This report documents the results of a recent survey in the SIGGEN community, focusing on Dual Use issues in Natural Language Generation (NLG). SIGGEN is the Special Interest Group (SIG) of the Association for Computational Linguistics (ACL) for researchers working on NLG. The survey was prompted by the ACL executive board, which asked all SIGs to provide an overview of dual use issues within their respective subfields. The survey was sent out in October 2024 and the results were processed in January 2025. With 23 respondents, the survey is presumably not representative of all SIGGEN members, but at least this document offers a helpful resource for future discussions. This report is open to feedback from the SIGGEN community. Let me know if you have any questions or comments!

Currently, people with disability or difficulty to move their arms (referred to as "patients") have very limited technological solutions to efficiently address their physiological limitations. It is mainly due to two reasons: (1) the non-invasive solutions like mind-controlled prosthetic devices are typically very costly and require expensive maintenance; and (2) other solutions require costly invasive brain surgery, which is high risk to perform, expensive, and difficult to maintain. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, a mechanized intelligent non-invasive neuro-driven prosthetic arm system. Our MindArm system employs a deep neural network (DNN) engine to translate brain signals into the intended prosthetic arm motion, thereby helping patients to perform many activities despite their physiological limitations. Here, our MindArm system utilizes widely accessible and low-cost surface electroencephalogram (EEG) electrodes coupled with an Open Brain Computer Interface and UDP networking for acquiring brain signals and transmitting them to the compute module for signal processing. In the compute module, we run a trained DNN model to interpret normalized micro-voltage of the brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. The experimental results on a fully working prototype demonstrate that, from the three defined actions, our MindArm system achieves positive success rates, i.e., 91\% for idle/stationary, 85\% for shake hand, and 84\% for pick-up cup. This demonstrates that our MindArm provides a novel approach for an alternate low-cost mind-controlled prosthetic devices for all patients.

The use of AI analytics in health informatics has seen a rapid growth in recent years. In this talk, we look at AI analytics use in managing chronic health conditions such as diabetes, obesity, etc. We focus on the challenges in managing these conditions especially with drug-free approaches due to the variations in individual circumstances. These variations directed the research into user-centric approach leading to variety of research questions. In this short paper, we give examples from recent and current research work and conclude with what, in our opinion, to be the next steps and some remaining open research questions.

Artificial intelligence is more ubiquitous in multiple domains. Smartphones, social media platforms, search engines, and autonomous vehicles are just a few examples of applications that utilize artificial intelligence technologies to enhance their performance. This study carries out a scoping review of the current state-of-the-art artificial intelligence technologies following the PRISMA framework. The goal was to find the most advanced technologies used in different domains of artificial intelligence technology research. Three recognized journals were used from artificial intelligence and machine learning domain: Journal of Artificial Intelligence Research, Journal of Machine Learning Research, and Machine Learning, and articles published in 2022 were observed. Certain qualifications were laid for the technological solutions: the technology must be tested against comparable solutions, commonly approved or otherwise well justified datasets must be used while applying, and results must show improvements against comparable solutions. One of the most important parts of the technology development appeared to be how to process and exploit the data gathered from multiple sources. The data can be highly unstructured and the technological solution should be able to utilize the data with minimum manual work from humans. The results of this review indicate that creating labeled datasets is very laborious, and solutions exploiting unsupervised or semi-supervised learning technologies are more and more researched. The learning algorithms should be able to be updated efficiently, and predictions should be interpretable. Using artificial intelligence technologies in real-world applications, safety and explainable predictions are mandatory to consider before mass adoption can occur.

The key goals of Astronomical AI are to create an AI tool that reduces waiting times for the diagnosis and treatment of lung cancer and improve the workflow of the Oncology department. With this in mind, I came across a BBC Breakfast Segment which was followed up with a BBC article titled: 'Cancer care delays: How bad are they in your area?' The segment and article highlight a rise in long waits for cancer therapy in the last 4 years. The number of people waiting for cancer treatment in the UK has doubled since 2018, the number of people waiting for more than 62 days from referral to treatment (62 days being the maximum target wait time from the NHS, from urgent cancer referral to treatment being started) has risen to over 69,000. This is resulting in patients dying from long delays between diagnosis and treatment.

Over the last couple years, I've been spending time writing about creating ghosts -- perhaps an inevitability in the midst of a pandemic. While created by far-from-supernatural means, these are ghosts nonetheless; they are created from an essence of you -- from your voice, your data, your feelings, beliefs, habits, and history. Groups around the world are looking to take such information, this essence, and use it to create a digital version of you that may last once you are gone. Consider it a technological solution to the problem of death. Over the last couple years, I've been writing about creating ghosts -- perhaps an inevitability in the midst of a pandemic.

The current issues of the lending industry can be solved through technology and digitalizing the operations as much as possible. A thorough research and analyses on this matter has led Rishabh Goel to the idea of launching Credgenics, a technological solution to digitize a largely manual collections workflow. Rishabh was soon joined by Anand and Mayank, who are currently the CTO and COO of Credgenics respectively. Analytics Insight has engaged in an exclusive interview with Rishabh to discuss about his vision of creating a technology-based solution for the lending industry. After graduating from IIT Delhi, I worked first with Deutsche Bank and then with Blackrock, where I understood the nuances of the lending industry and observed the problems with the current collections practices.

Disinformation has been used in warfare and military strategy over time. But it is undeniably being intensified by the use of smart technologies and social media. This is because these communication technologies provide a relatively low-cost, low-barrier way to disseminate information basically anywhere. The million-dollar question then is: Can this technologically produced problem of scale and reach also be solved using technology? Indeed, the continuous development of new technological solutions, such as artificial intelligence (AI), may provide part of the solution.

Research project'Technology Foresight on Biometrics' from EU border security agency Frontex, which studies the impact of emerging biometric technologies on the facilitation of border crossing at the EU external borders, has completed two new steps. Frontex announced the tender for the project in September 2021 with a contract value of EUR 500,000 (US$590,000). The project, led by Steinbeis 2i together with three subcontracted partners (4CF, ERREQUADRO and WAT) has been examining how to maximize the future benefits of biometrics in border management while minimizing its risks and ensuring full compliance with the existing legal, ethical and technological constraints. The research team created a taxonomy of biometric technologies and carried out a Delphi survey to gather information on key technologies. Looking at the taxonomy of technologies early on provides foresight into areas which need to be addressed, according to the report, and establishes a common and systematic understanding of the technological field.

The COVID-19 pandemic has both created and exacerbated a series of cascading and interrelated crises whose impacts continue to reverberate. From the immediate effects on people's health to the pressures on healthcare systems and mass unemployment, millions of people are suffering. For many of us who work in the digital technology industry, our first impulse may be to devise technological solutions to what we perceive as the most urgent problems when faced by crises such as these. Although the desire to put our expertise to good use is laudable, technological solutions that fail to consider broader social, political, and economic contexts can have unintended consequences, undermining their efficacy and even harming the very communities that they are intended to help.10 To ensure our contributions achieve their intended results without causing inadvertent harm, we must think carefully about which projects we work on, how we should go about working on them, and with whom such work should be done.