In the first of this two-part article, we’re exploring the challenges and risks AI systems pose for organisations, and how they can turn adversity into advantage by establishing the foundations of responsible AI, with ISO/IEC 42001 at its core.
As AI adoption continues at breakneck speed, organisations are under mounting pressure to keep pace. It’s a speed that creates significant risk for today’s AI-engaged organisations, calling for strategic leaders to navigate ethical dilemmas, regulatory demands, and secure public trust to deliver truly responsible AI – without losing their competitive edge. Easier said than done.
Yet it’s by understanding these risks that organisations can identify the current challenges and compliance gaps in their AI operations – paving a path towards a more trusted and resilient AI future. In a recent Alcumus ISOQAR guide ‘Empowering Responsible AI: Understanding ISO/IEC 42001’, we defined the following priority challenges leaders should consider in order to turn the dial from reactive to proactive.
Establishing Ethical AI Governance
AI systems increasingly inform decisions that impact people, businesses, and society; decisions that, without proper oversight, can cause unintended damage. As organisations roll out new and evolved AI tools, defining clear policies and governance frameworks to ensure responsible AI prevails is business critical. Without a structured approach, organisations risk their AI systems becoming a black box of unchecked automation – leading to bias, discrimination, and ethical concerns.
Transparency and Explainability
This idea of an AI ‘black box’ – where an AI system’s underlying algorithmic and decision-making processes are unknown or not communicated – makes it difficult for users to understand how they arrive at AI-enabled decisions. Left unchecked, a lack of transparency and
explainability can ripple out into distrust, regulatory scrutiny, and reputational damage. The challenge for organisations here is how to best balance ever-evolving AI capabilities with clear, interpretable decision-making processes that instill trust and allow stakeholders to validate AI systems at speed, and with ease.
Stakeholder Trust and Accountability
From employees and customers to regulators and investors, building confidence in the fairness and reliability of AI is crucial. Without clear accountability measures in place, organisations stifle assurance and risk backlash when AI systems deliver unintended consequences. From there, it’s a short walk to a lack of trust and stalled AI adoption. The key here is in how to establish transparency and accountability with sound AI governance.
Data Privacy and Security
In vast volumes and with sensitive personal and financial information at play, AI feeds on data. While it simply can’t function without it, AI-enabled organisations will struggle to function if they can’t protect it. But against a backdrop of existing cybersecurity and data privacy requirements and strategies, ensuring specialised AI data governance is also now prioritised will be the difference between an organisation’s AI systems becoming an asset or a liability.
These challenges are not exhaustive; each will change depending on the type and technical complexity of AI applications being deployed by an organisation. But neither are they insurmountable. By addressing them head on and putting a structured framework in place, they present new opportunities for the organisation. It’s precisely where ISO/IEC 42001 comes into its own – and where we’ll be heading in part two.
Looking to start exploring the current challenges and compliance gaps in your AI operations? Check out our ISO/IEC 42001 Gap Analysis, here.
To download the Alcumus ISOQAR guide to ‘Empowering Responsible AI: Understanding ISO/IEC 42001’, click here.
