Artificial Intelligence, despite its benefits, presents designers with unprecedented challenges that demand ouraction.The responsibility of us designers to protect user interest and value has never been so important. Not only were living in a trendy era with the rise of ChatGPT and many other AI tools, but were also experiencing how these systems shifts control away from the user. Whether its automating tasks, discovering hidden patterns, enhancing user experiences, generating new content, or optimising decisions, AIs existence is driven by its ability to learn from data and improve over time without explicit programming. Currently, the speed and the way these super powerful technologies are being rolled out, we cannot follow as society, which causes great impact on the economy, on mental health, on culture and on society aswhole.We, designers, have to own up to our role in this emerging predictive world because we have our invisible hand meddling with the controls Helen ArmstrongThis quote is from the wonderful book Big Data, Big Design: Why Designers Should Care About Artificial Intelligence authored by Helen Armstrong, which explores the intersection of design and artificial intelligence, emphasising the role of designers in shaping AI-driven experiences. In a predictive digital world, we play a crucial role in ensuring that AI solutions are ethical, transparent, and aligned with human values. We must consider the potential biases in data, the impact on user privacy, and the societal implications of our creations. By prioritising responsibility and accountability, designers can help build a future where AI technologies enhance human capabilities and improve quality oflife.The book explores these issues in depth, featuring contributions from various designers, educators, and researchers who work at the intersection of artificial intelligence and design. It serves as my primary reference for the questions and discussions raised in this article, but I have drawn from numerous other sources to provide a comprehensive perspective, all of which are listed at the end of thisarticle.https://helenarmstrong.info/big-data-big-design/The central theme of the bookand of this articleis to emphasize the responsibility of designers in the ethical development of AI products and services. Our discipline is fundamentally human-centric, focusing on understanding and addressing human needs and desires within the broader societal context. The psychology, research, and strategy we employ in designing our products have a profound impact on the physical, mental, and emotional well-being of people worldwide.AI is human-centric?Human-centric design, like inclusive and participatory approaches, ensures fairness in data models, mitigates bias, and promotes inclusivity. It is widely recognised that human-centric design has been the guiding principle of all design frameworks and processes over the past decade, and this should be no different when working with AI. These perspectives have been valuable because they inform decision-makers about potential human impacts and help anticipate unintended consequences. Initiatives, such as Stanford Universitys Human-Centered AI Institute and MITs substantial investment in AI education, exemplify the global effort to prioritise a human-centric approach. According to this article by Sarah Tan, by exploring AIs human impact through various Human-Centered Design (HCD) approaches, design aligns values between humans and machines, integrating ethics at the projects core.Putting humans at the center of AI systemsHuman-Centered Artificial Intelligence: Three Fresh Ideas. Shneiderman B.(2020)Advocating for user needs, design research has the power to examine AI implications in real-world contextsaddressing socio-economic dynamics too. However, this isnt easy. We, as designers working in the industry, often feel pressured to quickly adopt the latest trends without time for reflection. We are frequently required to use behavioural research to influence user choices, sometimes at the expense of their well-being. Additionally, we must operate in fast-paced environments, adopting to the move fast and break things mentality popularised by early Facebook culture. This approach can make it harder for us to design thoughtfully and ethicallyBut how to stand up for people in the face of suchforces?AI capabilities all designers shouldknowHeres what I aim to achieve with this article: to inspire designers to get more involved in creating AI systems, so we can make the process more ethical and human-centered. This means using design thinking as our guide to reduce the negative impacts of AI on society by developing human-centered AI systems and guidelines.How we can achieve this is still being figured out, and we are all part of this process! To help, I want to highlight the main capabilities discussed in Armstrongs book, where we designers should be actively involved. We need to advocate for design decisions that meet user needs and respect the socio-economic context in an ethical way. It wont be easy, but we can start paving the way with these elements.1. Designing to power up predictionsPrediction is a central capability of machine learning (a subset of AI that involves training algorithms on data to make predictions or decisions without being explicitly programmed). In the context of predictive modelling, the goal of machine learning is to forecast future events or outcomes and anticipate behaviours based on patterns found in historical data.For instance, we can think of Gmails feature that autocompletes sentences based on your past behaviour. Over time, the system learns an individuals style so well that it can finish sentences and even suggest appropriate tones or sentiments.Screen capture from my personalGmailOr we can think of the numerous applications across various industries, such as stock price forecasting, inventory and supply chain optimisation, healthcare predictions (such as disease outbreak forecasting), targeted marketing campaigns, and other personalised user experiences.While these AI-driven predictions arent always 100% accurate, they offer significant benefits to businesses. The advantage is that by anticipating future demands, trends, and needs, companies can better prepare for opportunities and gain a competitive edge. For that end, designers are required to plan for a shifting experiential landscape when working with intelligent technology in their projects.On the other hand, designers