The Computing Community Consortium (CCC) continues to be amazed by the accomplishments of our council members. On May 31, Pamela Wisniewski was appointed to the Defense Advanced Research Projects Agency (DARPA) Information Science and Technology (ISAT) Study Group beginning in August 2024. The ISAT Study Group, established by DARPA in 1987, brings together 40 scientists and engineers to identify emerging areas in computer science and information technology, and recommend potential future research directions. CCC believes she is an excellent fit for the group due to her extensive work in advancing socio-technical interdisciplinary research in computing. Her contributions to CCC’s initiatives on AI accountability, the development of consent language for digital […]

Imagine a situation where an AI system labels someone as high risk without taking into account important factors like their low income, family responsibilities, and parental duties. It’s crucial to realize that most defendants, especially those from marginalized backgrounds, aren’t likely to pose a serious threat to society. Additionally, past infractions, especially those that occurred years ago, should not be the sole basis for predicting future inmate behavior. Judges need to be careful about relying only on AI algorithms because they can’t fully understand the complexities of human life and may not consider all the relevant details. Human judgment, which considers all aspects of a person’s situation, should always guide […]

In another installment of CCC’s Weekly Computing News, we are highlighting a fascinating article by the University of Michigan News. This article explores how artificial intelligence is being used to develop tools that can understand what a dog’s bark means. Using AI to decode dog vocalizations Researchers at the University of Michigan, in collaboration with Mexico’s National Institute of Astrophysics, Optics and Electronics (INAOE), achieved a breakthrough in animal communication research by repurposing AI models originally trained for human speech analysis to understand dog barks. Led by CCC Council Member Rada Mihalcea, the team adapted the Wav2Vec2 machine-learning model to interpret a dataset of dog vocalizations collected by the INAOE.  […]

The Computing Community Consortium (CCC) council member Pamela Wisniewski was featured in the Center for Democracy & Technology Research for an important study on online risk experiences of youth and the interventions needed to protect them from harm. The study highlights the necessity of a nuanced approach to youth online safety, emphasizing that not all young people face the same level of risk. Instead of implementing broad measures, tailored interventions should address the specific needs of different youth subgroups.  Using the survey responses from their study, Wisniewski and her colleagues identified five risk profiles with a unique set of online and offline risks, some including low risk, increased self harm […]

In the rapidly evolving landscape of computing, grand challenges emerge as pivotal milestones that shape the trajectory of technological advancement and societal progress. These challenges show the most complex and pressing issues that the computing community faces, spanning diverse domains from artificial intelligence and cybersecurity to quantum computing.  What makes a grand challenge successful? Here are three examples throughout history. Human Genome Project (HGP) The Human Genome Project (HGP) was a landmark international endeavor that aimed to map, sequence, and understand the entirety of the human genome, our genetic blueprint. It spanned over a decade and involved thousands of scientists from across the globe. Its primary goals included mapping the […]

This week we discovered an interesting article from ACM Queue, a bimonthly magazine of the Association for Computing Machinery (ACM). This article, written by researchers Jinnan Guo, Peter Pietzuch, Andrew Paverd, and Kapil Vaswanin, explores how as the demand for trustworthy AI systems grows, the confluence of Federated Learning (FL) and Confidential Computing emerges as a promising solution.  Trustworthy AI Using Confidential Federated Learning The article emphasizes the crucial need to ensure the trustworthiness of AI systems, particularly in safeguarding personal information. It highlights two key methodologies, Federated Learning (FL) and Confidential Computing, as effective approaches to achieving this goal. While FL addresses privacy concerns by enabling collaborative model training […]