Unlocking AI for All: How Fiddler AI is Paving the Way For Transparent and Equitable Tech

In the dynamic realm of artificial intelligence (AI), where innovation often outpaces comprehension, the quest for transparency and equity has become paramount. At the vanguard of this transformative movement stands Fiddler AI, led by its Founder and CEO, Krishna Gade. With a steadfast commitment to unlocking the mysteries of AI and making its intricacies accessible to all, Fiddler heralds a new era of transparent and equitable technology. In an era where the black box of AI can seem inscrutable, Fiddler's pioneering approach empowers users to interact with AI systems, unraveling their inner workings and fostering trust in their decisions. As we delve into the evolving landscape of artificial intelligence, Fiddler emerges as a guiding light, illuminating the path towards a future where AI is demystified and embraced as a catalyst for understanding and progress. AIM: What is the origin and significance of the name "Fiddler" and what does "Fiddler" mean? “One day people will refer to using Fiddler to interact with their AI and build trust.” Krishan Gade: The model is essentially becoming more and more like a complex brain on its own. You know, this interaction, where you're trying to understand me and I'm trying to understand you, allows us to exchange questions. Similarly, we wanted to build an interface so that humans can ask questions to AI and essentially tinker with these inputs to observe how the model reacts in response to them. That's where the name comes from. And we are excited; hopefully, one day people will refer to using Fiddler to interact with their AI and build trust. AIM: What motivated the effort to address transparency and explainability in a particular problem domain? What approaches or methods were previously employed for transparency and explainability, and what specific problem statement prompted the need for a new solution or improvement in this regard? “We aimed to establish an enterprise company capable of offering observability and explainability as a service to large enterprises." Krishna Gade: I've been immersed in AI and machine learning for about 20 years now. My first job out of college was at Microsoft Bing as a search quality engineer exactly 20 years ago, when Microsoft was striving to develop a Google-like search engine. One of the strategies we adopted was to build state-of-the-art machine learning algorithms to enhance search quality and provide the best possible search results. At that time, we were implementing neural networks for search ranking, primarily two-layer networks. Since then, I've been exposed to and working on these types of systems. Over the years, I've observed AI and machine learning becoming increasingly complex. Even during the era of two-layer networks, understanding how the network arrived at a particular search or ranking score wasn't straightforward. As time progressed and I moved on to companies like Twitter, Pinterest, and Facebook, these systems evolved into highly intricate entities. We began dealing with deep neural networks, sparse neural networks processing vast amounts of user activity data, including Facebook posts and metadata, to personalize news and ads. Consequently, questions arose—why am I seeing this news story? Why is this particular ad showing up? This raised concerns about transparency. During my tenure at Facebook, I contributed to tools like "Why Am I Seeing This?", which provided easily understandable insights into how the News Feed algorithm operated. This experience led me into the field of explainable AI. I realized that while companies like Facebook and Microsoft could address this need, not every company had the talent, skills, or tools to do so. Therefore, we aimed to establish an enterprise company capable of offering observability and explainability as a service to large enterprises. AIM: What advancements does Fiddler offer in terms of model explainability and transparency compared to traditional methods, and how do these advancements address the limitations of existing approaches? “Fiddler adopts the latter approach, treating the model as a black box and using game-theoretic algorithms to ask counterfactual questions.” Krishna Gade: There are various forms of explainability. There's explainability aimed at the developer, where the focus is on understanding how the model works. For instance, in a simple model like a decision tree, it's clear which pathway triggered a particular decision. However, as models become more complex—such as multi-layer networks, deep neural networks, and large language models—it becomes increasingly challenging to decipher what's happening within the model. That's one aspect of explainability. The other form of explainability is directed towards the user who consumes the model's outputs. For example, in scenarios like credit underwriting in a bank or generating content in customer service, users may not understand why a certain prediction or response is being made. Th

Unlocking AI for All: How Fiddler AI is Paving the Way For Transparent and Equitable Tech

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