“Operationalizing AI Trust,” in simple terms, means creating a quantifiable measurement of all of the AI’s dimensions. But how can companies create that quantifiable measurement and which dimensions should they measure?
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recent survey by IBM reveals that despite the need to advance ethical AI, there’s still a gap between business leaders’ intentions and meaningful actions as well as between humans and AI. While 80% of CEOs are ready to embed AI ethics into their business practices, less than 25% have operationalized them. Moreover, less than 20% said their company’s actions were consistent with its AI ethics principles. The disconnect between humans and AI stems from the absence of the
foundation of similarity. Humans do not understand the motivations, reasoning methods, and reactions of AI towards certain situations.
To achieve ethical AI, companies need to operationalize AI Trust. It’s a non-negotiable requirement. Clients, partners, and all other stakeholders can never feel confident that the AI system is responsible and ethical unless the AI is transparent. That is, making AI solutions
explainable so that stakeholders can understand the inner workings of their AI systems or why their AI solutions do what it has been programmed to do.
It is imperative that companies must build ethicality into every aspect of AI. Standards and protocols for efficacy, ethics, and trustworthiness must be present in the design, development, deployment, and management of the AI system. As such, it is vital to operationalize AI ethics by indicating quantifiable metrics on all dimensions of AI in line with ethical standards that the system must meet. These quantifiable metrics must include track records, transparency, and responses to new situations. In this regard, AI must meet two levels of trustworthiness: technical trust and ethical trust.
Technical trust refers to trustworthiness in terms of functionality, including accuracy, robustness, resiliency, security, and explainability. This covers the capability of an AI system to perform its tasks with minimal error, less susceptibility to modifications, and justifiability in actional and decisional aspects.
Ethical trust is trustworthiness in social and ethical responsibility. This covers privacy, fairness, interpretability, transparency, and accountability. Ethical trust, however, is susceptible to human bias that may have been fed into the system during the development of the AI model. This is where solutions such as
Konfer Confidence Cloud can operationalize trust in AI applications for organizations. While ethical trust is hard to achieve — because AI systems are susceptible to being infiltrated by bias from the training data fed into the system — Konfer Confidence Cloud is designed to perform “mapping,” which automatically gathers all data used for AI modeling, development, etc. It then measures and manages AI-powered applications across all stages of solution development, deployment, and production, thereby elevating trust.
By achieving AI Transparency and operationalizing AI Trust, organizations can obtain sufficient evidence to assume that their AI system is responsible and ethical. And when the evidence says that their AI is responsible and ethical, they can be confident in their AI solutions.
Operationalize AI Trust with Konfer
Konfer provides a solution for businesses struggling with the ethical aspect of AI systems. To operationalize the principles of trust, Konfer Confidence Cloud offers a rich set of capabilities:
- KonferKnowledge is the repository of all AI software assets and associated attributes, including AI/ML applications, models, and data.
- KonferConfidence is a quantitative measure that enables businesses to profile and measure AI/ML applications on performance, robustness, privacy, compliance, and regulatory risk factors. Scores are computed using metrics and observations that are aggregated over time and available in detail as a part of the following information cards.
- KonferTrust generates operational alerts and reports; integrates with an organization’s service management and collaboration systems like Slack for the stakeholders to be alerted to noncompliance, automatically documenting the compliance status of AI/ML applications, models, and data to internal and external guidelines, standards, and directives with the help of Konfer Confidence Scores and Cards.
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.
Set up a demo with us today to find out how your business can achieve ethical and responsible AI.
