McMaster University (N/A)

The proposed application provides the method of making it possible to establish verifiable trust in collaborative data sharing environments. The system supports the human-in the-loop model by creating trust between all participants, which includes humans and AI systems, by making all data transformations transparent and easily verified by all participants.

The era of immense data analytics is transforming health care with the ability to interpret large and diverse datasets to provide better diagnoses, manage complex diseases, improve efficiency of care, and generate aggregated data for ArtificialIntelligence (AI) systems [1]. Alphabet Google, Google’s parent company has announced Cityblock Health, a new initiative to provide low-income households with medical care. It analyzes multiple datasets to determine the necessity for care. IBM Watson’s cognitive computing system utilizes numerous large datasets to create predictive analytics to improve diagnosis. In both examples, individuals (or patients) are continuously and actively engaged in contributing data that is fed to AI systems, and the research objective is accomplished by a continuous (sometimes in real time) collaboration between human beings (as the data contributors) as well as diverse researchers from different disciplines (e.g., computers, medicine, statistics, etc.)) as well as AI systems. This kind of research in health involves human or patient participants. It is very different from traditional clinical trials research, where just a handful of participants participate and the relationship between the patient and the researcher ceases after the data collection phase. To develop better predictive models for data analytics, there has to be a feedback loop among patients, researchers, and AI systems. However, trust needs to be built between all of the participants, not just AI systems. This inspiring scenario highlights the difficulties involved in maintaining trust among participants.

As part of a multidisciplinary research team, the researchers are currently working on a classification system that will predict a patient’s’ specific health. Because the machine learning model is constantly evolving, the classifier will remain active. Individuals can contribute data points to improve it. The updated risk assessment model will be available to the next patient when she is able to provide her data points for the next model’s improvement. A complex collaboration involving private data requires several Data Sharing Agreements (DSA) to be developed between diverse groups of researchers and hospitals, as each may adhere to different privacy policies and jurisdictions. Patients also participate in the research process as the AI system has to be updated in real-time to provide accurate and relevant details about every patient’s risk of developing cancer [2, [3(3). FIG. FIG. . . , an provide data analytics on the datasets. Continuous data is supplied by hospitals as it is created. . . , DSAn govern access to the datasets that are part of the research pipeline. Monitoring the lifecycle of data (from collecting to use, disclosure, and then transformation) is crucial for this type of scenario to ensure accountability. The process is usually overseen by both local and global auditors. While the trust between the different researchers and auditors is a presumption that there will be no conflicts of interest, it could cause a loss of trust [5].

Trusted systems are those where everyone agrees that the actions of the system are right and that the system’s outputs are reliable, and the system will perform its purpose. [6]. The trustworthiness of a system depends on the degree of trust that is perceived by each system component [7]. Trust is typically subjective and based on how people see their relationships with each other as well as the systems they interact with. Achieving a trustworthy system requires changing the concept of trust into an objective measurement. This transformation often relies on the use of a centralized trusted third party, where all parties involved trust this entity (e.g. certificates authorities of public key infrastructures [8],arbitrated protocols [9]). Collaboration is usually a source of trust when trust is created through a central third party. This can undermine the trust that the collaborators seek to establish. The Enron Scandal [10] is a textbookcase when the trust placed by central auditors was the primary element in the fraud. Instead of a central approach, we can leverage a distributed network to foster trust among colleagues and decrease the risks that tend to compromise trust in central systems.

Other features and advantages of the present invention are apparent in the description that follows. The following detailed description should be understood. While the details and examples are meant to demonstrate embodiments of the invention however, they shouldn’t be taken as limiting the scope of the claims. Instead, the examples are to be given the broadest interpretation that is consistent with the complete description.

This paper proposes a distributed approach employing a -based architectural to digitize trust within collaborative health research environments. Participants who do not necessarily trust one another can work together to accomplish a research goal. By using a permissioned blockchain (a blockchain that is controlled by authenticated users instead of a public blockchain utilized for crypto-currencies) we can create a more open and collaborative environment in which every data activity is transparent and can be verified by everyone involved. We define transactions that share data to protect the privacy of data subjects and control the size of data. Data pointers that rely upon institutional-based accesscontrol are used to allow data access. The data pointers are saved on the Blockchain and the privacy policies for each item are recorded. The blockchain provides the data with a secure trail integrity verification is performed and the participants are not able to deny their actions.

Data anonymization is one of the most current ways to protect the privacy of individuals and to maintain confidence when conducting collaborative research. These strategies are important, but insufficient when trust is not guaranteed among collaborators, particularly in cases where AI systems are also seen as part of the process of datatransformation. To decrease the chance of auditors and researchers colluding or unintentional breaches of data usage by an AI system, digitizing trust can be used alongside methods to anonymize data and proveance.

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