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Navigating AI Transparency: Insights for Businesses
22 November 2022
Our UCL CDI team collaborated with Holistic AI to host a three-part event series, placing a spotlight on AI ethics and risk management.
Our UCL CDI team collaborated with Holistic AI to host a three-part event series, placing a spotlight on AI ethics and risk management. This series dived deep into the pivotal topic of AI transparency and its profound repercussions for businesses. Our primary objective in organizing these panel discussions was to stimulate ongoing discourse and foster thought leadership in the domain of AI transparency.
This journey commenced by establishing a clear understanding of AI transparency and subsequently delving into strategies aimed at augmenting transparency within AI systems. These strategies shed light on the far-reaching implications they hold, both for businesses and the users of AI systems.
What is AI Transparency?
Artificial intelligence (AI) encompasses a wide array of algorithmic systems designed to accomplish predefined objectives, generating outputs such as images, predictions, recommendations, or decisions. These AI systems can either support or substitute human decision-making and actions. A considerable subset of these systems falls into the category of "black-box," where the internal workings of the model are either unknown or not interpretable by humans, resulting in a lack of transparency.
AI transparency, a fundamental facet of the Responsible Digital Innovation Lab, represents an overarching concept that includes components like explainable AI (XAI) and interpretability. It stands as a critical concern within the realm of AI ethics, trustworthy AI, and responsible AI. Broadly, AI transparency encompasses three key levels:
- Explainability of Technical Components: Evaluating the degree to which the internal mechanics of the algorithm are comprehensible.
- Governance of the System: Examining the presence of suitable processes and documentation for key decisions within the AI system.
- Transparency of Impact: Assessing whether the abilities and intentions of the algorithms are openly and clearly communicated to relevant stakeholders.
Have you considered the significance of AI transparency for your business in our AI-driven world?
If you're eager for a thorough exploration of this topic and wish to gain deeper insights from our experts, we invite you to read AI Transparency: What Does it Mean for Your Business? This paper summarises the key takeaways from the panel discussions, beginning with an outline of what is meant by AI transparency before discussing how AI can be made more transparent and the implications of this for both businesses and users of AI systems.
