Scientists Increasingly Can’t Explain How AI Works

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AI researchers are warning developers to focus more on how and why a system produces certain results than the fact that the system can accurately and rapidly produce them.

What’s your favorite ice cream flavor? You might say vanilla or chocolate, and if I asked why, you’d probably say it’s because it tastes good. But why does it taste good, and why do you still want to try other flavors sometimes? Rarely do we ever question the basic decisions we make in our everyday lives, but if we did, we might realize that we can’t pinpoint the exact reasons for our preferences, emotions, and desires at any given moment. 

There’s a similar problem in artificial intelligence: The people who develop AI are increasingly having problems explaining how it works and determining why it has the outputs it has. Deep neural networks (DNN)—made up of layers and layers of processing systems trained on human-created data to mimic the neural networks of our brains—often seem to mirror not just human intelligence but also human inexplicability.

Most AI systems are black box models, which are systems that are viewed only in terms of their inputs and outputs. Scientists do not attempt to decipher the “black box,” or the opaque processes that the system undertakes, as long as they receive the outputs they are looking for. For example, if I gave a black box AI model data about every single ice cream flavor, and demographic data about economic, social, and lifestyle factors for millions of people, it could probably guess what your favorite ice cream flavor is or where your favorite ice cream store is, even if it wasn’t programmed with that intention.

These types of AI systems notoriously have issues because the data they are trained on are often inherently biased, mimicking the racial and gender biases that exist within our society. The haphazard deployment of them leads to situations where, to use just one example, Black people are disproportionately misidentified by facial recognition technology. It becomes difficult to fix these systems in part because their developers often cannot fully explain how they work, which makes accountability difficult.

As AI systems become more complex and humans become less able to understand them, AI experts and researchers are warning developers to take a step back and focus more on how and why a system produces certain results than the fact that the system can accurately and rapidly produce them. 

“If all we have is a ‘black box’, it is impossible to understand causes of failure and improve system safety,” Roman V. Yampolskiy, a professor of computer science at the University of Louisville, wrote in his paper titled “Unexplainability and Incomprehensibility of Artificial Intelligence.” “Additionally, if we grow accustomed to accepting AI’s answers without an explanation, essentially treating it as an Oracle system, we would not be able to tell if it begins providing wrong or manipulative answers.”

Black box models can be extremely powerful, which is how many scientists and companies justify sacrificing explainability for accuracy. AI systems have been used for autonomous cars, customer service chatbots, and diagnosing disease, and have the power to perform some tasks better than humans can.

For example, a machine that is capable of remembering one trillion items, such as digits, letters, and words, versus humans, who on average remember seven in their short-term memory would be able to process and compute information at a much faster and improved rate than humans.

Among the different deep learning models include generative adversarial networks (GANs), which are most often used to train generative AI models, such as text-to-image generator MidJourney AI. GANs essentially pit AI models against each other to do a specific task; the “winner” of each interaction is then pitted against another model, allowing the model to iterate itself until it becomes very good at doing that task. The issue is that this creates models that their developers simply can’t explain.  

“I think in a lot of cases, people look to these black box models as a response to lack of resources. It would be very convenient to have an automated system that can produce the kinds of outputs they’re looking for from the kind of inputs they have,” Emily M. Bender, a professor of linguistics at the University of Washington, told Motherboard.

“If you have a dataset consisting of such inputs and outputs, it’s always possible to train a black box system that can produce outputs of the right type—but often much, much harder to evaluate whether they are correct. Furthermore, there are lots of cases where it’s impossible to make a system where the outputs would be reliably correct because the inputs just don’t contain enough information.

When we put our trust in a system simply because it gives us answers that fit what we are looking for, we fail to ask key questions: Are these responses reliable, or do they just tell us what we want to hear? Whom do the results ultimately benefit? And who is responsible if it causes harm?……

Read more…..

Source: Scientists Increasingly Can’t Explain How AI Works

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