From voice recognition devices to intelligent chatbots, AI has transformed our lives. But, every good thing also has a downside, and AI is no exception to this rule. Leading technology figures have warned of the looming dangers of AI, including Stephen Hawking, who said it could be the "worst event in the history of our civilization." Here are six times AI went a little too far and left us scratching our heads. Academic research is the backbone of scientific advancements and knowledge.

An HU Analytics Ph.D. student's research paper, recently named "Best Overall Paper" at the Tackling Climate Change with Machine Learning workshop at the NeurIPS2020 Conference, has been featured on Forbes.com. In the article, "A.I. needs to get real--and other takeaways from this year's NeurIPS," author Jeremy Kahn notes that (HU Analytics Ph.D. student) Lyra Wang and her collaborators have teamed "to create a machine learning system to automatically predict areas of oil and natural gas drilling sites that are likely to leak methane, the heat trapping gas that is 84 times more potent than carbon dioxide and a major contributor to global warming." Wang's paper, titled "A Machine Learning Approach to Methane Emissions Mitigation in the Oil and Gas Industry," in a section of the article titled, "An Eye of AI Research." To view the article, visit this link. Accredited by the Middle States Commission on Higher Education, Harrisburg University is a private non-profit university offering bachelor and graduate degree programs in science, technology, and math fields to a diverse student body.

In September 2019, four researchers wrote to the publisher Wiley to "respectfully ask" that it immediately retract a scientific paper. The study, published in 2018, had trained algorithms to distinguish faces of Uyghur people, a predominantly Muslim minority ethnic group in China, from those of Korean and Tibetan ethnicity1. China had already been internationally condemned for its heavy surveillance and mass detentions of Uyghurs in camps in the northwestern province of Xinjiang -- which the government says are re-education centres aimed at quelling a terrorist movement. According to media reports, authorities in Xinjiang have used surveillance cameras equipped with software attuned to Uyghur faces. As a result, many researchers found it disturbing that academics had tried to build such algorithms -- and that a US journal had published a research paper on the topic. And the 2018 study wasn't the only one: journals from publishers including Springer Nature, Elsevier and the Institute of Electrical and Electronics Engineers (IEEE) had also published peer-reviewed papers that describe using facial recognition to identify Uyghurs and members of other Chinese minority groups. The complaint, which launched an ongoing investigation, was one foray in a growing push by some scientists and human-rights activists to get the scientific community to take a firmer stance against unethical facial-recognition research.

The Coalition for Critical Technology (CCT) penned a letter opposing the publication of research called "A Deep Neural Network Model to Predict Criminality Using Image Processing." At the time of publication the letter has more than 1,000 signatures from researchers, practitioners, academics, and others. According to a press release from Harrisburg University, the paper is slated for publication in a book series from Springer Publishing, and the letter urges readers to demand that Springer pull the paper and condemn the use of criminal justice statistics to predict criminality. The use of algorithms in predictive policing is a fraught subject. As the CCT letter elaborates, criminal justice data is notoriously flawed.

The news: An open letter from a growing coalition of AI researchers is calling out scientific publisher Springer Nature for a conference paper it reportedly planned to include in its forthcoming book Transactions on Computational Science & Computational Intelligence. The paper, titled "A Deep Neural Network Model to Predict Criminality Using Image Processing," presents a face recognition system purportedly capable of predicting whether someone is a criminal, according to the original press release. It was developed by researchers at Harrisburg University and was due to be presented at a forthcoming conference. The demands: Citing the work of leading Black AI scholars, the letter debunks the scientific basis of the paper and asserts that crime-prediction technologies are racist. It also lists three demands: 1) for Springer Nature to rescind its offer to publish the study; 2) for it to issue a statement condemning the use of statistical techniques such as machine learning to predict criminality and acknowledging its role in incentivizing such research; and 3) for all scientific publishers to commit to not publishing similar papers in the future.

The last few decades have witnessed innovations in modern medicine, science and technology at a much faster rate than at any time before in history. A large portion of the credit goes to computers, for helping us solve problems more efficiently and therefore at a faster pace. Most recently, this has included the rise of Artificial Intelligence (AI), machine learning as well as neural networks that can simulate human thought patterns. They can then apply their more efficient brains to issues that desperately require resolution, many of which are in STEM fields like medicine or cryptography. STEM stands for science, technology, engineering and mathematics, but a far wider range of academic disciplines fall under this description. Courses one could study range from aerospace engineering and astronomy to civil engineering and statistics.

A robot holding a human brain in a virtual display isolated on a binary data numbered background, as an Artificial Intelligence (AI) in futuristic digital technology and medical concept/3-D illustration. The last few decades have witnessed innovations in modern medicine, science and technology at a much faster rate than at any time before in history. A large portion of the credit goes to computers, for helping us solve problems more efficiently and therefore at a faster pace. Most recently, this has included the rise of Artificial Intelligence (AI), machine learning as well as neural networks that can simulate human thought patterns. They can then apply their more efficient brains to issues that desperately require resolution, many of which are in STEM fields like medicine or cryptography. STEM stands for science, technology, engineering and mathematics, but a far wider range of academic disciplines fall under this description.

For thousands of years, humans have recognized patterns, gathered and analyzed data. Identifying patterns and using this information has been key to our evolution, as well as playing an important role in our pastime activities. Whether it is making a difference between animals that want to kill us and those who don't, categorizing plants based on their edibility, seeing patterns in the stars, or creating algorithms that know exactly which cat video we want to see next on Youtube. Although data and pattern recognition have been around forever, the way we use, store and spread this information has changed drastically. While at the beginning we had to rely on word-of-mouth and cave drawings to spread knowledge, nowadays we can store and spread trillions of gigabytes of information without much effort.

Businessman on blurred background using digital artificial intelligence (AI) interface 3D rendering. An artificial intelligence (AI) and Blockchain start-up with backing from Harrisburg University in Pennsylvania is developing a completely new way of utilizing and processing data by integrating AI and "smart logic" into every bit of data. The goal is to build what is described as an "AI Superhighway" according to the tech protagonists with the vision behind the project. Essentially, the proposition goes that by embedding every piece of data with artificial intelligence, otherwise "dumb data", which requires an application to become useful, becomes valuable and "smart." This means the "AI Thought" attaches to data allows the digital information to act on its own.

After recent school shootings in parts of the country and more than a dozen threats made against school districts here in Central Pennsylvania, many wonder how safety within schools could be improved. A professor from Harrisburg University says he knows something that could help: technology. Advanced technology is right at our fingertips, and Ron Jones, a cyber security professor at Harrisburg University says lawmakers should be pushing to have it within our schools. "I don't see anybody in any political spectrum standing up, making that kind of profound statement," he said. Jones says technology like facial recognition could save lives.