cannot blindly apply AI technology without the risk of subjecting humans to discrimination, surveillance, and/or manipulation, not just individually but at scale. Predictive models can sometimes reinforce existing biases or lead to unintended consequences.Face Cages are a dramatization of the abstract violence of the biometric diagram. More on: https://zachblas.info/works/face-cages/Designers have a responsibility to foresee these potential issues and take steps to mitigate them, ensuring that their designs are ethical and inclusive. As Kate Crawford, AI Now Institute founder, putsis:Understanding the politics within AI systems matters more than ever, as they are quickly moving into the architecture of social institutions: deciding whom to interview for a job, which students are playing attention in class, which suspects to arrest, and muchelse.2. Designing to anticipate future scenariosIn her book, Helen Armstrong explores the concept of anticipation, showing how designers can use AI to predict and meet user needs proactively. This involves leveraging data-driven insights to stay one step ahead of users, reducing friction and enhancing the overall user experience.One advantage of anticipatory design is that it reduces decision fatigue by minimizing the cognitive load on users through predictive systems. These systems offer relevant choices or take actions on behalf of users. For example, companies like Netflix and Amazon use predictive analytics to recommend products or content. However, there are ethical implications, as such systems might infringe on user autonomy or privacy. As Armstrong rightfully states on page210:The promise of anticipatory design lies in its potential to craft experiences that feel intuitive and personalised. Yet, with this power comes the duty to wield it responsibly, ensuring that the human element remains at the heart of every design decision.Fortunately, there is a forward-thinking approach that allows us to create products and services that are resilient to change and can evolve with user expectations. Designers can use AI to simulate and model potential future scenarios, anticipating various user needs, environmental factors, and societal changes. This ensures that our designs remain relevant and adaptable.Flock Defines Flight Risk to Make Insurance for Drone Operations Simple: the insurance company worked with studio IF to create an interface that help clients understand how automated decisions were made around insurance rates.3. Designing to create personal experiencesPersonalisation in user experience design involves tailoring interfaces, content, and interactions to meet the specific needs, preferences, and contexts of individual users. When we talk about personalisation in the context of AI, it often means leveraging predictive technologies and data analysis to deliver experiences that are more relevant and valuable to users at any given moment. Personalisation ensures that the information or actions presented to the user are useful, usable, and desirable, inspired by the popular quote from design researcher LizSanders.AI can enhance personalisation by analysing vast amounts of user data to identify patterns and preferences, using predictive models to anticipate what a user might need or want nextthis can involve considering the users current context, such as location or time of day, to provide more relevant suggestions. However, personalisation also raises ethical concerns, including privacy, bias, and transparency. This is why we, designers, play a crucial role in ensuring that personalisation is implemented effectively and ethically. We must understand user needs and preferences through research and testing, and continuously gather feedback to refine personalisation features.https://medium.com/media/6cf6ef6eec5b59e000dc098865a8e440/hrefWe can say that personalisation is a powerful tool in a designers toolkit, but as Armstrong emphasizes, the success of personalised experiences hinges on the thoughtful and ethical application of AI insights. It is crucial to always prioritise the users best interests, ensuring that personalisation efforts are not only effective but also respectful and beneficial to the individual.4. Using AI as a DesignMaterialDesigning for AI is challenging because the technology is invisible and abstract. As designers, if we cant sketch a solution, it feels like losing a crucial ability. As interaction design professor Philip Van Allen puts it, its like our arm is cut off. To address this, he created a no-code programming environment for his students, providing easy access to machine learning. The tool is called Delft AI Toolkit and it simulates an AI system in 3D, allowing designers to observe and manipulate the systems behaviour and data sources in a virtual space before investing time and expertise into model training and building a physicalrobot.https://medium.com/media/7554deb21a70eb4fc12cd2a0689fdd80/hrefAnother approach to working with AI as a design material comes from John Zimmerman, a professor at Carnegie Mellon University. He developed a method that focuses on the AI system capabilities rather than the underlying technology. Zimmerman observes that many of his design students are uncertain of whats possible when working with AI technologies, so he uses a match-making system which helps them grasp the AI systems potential. For example, he presents a two-class text classifier and asks, What could you do with this, and for whom?. This method helps designers identify existing capabilities and apply them in innovative ways. As with any design tool, the more we experiment with it, the more comfortable we become, and the more examples and abstractions of AI capabilities we will generate.Janelle Shanes experiments with DALL-E 3 playfully tease out all the ways algorithms can get things wrong. Here, she jumps into the algorithmic training process to generate candy heart messages. More on: https://www.aiweirdness.com/dall-e3-generates-candy-hearts/A third approach, which I find particularly effective, is using the Object-Oriented UX (OOUX) methodology. OOUX focuses on objects before actions, aligning with how users perceive the real world. It borrows concepts from object-oriented programming but applies them to user experience design. This approach works well with AI workflows by organising data and interactions around objects, making complex AI systems more intuitive for non-developers by matching their mental models. Sophia