Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. In this episode, we talk to Tomasz Hollanek, researcher at the Leverhulme Centre for the Future of Intelligence at the University of Cambridge. Tomasz argues that design is central to AI ethics and explores the role designers should play in shaping ethical AI systems. The conversation examines the importance of AI literacy, the responsibilities of journalists in reporting on AI technologies, and how design choices embed social and political values into AI. Together, we reflect on how critical design can challenge existing power dynamics and open up more just and inclusive approaches to human-AI interaction.

It has been roughly three years since the open-source release of Stable Diffusion ignited a Generative AI (GenAI) boom [Bengesi et al., 2023]. The proliferation of these technologies has since reshaped design practice and research. From early ideation to final implementation, these developments have significantly altered how design work is conceived, conducted, and evaluated [Hou et al., 2024]. This essay examines the critical juncture at which the design research community finds itself, seeking to understand and shape these developments while grappling with their implications for creative practice, design education, and professional identities. Popular discourse around GenAI often centers on simplified unequivocal narratives: AI as a threat to humanity, as a solution to global challenges, as a force of disruption, or as a replacement for humans [Gilardi et al., 2024]. While these narratives have sparked debate and interest, they can function as "semantic stopsigns"--conceptual framings that oversimplify complex issues, providing an illusion of resolution that hinders deeper inquiry [LessWrong Community, n.d., Lifton, 1961]. For instance, claims like "AI is unreliable" can lead to outright dismissal of its potential,

Dark patterns in user interfaces represent deceptive design practices intended to manipulate users' behavior, often leading to unintended consequences such as coerced purchases, involuntary data disclosures, or user frustration. Detecting and mitigating these dark patterns is crucial for promoting transparency, trust, and ethical design practices in digital environments. This paper proposes a novel approach for detecting dark patterns in user interfaces using logistic regression and bag-of-words representation. Our methodology involves collecting a diverse dataset of user interface text samples, preprocessing the data, extracting text features using the bag-of-words representation, training a logistic regression model, and evaluating its performance using various metrics such as accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Experimental results demonstrate the effectiveness of the proposed approach in accurately identifying instances of dark patterns, with high predictive performance and robustness to variations in dataset composition and model parameters. The insights gained from this study contribute to the growing body of knowledge on dark patterns detection and classification, offering practical implications for designers, developers, and policymakers in promoting ethical design practices and protecting user rights in digital environments.

In May 2023, the Georgia Tech Ethics, Technology, and Human Interaction Center organized the Conference on Ethical and Responsible Design in the National AI Institutes. Representatives from the National AI Research Institutes that had been established as of January 2023 were invited to attend; researchers representing 14 Institutes attended and participated. The conference focused on three questions: What are the main challenges that the National AI Institutes are facing with regard to the responsible design of AI systems? What are promising lines of inquiry to address these challenges? What are possible points of collaboration? Over the course of the conference, a revised version of the first question became a focal point: What are the challenges that the Institutes face in identifying ethical and responsible design practices and in implementing them in the AI development process? This document summarizes the challenges that representatives from the Institutes in attendance highlighted.

Should designers care about artificial intelligence (AI) or machine learning (ML)? There is no question that technology is adding texture to the current zeitgeist. Never could I have imagined seeing a blockbuster hit where Ryan Reynolds emerges as a conscious non-player character in a video game and a flop where Melissa McCarthy negotiates humanity's future with a James Corden-powered superintelligence within a year of each other. But does learning AI and ML's ins and outs really matter for the creative professions and our nebulous, invaluable way of operating? Helen Armstrong, a professor of graphic design at NC State, thinks so.

Talk of Artificial Intelligence, and it is immediately depicted as a replacement for humans. While there is no doubt that AI will transform the framework of design, the idea that this intelligent technology is here to replace humans is not strictly rational. As technology is evolving and the economy is transforming, shifts in business processes are natural. Design processes are also subject to this change. This article aims to discuss how AI will profoundly transform the design process.

