give me some references according to the paragraph:Trust in technical AI is the first step in developing trust toward AI on the part of employees who would actually use any such system. A faithful AI response, which seems producible, accessible, and reliable, will find more conformist user behavior over time. This is trust developed through interaction, turned into confidence that the AI system is capable and supportive of any decision-making process. Technical trust in AI does not become defined only by the accuracy of predictions or analyses, but also by how the same AI performs various tasks in generalizing its performance ability across a series of contexts. For instance, tools used for financial forecasting, diagnostics in healthcare, or even applications for recruitment have to maintain a certain level of accuracy to make employees use them while having trust in them. If problems are noticed with the outputs that AI can give and provide, it can fast diminish the level of trust given by employees since they can interpret this as that the system is not effective and start resisting its application. As an upshot, organizations should also concentrate on rigorous testing, validation, and quality assurance in developing AI to bring this system up to its standard performance. Trust in transparency and liability ensures that there will also be enduring trust. Beyond this, it means that employees also want to see artificial intelligence as transparent, understandable, and accountable. Indeed, trust in AI systems is likely to increase if employees are privy to how the decisions are made, which data is in use, and how the outputs come about. This awareness reduces uncertainty, and the employees feel encouraged to evaluate the reasonableness of the AI recommendation. Initiatives on the explainable AI create such awareness by opening up the curtain on the internal operations of AI systems. Given that XAI is capable of breaking down AI reasoning into understandable parts, it enables employees to work with these systems correctly without undue skepticism toward trust. Examples include AI tools that could express reasons for their recommendations in understandable terms. This would go a long way to reduce apprehensions regarding their opaque decision-making. There is also transparency as part of the ethical dimensions, which will make sure that the AI systems can be held accountable and their outcomes scrutinized for any bias or error. On the ethical dimension of AI trust, therefore employees will trust systems more if they are acting transparently and keeping with the norms of the organization. Ethical AI involves a commitment to fairness, equity, and accountability in developing, deploying, and using AI systems. In this regard, trust in AI by employees depends heavily on the perception of bias; even an inkling of discrimination or unfairness takes away confidence and nourishes resistance. For example, AI used in hiring or assessing work needs to not have gender, racial, or certain demographic biases. Truly, the apprehension for their revelation and suppression is that organizations make stringent efforts so that Artificial Intelligence setup provides fair and just results. It goes beyond that, considering that there is a trust of employees that information is being handled responsibly and safeguards are in place to protect against information misuse. By making AI ethics a priority, organizations show, even when fairness and accountability can sometimes be trusted, therefore building trust and a good perception of systems that use AI.