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How do we reliably know the trustworthiness of data, algorithms, and models?
Central to this is the notion of trust – how do we reliably know the trustworthiness of data, algorithms, and models?
The trust model uses vehicle trust to evaluate the trustworthiness of data [29].
Data-centric: The data/event message is the main object in this group, and the trust model focuses on the trustworthiness of data.
Moreover, the quality of safety/non-safety applications in VANETs largely depends upon the trustworthiness of data [7], and trust plays a vital role in the security and quality of a vehicular network.
This raises serious concerns about bias and the trustworthiness of data.
Similar(49)
Trust evaluation frameworks utilize data provenance along with the sensed data values to compute the trustworthiness of each data item.
Assessing the trustworthiness of sensor data and transmitters of this data is critical for quality assurance.
For example, Alabri and Hunter ([2010]) describe a framework combining data quality control and trust metrics to enhance the reliability of citizen science data, and Kuan et al. ([2010]) as well as Yang et al. ([2011]) propose reputation management systems to evaluate the trustworthiness of gathered data by co-observers and data end-users.
To support the richness of the data and to add to the trustworthiness of the data a reflective journal was kept by the researcher.
The qualitative analyses will incorporate the following processes [ 50, 51]: 1) Invite participants to review the transcripts of interviews, and summarize their perception of the data, for accuracy, and check for the trustworthiness of the data; 2) Conduct a thematic analysis using a reciprocal coding approach where researchers engage in open dialogue about themes and data interpretation.
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