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Google Creates New Explainable AI Program To Enhance Transparency and Debugability




Just recently, Google announced the creation of a new cloud platform intended to make gaining insight into how an AI program renders decisions, making debugging a program easier and enhancing transparency. As reported by The Register, the cloud platform is called Explainable AI, and it marks a major attempt by Google to invest in AI explainability.

Artificial neural networks are employed in many, perhaps most, of the major AI systems employed in the world today. The neural networks that run major AI applications can be extraordinarily complex and large, and as a system’s complexity grows it becomes harder and harder to intuit why a particular decision has been made by the system. As Google explains in their white paper, as AI systems become more powerful, they also become more complex and hence harder to debug. Transparency is also lost when this occurs, which means that biased algorithms can be difficult to recognize and address.

The fact that the reasoning which drives the behavior of complex systems is so hard to interpret often has drastic consequences. In addition to making it hard to combat AI bias, it can make it extraordinarily difficult to tell spurious correlations from genuinely important and interesting correlations.

Many companies and research groups are exploring how to address the “black box” problem of AI and create a system that adequately explains why certain decisions have been made by an AI. Google’s Explainable AI platform represents its own bid to tackle this challenge. Explainable AI is comprised of three different tools. The first tool a system that describes which features have been selected by an AI and it also displays an attribution score which represents the amount of influence that a particular feature has on the final prediction. Google’s report on the tool gives an example of predicting how long a bike ride will last based on variables like rainfall, current temperature, day of the week, and start time. After the network renders the decision, feedback is given that displays which features had the most impact on the predictions.

How does this tool provide such feedback in the case of image data? In this case, the tool produces an overlay that highlights the regions of the image that weighted most heavily on the rendered decision.

Another tool found in the toolkit is the “What-If” tool, which displays potential fluctuations in model performance as individual attributes are manipulated. Finally, the last tool enables can be set up to give sample results to human reviewers on a consistent schedule.

Dr. Andrew Moore, Google's chief scientist for AI and machine learning, described the inspiration for the project. Moore explained that around five years ago the academic community started to become concerned about the harmful byproducts of AI use and that Google wanted to ensure their systems were only being used in ethical ways. Moore described an incident where the company was trying to design a computer vision program to alert construction workers if someone wasn’t wearing a helmet, but they become concerned that the monitoring could be taken too far and become dehumanizing. Moore said there was a similar reason that Google decided not to release a general face recognition API, as the company wanted to have more control over how their technology was used and ensure it was only being used in ethical ways.

Moore also highlighted why it was so important for AI’s decision to be explainable:

“If you've got a safety critical system or a societally important thing which may have unintended consequences if you think your model's made a mistake, you have to be able to diagnose it. We want to explain carefully what explainability can and can't do. It's not a panacea.