Emergence of AI Trust Layers & Governance
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P116Keywords:
Artificial Intelligence, Trust Layers, AI Governance, Responsible AI, AI Ethics, Regulatory Compliance, Transparency, Accountability, Fairness, AI Policy, Case StudiesAbstract
The rapid ascent of artificial intelligence has opened up a plethora of possibilities for various sectors; however, it has equally generated some very severe problems with regard to trust, accountability, and the responsible use of the technology. Consequently, as AI systems progressively become the mediators of the decisions in the fields of medical care, finance, governance, and also in our daily lives, issues with fairness, transparency, bias, and taking advantage of the technology have surfaced. There is the concept of “trust layers”, which has been propounded to reconcile this widening gulf that is between dependability and innovation – these are specialised mechanisms, frameworks, and safeguards that double up as checks and balances, assuring that AI is not only efficient but safe, ethical, and in line with human values. The technical guardrails that track the models in real time, the organisational processes that act as a governor on how AI is deployed, and the societal oversight that weighs innovation against collective well-being are some of the levels these layers function in. Decision-making is the core of these trust layers where governance is situated and which acts as the backbone of the responsible conduct of AI by setting standards, forming accountability structures, and instituting transparent practices that make the trust grow with the use. This article outlines how governance, combined with trust-enabling layers, is a game changer for AI making it a technology of certainty rather than one of doubt. We develop a coherent framework demonstrating the implementability of trust layers all through the AI lifecycle, practically facilitated through governance examples. So as to better understand these concepts, we add a real-life situation that exemplifies how these ideas were implemented in an actual AI case, thereby deriving learnings from both triumphs and predicaments. Collectively, these revelations confirm that trust is not a stumbling block to but rather a stepping stone of innovation, and that the fate of AI is so much dependent on our ways of governing and protecting it as on our ability to construct it
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