Unexplainable AI: An impediment to Enterprise adoption of AI
How opacity of deep learning models is a hindrance to widespread adoption of AI in Enterprises?
Introduction
These days, most conversations about digital transformation initiatives in enterprises, across Industries, are centred around the application of Artificial Intelligence (AI). Rightly so, as AI is promising unprecedented advancements: from enhancing operational efficiency to revolutionizing customer experiences; the potential benefits of AI are vast. However, despite the enthusiasm surrounding AI, the adoption of this technology in enterprises faces a significant hurdle — the unexplainability of AI systems.
The Challenge of Unexplainability
One of the most significant impediments to widespread adoption of AI in enterprises is the opacity of AI decision-making processes. Many advanced AI models, particularly those based on deep learning, operate as black boxes, making it extremely difficult to interpret the reasoning behind their predictions or decisions. Opaque systems carry significant security and compliance risk for Organizations; as hackers can sneak in malicious behaviour, undetected, into opaque systems. Take for example the research done by engineers at Anthropic and published in the paper: “Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training”. They show how Large Language Models (LLMs) can be trained to exhibit malicious behaviour only under certain conditions. For example, it is possible to train a model to generate malicious code only if the user is from a certain company. It is this lack of transparency in model behaviour that raises critical questions about accountability, trust, and ethical considerations, all of which are extremely important in an enterprise setting.
Consequences of Unexplainability
Enterprises operate in a complex environment where decisions have far-reaching consequences in many areas, some of which are: Regulatory Compliance, Risk Management, Employee Trust and Customer Confidence. Therefore, it is not only necessary to understand but also to trust these AI systems. Unexplainable AI poses a barrier to achieving this trust, as stakeholders are often hesitant to rely on systems they cannot comprehend.
Here are a few ways we can make AI systems safe for enterprise adoption: (i) making models interpretable, (ii) regularly auditing models for biases and unintended consequences and (iii) training employees on how to effectively work alongside AI systems.
Conclusion
Unexplainable AI poses a significant impediment to the widespread adoption of AI in enterprises. To overcome this challenge, organizations must prioritize transparency, accountability, and ethical considerations in their AI initiatives. By embracing explainable AI models and fostering a culture of trust and collaboration, enterprises can unlock the full potential of AI while navigating the complexities of the modern business landscape.