Based on internal employee brainstorming and consistent with the company's "pushing the limits" spirit, it recently injected augmented reality (AR) into this backend process. It allows users to don AR goggles and immerse themselves into the 3D digital environment. AR allows management, an engineering company or an insurance company, often in different locations around the globe, far from the drone pilot and onsite inspection crew, to not only review the asset set for their own purposes in near real time, but to now also virtually transport themselves into the asset first-hand.

Algorithmic systems that employ machine learning play an increasing role in making substantive decisions in modern society, ranging from online personalization to insurance and credit decisions to predictive policing. But their decision-making processes are often opaque--it is difficult to explain why a certain decision was made. We develop a formal foundation to improve the transparency of such decision-making systems. Specifically, we introduce a family of Quantitative Input Influence (QII) measures that capture the degree of influence of inputs on outputs of systems. These measures provide a foundation for the design of transparency reports that accompany system decisions (e.g., explaining a specific credit decision) and for testing tools useful for internal and external oversight (e.g., to detect algorithmic discrimination). Distinctively, our causal QII measures carefully account for correlated inputs while measuring influence. They support a general class of transparency queries and can, in particular, explain decisions about individuals (e.g., a loan decision) and groups (e.g., disparate impact based on gender). Finally, since single inputs may not always have high influence, the QII measures also quantify the joint influence of a set of inputs (e.g., age and income) on outcomes (e.g.

Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University researchers could provide important insights to this process. Was it a person's age, gender or education level that had the most influence on a decision? Was it a particular combination of factors? CMU's Quantitative Input Influence (QII) measures can provide the relative weight of each factor in the final decision, said Anupam Datta, associate professor of computer science and electrical and computer engineering.

A team of CMU researchers led by Associate Professor Anupam Datta have developed new measurement methods that provide important insight into how machine-learning algorithms make decisions about things like credit applications, job opportunities and medical diagnoses. Machine-learning algorithms increasingly make decisions about credit, medical diagnoses, personalized recommendations, advertising and job opportunities, among other things, but exactly how usually remains a mystery. Now, new measurement methods developed by Carnegie Mellon University researchers could provide important insights to this process. Was it a person's age, gender or education level that had the most influence on a decision? Was it a particular combination of factors?

Between recent controversies over Facebook's Trending Topics feature and the U.S. legal system's "risk assessment" scores in dealing with criminal defendants, there's probably never been broader interest in the mysterious algorithms that are making decisions about our lives. That mystery may not last much longer. Researchers from Carnegie Mellon University announced this week that they've developed a method to help uncover the biases that can be encoded in those decision-making tools. Machine-learning algorithms don't just drive the personal recommendations we see on Netflix or Amazon. Increasingly, they play a key role in decisions about credit, healthcare and job opportunities, among other things.

Between recent controversies over Facebook's Trending Topics feature and the U.S. legal system's "risk assessment" scores in dealing with criminal defendants, there's probably never been broader interest in the mysterious algorithms that are making decisions about our lives. That mystery may not last much longer. Researchers from Carnegie Mellon University announced this week that they've developed a method to help uncover the biases that can be encoded in those decision-making tools. Machine learning algorithms don't just drive the personal recommendations we see on Netflix or Amazon. Increasingly, they play a key role in decisions about credit, healthcare, and job opportunities, among other things.