Prater is a leading expert in the OOUX field, and I highly recommend her materials and podcasts on thesubject.On a personal note, I recently completed my first project using this approach for a native AI product. The methodology was incredibly helpful. It allowed me to visualise the systems functionality and limitations more clearly, which also aided the engineers. Additionally, it made my design process more tangible through object mapping and user flows, enhancing the quality and accuracy of my proposed solutions.A beautifully chaotic collaboration between me and the engineering team using the OOUXapproachML is the new UX. I envision UX practitioners leveraging machine learning as a design material creatively and thoughtfully, guiding users and technologists toward a deliberative ML-mediated futureQian Yang, Cornell University5. Designing to reduce climate impact ofAIAlthough this isnt mentioned in the book and isnt a AI capability, it is certainly one of the key skills designers should possess, especially in the 21st century with the rise of AI technologies.Theres an article called How to Design Climate-Forward AI Companies by Santhi Analytis that emphasises the important role designers play in mitigating the environmental impact of AI technologies. It frames the challenge of AIs growing carbon footprintfueled by energy-intensive processes like model training and data center operationsas one that requires urgent attention from both engineers and designers. Designers can contribute by creating energy-efficient UI/UX elements, minimizing unnecessary data and media loads, and applying green software principles. Additionally, designers can push for socially equitable uses of AI by developing inclusive, climate-conscious user experiences.The Intercept Brasil (in Portuguese) also published last year an article stating that the major tech companies like Microsoft, Google, and Amazon are now turning to nuclear energy as a solution in response to the growing energy demands of AI and data centers. Microsoft plans to reopen the Three Mile Island plant, Google has partnered with Kairos Power for small reactors, and Amazon has invested $500 million in nuclear technology. Many of these data centers are being built in countries from the Global South, where labor and infrastructure costs are lower, raising concerns about resource exploitation. While nuclear energy is carbon-free and efficient, it is costly and carries risks, and critics argue that big tech is addressing a crisis of its own making, prioritizing rapid technological expansion over sustainability.The industrys move fast mentality raises concerns about whether nuclear energy can truly keep pace with AIs accelerating energy consumption, or if it will simply become another chapter in big techs history of unchecked growth. This Vox documentary tries to answer this question:https://medium.com/media/8d8c5a33bba53566072d960d2be0f0f4/hrefIn this other article from MIT, the author Andrew Winston also highlights the growing environmental impact of AI and provides recommendations to reduce its carbon footprint, such as using existing models instead of creating new ones. It emphasises the role of designers in this issue, urging them to develop AI expertise to simplify complex concepts, communicate the nutriscore of AI in their products (its usage and environmental cost), and adhere to an ethical code of conduct similar to the EUs AI Act, ensuring AI products are safe, transparent, traceable, non-discriminatory, and environmentally responsible.As designers, we can influence user behaviour and company policies by incorporating transparent data models that reflect the true costs of AI, thus ensuring the technology not only solves human problems but also helps tackle environmental challenges.Conclusion: Lets takeactionIts easy to feel overwhelmed by the urgent need for AI ethics. AI is a versatile and dynamic field, capable of performing a wide range of tasks that can help solve real-world problems. However, algorithmic systems often reflect and amplify existing societal biases. Until interfaces clearly communicate the logic behind these algorithmic decisions, users wont be able to hold these systems accountable. This is why designers and the communities they serve need to understand digital rights. We must hold ourselves and the industry accountable for the choices we make through ourdesigns.The key question is: Will we allow machine learning to prey on those already victimised by society, or will we use this technology as a mechanism for equity andjustice?Here are some actions we can start taking as designers in this industry: Design for anticipation to prepare for multiple possible futures, making our designs more flexible and future-proof Think critically about the long-term impacts of our designs and the data-driven decisions that underpinthem Articulate digital rights and guide users with transparency toward options through design interactions Break down complex privacy agreements into quick, comprehensible, just-in-time interactions Prototype future-facing concepts, like personal privacyagents Support collective digital rights organisations and community-driven dataset initiatives Ask early questions about ethics and biases to ensure technology adapts to human needs, not the other wayaroundHow do you approach building AI products? What examples do you know of ethical and inclusive design solutions?ReferencesBig Data, Big Design: Why Designers Should Care About Artificial IntelligenceHelen Armstrong (Book)Design and the Question of HistoryTony Fry, Clive Dilnot, Susan Stewart(Book)Human-Centered Design for AIWebinar by Niwal Sheikh (Product Design Lead, Netflix) byIxDFHow to Design Climate-Forward AI CompaniesArticle by SanthiAnalytisThe problem with AI development today: Designers need to step upArticle by SarahTanWill AI Help or Hurt Sustainability? YesArticle by AndrewWinstonWhat is predictive AI?Article by CloudflareReviewing the Terms & Conditions of popular generative AI toolsArticle by DavidSerraultThe Problem with AI Development: Designers need to step upArticle by SarahTanDesign For AI (Artificial Intelligence)Article by Sudarshan SahuDesign Against AIWebsite by JohnMaedaThe Intercept: Big techs apelam para energia nuclearArticle from Portal InvestNE mentioning the original piece from The Intercept BrasilOOUXWebsite by SophiaPraterAI Now InstituteWebsiteWhy AI (desperately) needs designers was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.