Generative AI Principles
The full impact of Generative AI on the future of financial services remains a landscape under construction. While its potential to revolutionise productivity and drive economic growth is undeniable, the path forward is not without its challenges and uncertainties.
While high quality, well-organised and properly catalogued data is arguably the most critical foundation for any successful AI deployment, issues surrounding data privacy, bias and ethical considerations, as well as model transparency all demand proactive attention.
Recognising this need for guidance, the SFE Data & AI Steering Group has outlined a number of suggested key principles for the responsible deployment of Gen AI to help navigate this powerful and evolving technology impacting the financial sector.
Implementation of regulatory framework – this encompasses the interpretation of regulation including the ethical and responsible use of Gen AI, with care to be taken to mitigate bias, inaccuracies and “hallucinations”, and provide accountability. Transparency and the ease of explainability should also be considered in this framework.
Data privacy and security – when using an organisation’s data in a generative AI setting, data governance, quality and protection should be front of mind
Fair and inclusive – systems should provide equal access and incorporate inclusive design element to provide accessibility those that require adjustments due to disability
Output Verification: Generative AI requires solid and consistent feedback loops with essential input from a human-in-the-loop
Security and resilience – Generative AI should be resilient to cyber attacks, and aided by responsible usage
Sustainability – prioritising sustainability by optimising resource usage and minimising environmental impact should be a key focus
Collaboration and knowledge sharing – collaboration should be everywhere and include cross industry partnerships. There should also be easily accessible and safe environments to consolidate new skills to help upskill the workforce.
Legal compliance – all generative AI platforms should adhere to local laws and regulations
Continuous improvement – businesses should never stop innovating, training, updating, and ensuring that post deployment monitoring is taking place
To underpin these principles, businesses should provide a central governance model which should consider the benefits of buying vs building system, managing significant changes to the way AI operates or is used. This should remain flexible to ensure it adapts to this rapidly changing technology.
Navigating the integration of Generative AI into financial services is undoubtedly a journey, not a destination. The nine principles outlined here offer a valuable compass, guiding firms towards responsible and effective implementation. While not exhaustive, they underscore the critical importance of balancing innovation with ethical responsibility, transparency, and security.
The dynamic nature of AI necessitates a culture of continuous vigilance and adaptation. Ultimately, adhering to such principles will be crucial for the financial sector to unlock the full potential of Generative AI while maintaining the trust and confidence of shareholders, customers, regulators, and the wider ecosystem.
https://www.scottishai.com/values
https://www.oecd.org/en/topics/sub-issues/ai-principles.html