By: Suvarna Tambe RIG Inc Cooperative Researcher

 

Talking about the general term trust, in all kinds of relationships, such as human-social encounters, seller-buyer relationships, and contacts between members of a virtual team, trust is important. The way individuals communicate with technology may also be characterized by trust. It is about using algorithms and models to create Dynamic Trust, tuning them to the specific characteristics of a place of trust, and feeding them with suitable data. These models define the features of trust and allow us to apply data and continuously assess the risk. Dynamic Trust is an objective method of prioritizing an uncertainty and their present actions. In general, compared to other technologies, Artificial Intelligence (AI) has many fresh features. Applications of AI are becoming more and more popular. In the creation and acceptance of AI, Dynamic Trust is crucial. In Mobile Device Management(MDM), AI will track the mobile system continuously. It is possible to use advanced AI to find data relationships, to come up with new solutions to optimize a process, which are beyond what human experts have previously considered. In the creation and acceptance of Artificial Intelligence for MDM Dynamic Trust is essential. Dynamic trust is a process that involves moving from initial trust to the creation of continuous trust. Continuous Dynamic Trust may depend on Artificial Intelligence’s success and intent.

In real life, we look to an instance of Uber service to learn more about Dynamic Trust. In a post by Bryan Ware CTO of Haystax Technology (July 2014, summarized here)1  , Uber has an incredibly simple to use software application that automates car calls and billings but also keeps track of the scores of each driver and client. We rate the driver on a five-star scale basis each time we take a trip, and also, the driver scores the passenger accordingly. If the driver slips below the average rating of four point six, the Uber worker is removed. Similarly, if a passenger has a bad rating on his/her previous journey, maybe for being offensive or drunken, the rating is introduced to drivers, and they will decide whether to pick him/her up, say, on a Friday at midnight. The competitive rating system guarantees the integrity of the Uber operation, the safety of drivers and riders, and its name. It weeds out a “bad” driver or customer automatically. A fascinating contrast to how a pre-employment screen and a driving test are offered to taxi drivers and then have no real feedback or monitoring from that moment on. When you think of it like that, you’re basically a five-star driver until you collect your taxi license because there’s no feedback mechanism to track your results. Over the years, I have had a lot of poor taxi drivers, but their boss or the next passenger will not have any means of understanding it because there is no ranking mechanism.

Similarly, with the Airbnb application, we are allowed to rate the property dealer on a scale of 5 and leave feedback on the Airbnb application for a particular property. Also, the property manager can leave feedback and ratings for tourists and customers. This helps both the tourists and property managers to build trust in each other while booking and renting property houses.

So, in the era of ubiquitous data and cheap processing, Dynamic Trust-AI implementation comes into the picture. Easy-to-use and secure AI applications that can collaborate and interact well and have good security and privacy protection and clarify the reasoning behind conclusions or behavior can facilitate Dynamic Trust. The cornerstone of the connection between humanity and Artificial Intelligence is Dynamic Trust.

References
1. Practice, P., Science, T., Updates, P., News, C., & Stories, S. (2014, July 08). Dynamic Trust – What is it and why should you care about it? Retrieved October 28, 2020, from https://haystax.com/dynamic-trust-what-is-it-and-why-should-you-care-about-it/
2. Siau, K., & Wang, W. (n.d.). Building Trust in Artificial Intelligence, Machine Learning, and Robotics: Cutter Consortium. Retrieved October 28, 2020, from https://www.cutter.com/article/building-trust-artificial-intelligence-machine-learning-and-robotics-498981