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Explainability, Legality & AI: Why Is It Important?

In the ever-evolving landscape of Artificial Intelligence (AI), understanding the concepts of explainability and legality is crucial. These aspects not only dictate the transparency and accountability of AI systems but also ensure compliance with legal regulations. Let’s delve into why AI explainability and legality are of utmost importance for any organisation beginning its journey into AI…

What Is ‘Explainability’?

Explainability in AI refers to the ability to understand and interpret the decisions made by AI systems. It involves transparency in how the AI and Machine Learning (ML) systems arrive at their conclusions. However, not all AI systems can provide explanations for their decisions. This makes it imperative to choose the right AI platform for specific tasks, especially when decisions impact individuals or critical processes within sectors like social housing service delivery and/or standards.

Why Is ‘Explainability’ Important When Adopting AI-Powered Systems?

Explainability holds significant importance in the adoption of AI-powered systems for several reasons. Firstly, it fosters trust and confidence among users by providing insights into the decision-making process. This transparency allows stakeholders to understand and justify the actions taken by AI platforms. Perhaps most pertinently though, in sectors such as social housing (where decisions directly affect people’s lives), explainability ensures accountability and fairness.

In the realm of AI, legal frameworks play a pivotal role in governing its development and deployment. Currently, discussions and initiatives are underway both in the UK and worldwide to establish comprehensive regulations for AI technologies. For instance, a proposed new EU AI directive aims to lower evidentiary hurdles for victims of AI-related products or services, potentially facilitating the establishment of claims against AI operators. It’s crucial for organisations to stay informed about these legal developments to ensure compliance and mitigate potential risks.

Why Do Organisations Need To Be Cautious About The Data Used To Train AI Models?

The data used to train AI models carries significant implications for legal compliance and ethical considerations. Recent cases, such as the controversy surrounding Meta’s LLaMA model, highlight the importance of using authorised data sources and adhering to copyright laws. Unauthorised data usage can lead to legal challenges, similar to the issues faced by pioneers like Napster in the music streaming industry. Therefore, organisations must exercise caution and ensure that their AI models are trained using ethically sourced and legally compliant data to avoid potential legal infringements.

Misuse of AI technologies can result in severe legal consequences for organisations. Whether it’s data privacy violations, discrimination issues, or intellectual property infringement, AI-related legal challenges can lead to reputational damage, financial losses, and regulatory penalties. By prioritising legal compliance and ethical considerations in AI development and deployment, organisations can mitigate these risks and build trust with stakeholders.

As we’ve explored in this article, explainability and legality are indispensable aspects of AI implementation. By heightening the focus on transparency, accountability, and legal compliance, organisations can navigate the complexities of AI governance and harness its transformative potential responsibly. As AI technologies continue to advance, it’s essential for organisations to stay vigilant and proactive in addressing these critical aspects. For further exploration of some of the key considerations when adopting AI, check out Mobysoft’s Introducing AI Into Your Organisation guide. Click the image below to download your copy today!

Mobysoft’s Introducing AI Into Your Organisation: A Guide for the UK Social Housing Sector

Dean Quinn
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