Technical Aspects of Artificial Intelligence: An Understanding from an Intellectual Property Perspective

Drexl, Josef; Hilty, Reto M.; Beneke, Francisco; Desaunettes, Luc; Finck, Michèle; Globocnik, Jure; Otero, Begoña González; Hoffmann, Jörg; Hollander, Leonard; Kim, Daria; Richter, Heiko; Scheuerer, Stefan; Slowinski, Peter R.; Thonemann, Jannick

doi:10.2139/ssrn.3465577

Cited by 21 publications

(4 citation statements)

References 5 publications

(1 reference statement)

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“…In particular, the developed, ie, applicable AI tool is characterized by the 'black box' phenomenon already outlined above. 99 This phenomenon is closely related to the issue of reverse engineering, which can be defined as 'the possibility to extract or deduce certain elements of the machinelearning process through access to other elements' . Take ML as an example.…”

Section: Investment Protection Theorymentioning

confidence: 99%

Intellectual Property Justification for Artificial Intelligence

Hilty

Hoffmann

Scheuerer

2021

Artificial Intelligence and Intellectual Property

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Against the backdrop of the current discussion of how AI reshapes certain IP paradigms, this chapter reassesses the need for IP protection in AI markets per se. We assess the question of justification of IP rights for both AI as a tool and AI-generated output in light of the very theoretical foundations of IP protection (from both legal embedded deontological and utilitarian economic standpoints). Traditionally, IP protection is granted due to deontological reasoning, according to which a human creator’s personality and efforts have to be protected, and economic reasoning, according to which exclusive rights in intangible goods have to be established in order to remedy market failure in public goods markets. IP ought to serve as a regulatory system of stimulation of creation and innovation using market forces to achieve this goal. Based on the current state of knowledge, however, it seems that specific market implications of the widespread use of most AI applications may have altered the justification for AI-related IP protection in certain cases. Whereas this seems particularly true regarding AI tools, the case for AI outputs may be different.

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Section: Investment Protection Theorymentioning

confidence: 99%

Intellectual Property Justification for Artificial Intelligence

Hilty

Hoffmann

Scheuerer

2021

Artificial Intelligence and Intellectual Property

Self Cite

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“…CBK models may also be called CDS artifacts or machine learning, deep learning, AI, decision, and business process models, or even actionable knowledge units . 12 , 13 , 14 Here, we generally refer to any model that represents biomedical results and insights as a CBK model . To better support learning health systems, we demonstrate the building of composite CBK models by interconnecting multiple distinct CBK submodels packaged inside many individual KOs.…”

Section: Introductionmentioning

confidence: 99%

“…Using multiple KOs, we are primarily interested in composing models of computable biomedical knowledge (CBK). CBK models may also be called CDS artifacts or machine learning, deep learning, AI, decision, and business process models, or even actionable knowledge units 12‐14 . Here, we generally refer to any model that represents biomedical results and insights as a CBK model .…”

Section: Introductionmentioning

confidence: 99%

CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services

Flynn

Taksler

Caverly

et al. 2022

Learning Health Systems

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Introduction Learning health systems are challenged to combine computable biomedical knowledge (CBK) models. Using common technical capabilities of the World Wide Web (WWW), digital objects called Knowledge Objects, and a new pattern of activating CBK models brought forth here, we aim to show that it is possible to compose CBK models in more highly standardized and potentially easier, more useful ways. Methods Using previously specified compound digital objects called Knowledge Objects, CBK models are packaged with metadata, API descriptions, and runtime requirements. Using open‐source runtimes and a tool we developed called the KGrid Activator, CBK models can be instantiated inside runtimes and made accessible via RESTful APIs by the KGrid Activator. The KGrid Activator then serves as a gateway and provides a means to interconnect CBK model outputs and inputs, thereby establishing a CBK model composition method. Results To demonstrate our model composition method, we developed a complex composite CBK model from 42 CBK submodels. The resulting model called CM‐IPP is used to compute life‐gain estimates for individuals based their personal characteristics. Our result is an externalized, highly modularized CM‐IPP implementation that can be distributed and made runnable in any common server environment. Discussion CBK model composition using compound digital objects and the distributed computing technologies is feasible. Our method of model composition might be usefully extended to bring about large ecosystems of distinct CBK models that can be fitted and re‐fitted in various ways to form new composites. Remaining challenges related to the design of composite models include identifying appropriate model boundaries and organizing submodels to separate computational concerns while optimizing reuse potential. Conclusion Learning health systems need methods for combining CBK models from a variety of sources to create more complex and useful composite models. It is feasible to leverage Knowledge Objects and common API methods in combination to compose CBK models into complex composite models.

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“…This data-driven knowledge can then be adapted and applied to address new challenges and evaluate previously unexplored data [5]. ML allows information technology (IT) systems to identify patterns and rules based on existing data and algorithms and build solutions on their own [6]. Over the years, using ML has proven to be beneficial in a variety of sectors, with success owing to the development of more advanced ML models [7,8].…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

et al. 2022

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The machine learning approach has been widely used in many areas of studies, including the tourism sector. It can offer powerful estimation for prediction. With a growing number of tourism activities, there is a need to predict tourists’ classification for monitoring, decision making, and planning formulation. This paper aims to predict visitors to totally protected areas in Sarawak using machine learning techniques. The prediction model developed would be able to identify significant factors affecting local and foreign visitors to these areas. Several machine learning techniques such as k-NN, Naive Bayes, and Decision Tree were used to predict whether local and foreign visitors’ arrival was high, medium, or low to these totally protected areas in Sarawak, Malaysia. The data of local and foreign visitors’ arrival to eighteen totally protected areas covering national parks, nature reserves, and wildlife centers in Sarawak, Malaysia, from 2015 to 2019 were used in this study. Variables such as the age of the park, distance from the nearest city, types of the park, recreation services availability, natural characteristics availability, and types of connectivity were used in the model. Based on the accuracy measure, precision, and recall, results show Decision Tree (Gain Ratio) exhibited the best prediction performance for both local visitors (accuracy = 80.65) and foreign visitors (accuracy = 84.35%). Distance to the nearest city and size of the park were found to be the most important predictors in predicting the local tourist visitors’ park classification, while for foreign visitors, age, type of park, and the natural characteristics availability were the significant predictors in predicting the foreign tourist visitors’ parks classification. This study exemplifies that machine learning has respectable potential for the prediction of visitors’ data. Future research should consider bagging and boosting algorithms to develop a visitors’ prediction model.

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Technical Aspects of Artificial Intelligence: An Understanding from an Intellectual Property Perspective

Cited by 21 publications

References 5 publications

Intellectual Property Justification for Artificial Intelligence

Intellectual Property Justification for Artificial Intelligence

CBK model composition using paired web services and executable functions: A demonstration for individualizing preventive services

Using Machine Learning to Predict Visitors to Totally Protected Areas in Sarawak, Malaysia

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