Through advances in their conversational abilities, chatbots have started to request and process an increasing variety of sensitive personal information. The accurate disclosure of sensitive information is essential where it is used to provide advice and support to users in the healthcare and finance sectors. In this study, we explore users' concerns regarding factors associated with the use of sensitive data by chatbot providers. We surveyed a representative sample of 491 British citizens. Our results show that the user concerns focus on deleting personal information and concerns about their data's inappropriate use. We also identified that individuals were concerned about losing control over their data after a conversation with conversational agents. We found no effect from a user's gender or education but did find an effect from the user's age, with those over 45 being more concerned than those under 45. We also considered the factors that engender trust in a chatbot. Our respondents' primary focus was on the chatbot's technical elements, with factors such as the response quality being identified as the most critical factor. We again found no effect from the user's gender or education level; however, when we considered some social factors (e.g. avatars or perceived 'friendliness'), we found those under 45 years old rated these as more important than those over 45. The paper concludes with a discussion of these results within the context of designing inclusive, digital systems that support a wide range of users.

As an engineering director leading research projects into the application of machine learning (ML) and deep learning (DL) to computational software for electronic design automation (EDA), I believe I have a unique perspective on the future of the electronic and electronic design industries. The next leap in design productivity for semiconductor chips and the systems built around them will come from the fusion of fully integrated EDA computational software tool flows, the application of distributed and multi-core computing on a broader scale and ML/DL. The current wave of artificial intelligence (AI) and ML innovation began with improved GPU computing capacity and the smart engineers who figured out how to harness it to accelerate deep neural network training. AI/ML will play a key role in the design of next-generation platforms, enabling the proliferation of today's technology drivers including 5G, hyperscale computing and others. In my role, the fun comes from the numerous non-deterministic polynomial (NP)-hard and NP-complete problems that exist at every stage of the design and verification process.

As technology becomes more complex and opaque, how will we as designers understand its potential, do hands-on work, translate it into forms people can understand and use, and lead meaningful conversations with manufacturers and policymakers about its downstream implications? We are entering a new technology landscape shaped by artificial intelligence, advanced robotics and synthetic biology. Historically, design has transformed and generated new disciplines during times of fast technological change. Through conversations led by Tellart, this project explores and documents transformations in design between the Dotcom Crash and the rise of machine intelligence. Through reflections on key projects from this period and interviews with a community of today's top design practitioners, Design Nonfiction explores the future of design practice.

The evaluation question is inseparable from questions about what sort of thing AI is--and both are inseparable from questions about how best to do it. Most intellectual disciplines have standard, unquestioned criteria for what counts as progress. Artificial intelligence is an exception. This has always caused trouble. The diverse evaluation criteria are incommensurable. They suggest divergent directions for research. They produce sharp disagreements about what methods to apply, which results are important, and how well the field is progressing. Can't AI make up its mind about what it is trying to do? Can't it just decide to be something respectable--science or engineering--and use a coherent set of evaluation criteria drawn from one of those disciplines? That doesn't seem to be possible. AI is unavoidably a wolpertinger, stitched together from bits of other disciplines. It's rarely possible to evaluate specific AI projects according to the criteria of a single one of them. This post offers a framework for thinking about what makes the AI wolpertinger fly. The framework is, so to speak, parameterized: it accommodates differing perspectives on the relative value of criteria from the six disciplines, and their role in AI research. How they are best combined is a judgement call, differing according to the observer and the project observed. Nevertheless, one can make cogent arguments in favor of weighting particular criteria more or less heavily.1 Choices about how to evaluate AI lead to choices about what problems to address, what approaches to take, and what methods to apply. I will advocate improving AI practice through greater use of scientific experimentation; pursuit particularly of philosophically interesting questions; better understanding of design practice; and greater care in creating spectacular demos. Follow-on posts will explain these points in more detail. This framework is meant mainly for AI participants.