Posted September 14, 2022
How to Create a Culture of Robustness, Transparency, and Security Leading to Trusted AI/ML Solutions.
Artificial intelligence (AI) is transforming the business landscape by serving a supporting function in processing and analyzing large volumes of data. With the continuous adoption of AI technologies around the world, it is only a matter of time until it becomes an indispensable part of businesses. As such, it is unsurprising that the AI market is currently valued at USD 93.5 billion and is predicted to expand at a compound annual growth rate (CAGR) of 38.1 percent until 2030.
Despite the positive advancements introduced by AI, businesses can have reservations about adding AI technology to their systems due to ethical reasons. These issues stem from mistrust of AI, inhibiting business leaders from reaping the full benefits of this exciting new technology.
At its core, the trust gap is due to the missing foundation of similarity between humans and AI systems. Humans do not understand the motivations, reasoning methods, and reactions of AI towards certain situations. As humans, we build trust on quantifiable metrics such as track record, transparency, and responses to new situations.
In order to address the trust gap, AI must meet two levels of trustworthiness: technicality and ethicality.
Technical trust refers to trustworthiness in terms of functionality. These include accuracy, robustness, resiliency, security, and explainability, which reflects the capability of an AI system to perform its tasks with minimal error, minimal susceptibility to changes and attacks, and justifiability in actional and decisional aspects. With the level of technology currently available, most AI systems can meet these technical requirements due to the numerous quantifiable tests conducted on AI technologies to ensure repeatability, predictability, and reliability.
On the other hand, ethical trust is trustworthiness in social and ethical responsibility. This is a more pressing concern compared to technical trust since this takes on a more human approach to trust. Ethical trust includes privacy, fairness, interpretability, transparency, and accountability. These factors ensure professional competence, reputation, and good governance. However, ethical trust is hard to achieve due to bias. AI systems are highly susceptible to being infiltrated by bias since it learns how to make decisions based on the training data fed into the system. This data could potentially contain biased human decisions and historical or social inequities.
Ethicality must be built into every aspect of AI. Standards and protocols for efficacy, ethics, and trustworthiness must be present as you design, develop, deploy, and manage your AI system. As such, it is vital to operationalize AI ethics by indicating a quantifiable metric on all dimensions of AI in line with ethical standards that the system must meet.
Konfer provides a solution for businesses struggling with the ethical aspect of AI systems. Konfer operationalizes AI trust through the Konfer Confidence Cloud, a solution that helps business leaders achieve confidence in AI by elevating traditional AI/ML development life cycles into trusted AI/ML development life cycles and providing verification, visibility, and trust management framework. To do this, Konfer Confidence Cloud follows a three-step process of mapping, measuring, and managing internal and third-party AI/ML systems:
Konfer Confidence Cloud helps businesses avoid compliance challenges that may entail security, legal, regulatory, and financial repercussions. Konfer’s solution empowers business leaders to trust AI-powered decision-making and maintain a competitive advantage.
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