röcker ◽  
Andreas Holzinger
Keyword(s):  
Machine Learning ◽  
Reasoning Under Uncertainty ◽  
Interactive Machine Learning

Dealing with Mislabeling via Interactive Machine Learning

2020 ◽  
Vol 34 (2) ◽  
pp. 271-278
Author(s):  
Wanyi Zhang ◽  
Andrea Passerini ◽  
Fausto Giunchiglia
Keyword(s):  
Machine Learning ◽  
Interactive Machine Learning

scholarly journals A Review on Human–AI Interaction in Machine Learning and Insights for Medical Applications

2021 ◽  
Vol 18 (4) ◽  
pp. 2121
Author(s):  
Mansoureh Maadi ◽  
Hadi Akbarzadeh Khorshidi ◽  
Uwe Aickelin
Keyword(s):  
Machine Learning ◽  
Future Research ◽  
Computational Power ◽  
Medical Field ◽  
Interactive Machine Learning ◽  
Human In The Loop ◽  
Human Interactions ◽  
Scoping Literature Review ◽  
Domain Expertise ◽  
Expertise Level

Objective: To provide a human–Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical field, as the medical ML application literature highlights a special necessity of medical experts collaborating with ML approaches. Methods: A scoping literature review is performed on Scopus and Google Scholar using the terms “human in the loop”, “human in the loop machine learning”, and “interactive machine learning”. Peer-reviewed papers published from 2015 to 2020 are included in our review. Results: We design four questions to investigate and describe human–AI interaction in ML applications. These questions are “Why should humans be in the loop?”, “Where does human–AI interaction occur in the ML processes?”, “Who are the humans in the loop?”, and “How do humans interact with ML in Human-In-the-Loop ML (HILML)?”. To answer the first question, we describe three main reasons regarding the importance of human involvement in ML applications. To address the second question, human–AI interaction is investigated in three main algorithmic stages: 1. data producing and pre-processing; 2. ML modelling; and 3. ML evaluation and refinement. The importance of the expertise level of the humans in human–AI interaction is described to answer the third question. The number of human interactions in HILML is grouped into three categories to address the fourth question. We conclude the paper by offering a discussion on open opportunities for future research in HILML.

An Interactive Machine Learning System for Image Advertisements

2021 ◽  
Author(s):  
Markus Foerste ◽  
Mario Nadj ◽  
Merlin Knaeble ◽  
Alexander Maedche ◽  
Leonie Gehrmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Interactive Machine Learning

scholarly journals You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems

2014 ◽  
Vol 40 (3) ◽  
pp. 307-323 ◽  
Author(s):  
Alex Groce ◽  
Todd Kulesza ◽  
Chaoqiang Zhang ◽  
Shalini Shamasunder ◽  
Margaret Burnett ◽  
...  
Keyword(s):  
Machine Learning ◽  
End Users ◽  
Learning Systems ◽  
Test Selection ◽  
Interactive Machine Learning ◽  
Selection For

Human Factors in Interactive Machine Learning: A Cybersecurity Case Study

2021 ◽  
Vol 65 (1) ◽  
pp. 1495-1499
Author(s):  
Mark H. Chignell ◽  
Mu-Huan Chung ◽  
Yuhong Yang ◽  
Greg Cento ◽  
Abhay Raman
Keyword(s):  
Machine Learning ◽  
Human Factors ◽  
Financial Services ◽  
Explicit Knowledge ◽  
Implicit Knowledge ◽  
Cyber Attacks ◽  
Transfer Data ◽  
Interactive Machine Learning

Cybersecurity is emerging as a major issue for many organizations and countries. Machine learning has been used to recognize threats, but it is difficult to predict future threats based on past events, since malicious attackers are constantly finding ways to circumvent defences and the algorithms that they rely on. Interactive Machine learning (iML) has been developed as a way to combine human and algorithmic expertise in a variety of domains and we are currently applying it to cybersecurity. In this application of iML, implicit knowledge about human behaviour, and about the changing nature of threats, can supplement the explicit knowledge encoded in algorithms to create more effective defences against cyber-attacks. In this paper we present the example problem of data exfiltration where insiders, or outsiders masquerading as insiders, who copy and transfer data maliciously, against the interests of an organization. We will review human factors issues associated with the development of iML solutions for data exfiltration. We also present a case study involving development of an iML solution for a large financial services company. In this case study we review work carried out on developing visualization dashboards and discussing prospects for further iML integration. Our goal in writing this paper is to motivate future researchers to consider the role of the human more fully in ML, not only in the data exfiltration and cybersecurity domain but also in a range of other applications where human expertise is important and needs to combine with ML prediction to solve challenging problems.

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