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The speaker reflects on their evolving understanding of sustainability through the lens of artificial intelligence (AI) as a modern wicked problem. Initially associating sustainability with environmental issues, they now see its broader impact on society, economy, and future generations. Using Gibbs reflective cycle, they analyze how AI affects sustainability, highlighting the need for long-term thinking and personal responsibility. They recognize the complexities of AI, its impacts on sustainability goals, and the importance of interdisciplinary approaches for creating more responsible and sustainable AI systems. The speaker emphasizes the need for individuals to take responsibility in shaping the development of AI with a focus on ethics and sustainability. Their reflection has influenced their career thinking, leading them to prioritize sustainability in AI-related work. They stress the ongoing nature of sustainability efforts and the importance of reflection, responsibility, and act Hello everyone and welcome to my podcast in this reflection I'll be exploring the question what does sustainability mean to me and I'll be doing this through the lens of artificial intelligence which I think and really believe is a modern wicked problem. That said let's start when I first thought about sustainability I mainly associated it with environmental issues like climate change, recycling or renewable energy. I didn't really connect sustainability with digital technologies however through this module and my growing exposure to artificial intelligence in everyday life and my university education my understanding has changed quite significantly. I've come to realize that sustainability is not only about protecting the environment but also about how technologies like AI affect society, the economy and future generations. To structure this reflection I've used Gibbs reflective cycle which we learned in this module and it helped me think critically about how my views have developed and what this means for my future. That said I chose this reflective cycle because it provides a clear and logical way to reflect on learning experiences. It moves through stages such as describing what happens, exploring feelings, evaluating positives and negatives, analyzing the meaning of something and then thinking about future actions. This module works particularly well for sustainability because it encourages long-term thinking and personal responsibility. Instead of just describing what sustainability is, Gibbs module allows me to reflect on how my understanding has changed and how that might influence my behavior going forward especially in relation to artificial intelligence. Initially my experience with artificial intelligence was mostly positive. I saw AI as a useful and exciting technology that could make tasks quicker, decisions smarter and systems more efficient. At that stage my values were closely linked to innovation and technical logical progress. However sustainability wasn't something I really considered in relation to AI. I assumed that because AI is digital it must be relatively low impact. Over time I learned that this isn't necessarily true. AI systems rely heavily on large data centers, constant data processing and significance in energy use. This made me rethink my assumptions. I started to understand that sustainability is about more than efficiency. It's about considering the hidden cost of technology and questioning who benefits, who is disadvantaged and what the long-term impacts might be. That said, my understanding of sustainability developed gradually through a combination of personal reflection and academic learning such as this one, engaging with sustainability theories and their sustainable development goals helped me see how interconnected global challenges really are. I also began reflecting on my own academic discipline and noticed that technical performance is often prioritized over ethical or environment considerations. This shift in perspective helped me realize that sustainability should be built into technology from the start rather than added on as an afterthought. I have chosen AI as my wicked problem because it is complex and there is no simple solution. AI can support sustainability efforts such as improving energy efficiency or analyzing climate data. At the same time it creates more and new problems. This wicked problem links to several UN sustainable development goals including SDG 9 which focuses on innovation, SDG 10 on reducing inequalities, SDG 12 on responsible consumption and SDG 13 on climate action. I found all these factors on the United Nations of 2015. For example, training AI models requires large amounts of energy and everyone knows that contributing to carbon emissions. At the same time biased algorithms can reinforce social inequalities. These conflicting outcomes show why AI is considered a wicked problem. So by solving one issue it can create another issue. One limitation of my discipline is that it often focuses on technical solutions while overlooking wider social and environmental impacts. While technical skills are indeed essential, they are not enough on their own to address sustainability challenges. This is where interdisciplinary approaches, particularly systems thinking, become more important. Systems thinking encourages us to look at the bigger picture and understand how AI interacts with the society, the economy and the environment. By drawing on other disciplines such as ethics, sociology and environment studies, for example, we can develop more responsible and sustainable AI systems. That said, artificial intelligence impacts sustainability in many ways. Socially, it can reinforce inequality if certain groups are excluded or misrepresented. Economically, AI can increase productivity but also raise concerns about job displacements. Environmentally, AI contributes to increased energy consumption and electronic waste. Politically, there are ongoing challenges around regulation and accountability. From an educational perspective, access to AI knowledge and digital skills is uneven, which affects who gets to shape future technologies. All of these issues are connected, which highlights the need for coordinated and sustainable approaches to AI development. Both formal education, such as the lectures and academic readings, and the informal learning that I've done, like news and public discussions, have shaped my understanding of sustainability and artificial intelligence. This reflection has also influenced my career thinking. It's made me more aware of the responsibility that comes with working in AI-related areas. Rather than focusing only on innovation, I now see sustainability as an essential part of professional and ethical practice. That said, looking to the future, sustainability will play a crucial role in how artificial intelligence develops. As AI becomes more embedded in everyday life and in all the present tasks, it's important that individuals take responsibility for how these systems are designed and used. Personally, I see my role as staying informed, questioning assumptions, and promoting ethical and sustainability practice wherever possible. Sustainability is not a one-time goal, but an ongoing process that requires reflection, responsibility, and action. To conclude my podcast, I have explored how my understanding of sustainability has evolved through reflecting on AI as a wicked problem, and what started as a focus on environmental issues has developed into a broader understanding of sustainability that includes ethical, social, and technological responsibilities. Thank you so much for listening, and have a nice day.