Gimme That Model!: A Trusted ML Model Trading Protocol

Amorós, Laia; Hafiz, Syed Mahbub; Lee, Keewoo; Tol, M. Caner

doi:10.48550/arxiv.2003.00610

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…As outlined above, consumers of servitized DL models in particular need to be aware of the risks their black-box nature poses and establish similarly strict protocols as with human operators for similar decisions. As the market of AIaaS is only emerging, guidelines for responsible transfer learning have yet to be established (e.g., Amorós et al 2020).…”

Section: Resource Limitations and Transfer Learningmentioning

confidence: 99%

Machine learning and deep learning

2021

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Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

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Section: Resource Limitations and Transfer Learningmentioning

confidence: 99%

Machine learning and deep learning

2021

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“…For the latter, a pre-trained general model is tuned for its new task with comparably few specific observations in a process called transfer learning [26]. However, acquiring and using third-party pre-trained models, such as NLP models for chatbots, often means using a black box, which can exhibit any kind of prejudicial behavior such as local social or geographical biases or even susceptibility to adversarial attacks or [27].…”

Section: Ai1: Model and Training Data Selectionmentioning

confidence: 99%

“…Likewise, we show how these challenges should be solved and whether they will occur in the short or long term in an intelligent RPA implementation project. ), this process also causes trust and compliance concerns [26,27]. In this context, companies need to build trust with vendors and developers that their AI models are unbiased and robust against adversarial attacks [12].…”

Section: Overview Of Challengesmentioning

confidence: 99%

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

Herm

Janiesch

Reijers

et al. 2021

Lecture Notes in Computer Science

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Robotic process automation (RPA) is a novel technology that automates tasks by interacting with other software through their respective user interfaces. The technology has received substantial business attention because of its potential for rapid automation of process-driven tasks that would otherwise require tedious manual labor. This article explores the dichotomy between the practical reality of symbolic RPA, which requires handcrafting robots using process models and rulesets, and the promise of intelligent RPA, which relies on artificial intelligence technology to implement intelligent robots. Our research is based on a scholarly literature review as well as an interview study to derive and discuss challenges for this transition. We found that issues such as the lack of training data, human bias in data, compliance issues with transfer learning, poor explainability of robot decisions, and job-security-induced fear of AI robots all need to be addressed to enable the transition from symbolic to intelligent RPA.

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Gimme That Model!: A Trusted ML Model Trading Protocol

Cited by 2 publications

References 2 publications

Machine learning and deep learning

Machine learning and deep learning

From Symbolic RPA to Intelligent RPA: Challenges for Developing and Operating Intelligent Software Robots

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