The eighth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) will take place in Madrid, Spain from 20-22 October 2025. The programme will feature keynote talks, two panels, discussions, paper presentations, and poster sessions. There are two keynote talks, scheduled on Tuesday 21 and Wednesday 22. The two panels will take place on Monday 20 and Tuesday 21. Tuesday Panel: Pedagogy Panel: How (and to Whom) Do We Teach AI Ethics?

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This is a bonus episode looking back over answers to our question: What excites you about AI? This episode features clips from: Sarah Brin, Mark Coeckelbergh, Roger Spitz, Rachel Coldicutt, Pinar Guvenc, Guy Gadney, Nadia Piet, and Lisa Talia Moretti. This podcast was created and is run by Ben Byford and collaborators. The podcast, and other content was first created to extend Ben's growing interest in both the AI domain and in the associated ethics.

Convolutional neural networks learns spatial features and are heavily interlinked within kernels. The SE module have broken the traditional route of neural networks passing the entire result to next layer. Instead SE only passes important features to be learned with its squeeze and excitation (SE) module. We propose variations of the SE module which improvises the process of squeeze and excitation and enhances the performance. The proposed squeezing or exciting the layer makes it possible for having a smooth transition of layer weights. These proposed variations also retain the characteristics of SE module. The experimented results are carried out on residual networks and the results are tabulated.

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The Machine Learning Center at Georgia Tech (ML@GT) is home to many talented students from across campus, representing all six of Georgia Tech's colleges and the Georgia Tech Research Institute (GTRI). These students have diverse backgrounds and a wide variety of interests both inside and outside of the classroom. Today, we'd like you to meet Lara Martin, a fifth-year Ph.D. student who is interested in teaching artificial intelligence agents to tell interesting and coherent stories. Tell us about your research interests. Where might people be impacted them in everyday life?

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. In this bonus compilation episode we look back at our interviewees answers to the question: what excites you about our AI mediated future? This podcast was created, and is run by, Ben Byford and collaborators. Over the last few years the podcast has grown into a place of discussion and dissemination of important ideas, not only in AI but in tech ethics generally. The goal is to promote debate concerning technology and society, and to foster the production of technology (and in particular: decision making algorithms) that promote human ideals.

Benjamin Lee, a second-year Ph.D. student in the Allen School's Artificial Intelligence group working with professor Daniel Weld, has been named a 2020 Innovator in Residence by the Library of Congress. Now in its second year, the Innovator in Residence program aims to enlist artists, researchers, journalists, and others in developing new and creative ways of using the library's digital collections. During his residency, Lee will apply deep learning to enable the automatic extraction and tagging of photographs and illustrations contained in the more than 15 million newspaper scans comprising the library's Chronicling America collection. His goal is to produce interactive visualizations, searchable by topic, that will make the content more accessible to users and support cultural heritage research. "A primary motivation behind my project is to excite the American public by demonstrating the possibilities of applying machine learning to library collections," Lee explained in an interview posted on the library's blog.

Today, doing more with less is a key principle that drives business strategy across many resource-intensive industries. Businesses are looking to get a higher return out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify complex decisions around how scarce resources should be allocated, how to schedule tasks, and how to deal with constraints. As Alex Fleischer points out in his blog, a recent Enterprise Strategy Group (ESG) technical validation report cites the need to improve operational efficiency as the overarching theme driving AI and ML interest. To learn more, I sat down with Virginie Grandhaye, offering manager for IBM Decision Optimization.

Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. To make it clearer: you don't really know what your model actually learned and if you have a flaw in your training / data approach it might work well according to your metrics while having learnt the wrong thing. As a self-respecting developer you want to do better than that, so today I will show you a method you can use to get some better introspection into your model by using visualization techniques. So what is a visualization techniqe when we talk about deep neural networks?

A chatbot is a program, powered by rules and AI, which simulates a real interaction with users via a chat interface. In other words, a chatbot is a service that can have a conversation with you just like a real person. Chatbots are based on Machine Learning to gather conversational cadences that allow them to copy human conversations and react to written or spoken requests to deliver a service. They comprehend language, not just commands, since they use AI. Therefore, the more conversation chatbots have with users, the more intelligent they become.