Artificial intelligence-inspired policing technology and techniques like facial recognition software and digital surveillance continue to find traction and champions among law enforcement agencies, but at what cost to the public? Some cities like Wilmington, North Carolina, have even adopted AI-driven policing, where technology like ShotSpotter identifies gunshots and their locations. The software also recommends to patrol officers "next best action" based on their current location, police data on past crime records, time of day, and housing and population density. Renée Cummings, data activist in residence at the University of Virginia's School of Data Science, warns that the rules of citizenship are changing with the development of AI-inspired policing technologies. She explains, "If the rules are changing, then the public needs to have a voice and has the right to provide input on where we need to go with these technologies as well as demand solutions that are accountable, explainable and ethical." As artificial intelligence is used toward the development of technology-based solutions, Cummings' research questions the ethical use of technology to collect and track citizen data, aiming to hold agencies more accountable and to provide citizens greater transparency.

Recent advances in machine learning have been made possible by exploiting symmetries and invariants in data. In 2003, Simard, Steinkraus and Platt [15] applied two different tricks in this spirit to achieve a record-breaking 0.40 percent error rate in classifying the MNIST database of handwritten digits. First, they augmented the training set using the observation that handwritten digits are closed under certain elastic distortions. Second, they exploited the translation invariance of images by applying a convolutional neural network architecture. In the time since, both data augmentation and convolutional neural networks have enabled substantial strides in image recognition (e.g., [11, 16]). These engineering feats have inspired various theoretical treatments of symmetries and invariants in data. Mallat's scattering transform [12, 3] provides a principled alternative to convolutional neural networks that exhibits translation invariance and stability to diffeomorphisms. For settings beyond image classification, other symmetries and invariants must be considered.

Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating social unrest, curtailing the spread of misinformation during such events is a significant data challenge. While recent solutions that are based on machine learning have shown promise for the detection of misinformation, most widely used methods include approaches that rely on either handcrafted features that cannot be optimal for all scenarios, or those that are based on deep learning where the interpretation of the prediction results is not directly accessible. In this work, we propose a data-driven solution that is based on the ICA model, such that knowledge discovery and detection of misinformation are achieved jointly. To demonstrate the effectiveness of our method and compare its performance with deep learning methods, we developed a labeled COVID-19 Twitter dataset based on socio-linguistic criteria.

New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the planning, response, recovery, and assessment phases. Accurate and timely information is as crucial as is rapid and coherent coordination among the responding organizations. We are working towards a multi-pronged emergency response tool that provide stakeholders timely access to comprehensive, relevant, and reliable information. The faster emergency personnel are able to analyze, disseminate and act on key information, the more effective and timelier their response will be and the greater the benefit to affected populations. Our tool consists of encoding multiple layers of open source geospatial data including flood risk location, road network strength, inundation maps that proxy inland flooding and computer vision semantic segmentation for estimating flooded areas and damaged infrastructure. These data layers are combined and used as input data for machine learning algorithms such as finding the best evacuation routes before, during and after an emergency or providing a list of available lodging for first responders in an impacted area for first. Even though our system could be used in a number of use cases where people are forced from one location to another, we demonstrate the feasibility of our system for the use case of Hurricane Florence in Lumberton, a town of 21,000 inhabitants that is 79 miles northwest of Wilmington, North Carolina.

When society uses artificial intelligence (AI) to help build judgments about individuals, fairness and equity are critical considerations. On Nov. 12, Brookings Fellow Nicol Turner-Lee sat down with Solon Barocas of Cornell University, Natasha Duarte of the Center for Democracy & Technology, and Karl Ricanek of the University of North Carolina Wilmington to discuss artificial intelligence in the context of societal bias, technological testing, and the legal system. Artificial intelligence is an element of many everyday services and applications, including electronic devices, online search engines, and social media platforms. In most cases, AI provides positive utility for consumers--such as when machines automatically detect credit card fraud or help doctors assess health care risks. However, there is a smaller percentage of cases, such as when AI helps inform decisions on credit limits or mortgage lending, where technology has a higher potential to augment historical biases.

With more than 40 years of experience in the healthcare IT space, Franciscan Missionaries of Our Lady Health System CIO Avery Cloud has seen the value technology brings to healthcare. Some of Mr. Cloud's most memorable moments as CIO at the Baton Rouge, La.-based health system revolve around technology's effect on physicians and patients, ranging from instances when it helped prevent a clinical error to reducing patient anxiety. Prior to joining Franciscan Missionaries of Our Lady Health System, Mr. Cloud served as vice president of innovation and technology at CHI St. Luke's Health in Houston as well as CIO at Wilmington, N.C.-based New Hanover Regional Medical Center and Integris Health in Oklahoma City. Here, Mr. Cloud shares his strategy to build and encourage innovation among staff members. Editor's note: Responses have been lightly edited for clarity and length.

"This is pure corporate laziness," wrote Craig Picken, an executive recruiter based in Wilmington, N.C., who on LinkedIn called the process "D-U-M-B." "Did you hear that?" added Keith Campagna, an Allentown, Penn., regional sales manager for recruiting software company Jobvite. "That was the sound of a whole bunch of well-qualified, passive workers hanging up. Because recruitment is inherently a human process." Companies say they have reason to rethink how they hire now.

As an IBM developerWorks information architect, I gave a presentation last week about cognitive computing to entrepreneurs and staffers at tekMountain, a co-working and tech incubator in Wilmington, North Carolina where I work as a tech mentor. The title is a bit tongue and cheek, but I really tried to position the Watson application development demo I gave as the "applied" part of a series that we launched in the area last year on artificial intelligence. At a previous "Exploring Artificial Intelligence" TechTalk, my buddies Mike Orr (IBM Watson University program chair) and Julian Keith (UNC Wilmington Psychology Chair and brain guy), began a series of conversations about artificial intelligence that quickly blossomed into several different AI events with different AI focuses at different venues. An upcoming talk in this very popular series (for example) is titled "Is artificial intelligence going to do my job better than me?" As a software development enthusiast who sometimes teaches kids and others how to start coding, I naturally conceived of a hands-on version of Watson services as a way to take the conversation further.

The purpose of this study was to develop and test a platform that would assist the Environmental Protection Agency (EPA), and the scientific community at large, in the generation of a human activity and energy expenditure database of sufficient detail to accurately predict human exposures and dose to various pollutants. The monitoring system developed is easily extendable to the collection of other health-related data. Our protocol tested the use of a digital voice recorder to collect activity/location diary data assuming it to be a less burdensome and a more reliable method than using paper and pencil diaries or hand-held computers. We expected the data to be more complete and reliable than retrospective reports (diaries filled out at the end of day) because the recorders are easy to use, the diary entries are made as the events occur, and we expected that participants would be more likely to complete the study because of the reduced burden. The data collection plan was also expected to show that the cost of the transcription of the diary can be reduced substantially by using speech and language processing to translate the digital diaries into the EPA’s Comprehensive Human Activity Database (CHAD).