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The US government's mighty DARPA last year kicked off a research project designed to make systems controlled by artificial intelligence more accountable to their human users.
The Defense Advanced Research Projects Agency, to use this $2.97bn agency its full name, is the Department of Defense's body responsible for emerging technology for use by the US armed forces. It's not all military applications, however. Significantly, it was DARPA's early funding of packet-switching network the Advanced Research Projects Agency Network (ARPANET) more than 40 years ago that helped bring about the internet.
Date - Issue - Heart - Intelligence - XAI
Coming bang up to date, the issue at the heart of the Explainable Artificial Intelligence (XAI) programme is that AI is starting to extend into many areas of everyday life yet the internal workings of such systems are often opaque, and could be concealing flaws in their decision-making processes.
The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs). Researchers have found that vast sets of example data are the way to train up such systems to produce the desired results, whether that is picking out a face from a photograph or recognising speech input.
Systems - Box - Developers - Decision - Areas
But the resultant systems often turn out to operate as an inscrutable "black box" and even their developers find themselves unable to explain why it arrived at a particular decision. That may soon prove unacceptable in areas where an AI's decisions could have an impact on people's lives, such as employment, mortgage lending, or self-driving vehicles.
Because of this, a number of organisations as well as DARPA have started to take an interest in making AI systems more accountable, or at least able to explain themselves so that their decision-making processes can be tweaked if necessary. Oracle, for example, has...
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