The Biggest Mystery in AI: How to Navigate Explainability

AI has undoubtedly transformed numerous sectors, presenting both opportunities and challenges. It is crucial for business leaders to understand the legal and ethical implications surrounding the use of AI.

One of the central concerns with AI algorithms is that their outcomes cannot easily be explained. While humans can often articulate the rationale behind their decisions, some algorithms yield results that are difficult for human experts to comprehend or explain.

This poses a fundamental question: if algorithms are not accountable for their decisions, who should be held responsible?

In this article, we will explore the challenge of explainability in implementing an AI strategy in your organisation, and identify how we can foster trust and maximise the value of AI within your organisation.

The Greatest Obstacle to AI Adoption

A major challenge of implementing AI is understanding and interpreting the decision-making processes of artificial intelligence systems. As AI technologies become increasingly complex and sophisticated, they often operate as black boxes, making it difficult for humans to comprehend the rationale behind their outputs or predictions.

It becomes especially problematic when AI systems make critical decisions that impact individuals' lives, such as in healthcare, finance, or legal domains. If an AI denies someone a loan or recommends a medical treatment, it is crucial to understand the reason to ensure fairness, accountability, and prevent bias or discrimination.

This problem is two-fold. It harms our ability to trust AI outcomes, and it widens the gap between strategic and technical teams aligning on an AI strategy. 

Fostering Trust in AI

This lack of explainability in AI systems can undermine their credibility, hindering their adoption and limiting the value they can bring to an organisation.

Stakeholders, including customers and employees, may question the reliability and fairness of AI-driven decisions if they cannot comprehend the underlying mechanisms. This scepticism can impede the implementation of AI solutions that have proven benefits in various domains.

To overcome this challenge, a potential solution lies in adopting a hybrid approach that combines the power of machine learning (ML) models with human oversight. By allowing AI algorithms to make recommendations that are subject to human approval or modification, organisations can strike a balance between automation and human expertise.

This approach enables decision-makers to understand, verify, and refine the outputs generated by AI systems, ensuring transparency and accountability in the decision-making process.

Navigating the Language Barrier

Communication is another challenge. Explaining AI algorithms in a language that resonates with business stakeholders has proven difficult. Technical jargon and complex models can hinder effective communication, making it difficult for strategic professionals to grasp the underlying decision-making process.

When evaluating how beneficial an AI system could be for your organisation, it must be possible to reduce the explanation to a succinct answer that aligns with the language and expectations of your business stakeholders.

Imagine an AI-powered recommendation engine that suggests personalised marketing strategies for different customer segments. Explaining the algorithm's logic and factors that contribute to its recommendations in simple, business-oriented terms can bridge the gap between technical complexity and strategic decision-making.

Moving Forward: Explainability in Human Language

As we progress in the evolution of AI, the next crucial step is to provide AI systems with the ability to work in a way that is easily explainable using basic human language.

By translating complex algorithms into simple, business-oriented explanations, we empower stakeholders to comprehend and trust AI-driven decisions. This will unlock new possibilities for leveraging AI, driving innovation, and gaining a competitive edge. This makes AI more approachable, empowering users and fostering trust in the technology.

AI in Your Organisation

It is imperative for business leaders to navigate the legal and ethical landscape surrounding AI technology. By recognising the challenges associated with AI explainability and emphasising the importance of trust and credibility, you can strategically harness the potential of AI within your organisation.

By striving for AI systems that provide explanations in easily understandable human language, you pave the way for widespread adoption and utilisation of AI technologies. Furthermore, through a hybrid approach that combines machine learning capabilities with human oversight, you can ensure accountability, transparency, and comprehension of AI-driven decisions.