The pace of AI innovation is happening so quickly that even the most technologically sophisticated governments are struggling to keep up.

Repressive regimes are working hard on artificial intelligence (AI) systems that control populations and suppress dissent. If these efforts succeed, political protests will be a sentimental relic of the past, squashed before they ever get to the streets. Less dramatic but nonetheless serious risks also exist for AI in the enterprise. What if incorrect AI credit scoring stops consumers from securing loans? Or an attack on the AI model of a self-driving vehicle leads to a fatal accident?

One of the biggest impediments to adoption of new technologies is trust in AI. Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence. Neural networks are a type of artificial intelligence that are modeled after the brain and generate predictions. But can the predictions these neural networks generate be trusted?

One of the biggest impediments to adoption of new technologies is trust in AI. Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence. Neural networks are a type of artificial intelligence that are modeled after the brain and generate predictions. But can the predictions these neural networks generate be trusted?

Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence. Neural networks are a type of artificial intelligence that are modeled after the brain and generate predictions. But can the predictions these neural networks generate be trusted? One of the key barriers to adoption of self-driving cars is that the vehicles need to act as independent decision-makers on auto-pilot and quickly decipher and recognize objects on the road -- whether an object is a speed bump, an inanimate object, a pet or a child -- and make decisions on how to act if another vehicle is swerving towards it.

One of the biggest impediments to adoption of new technologies is trust in AI. Now, a new tool developed by USC Viterbi Engineering researchers generates automatic indicators if data and predictions generated by AI algorithms are trustworthy. Their research paper, "There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks" by Mingxi Cheng, Shahin Nazarian and Paul Bogdan of the USC Cyber Physical Systems Group, was featured in Frontiers in Artificial Intelligence. Neural networks are a type of artificial intelligence that are modeled after the brain and generate predictions. But can the predictions these neural networks generate be trusted?