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Blog: (Design + AI) Thinking


Image from Harvard Business Review

Artificial Intelligence (AI) is the buzzword, but how does this affect us as designers? To understand and explore this further, I took “Advanced design for Artificial Intelligence” taught by Prof. Jennifer Sukis in the spring 2019 semester, and I have to say, the experience, learning, and the skillset gained would prove essential in my journey as a designer.

After being grouped with individuals from diverse backgrounds, our team decided to improve the experience of Urban Farming. We started our research to understand the domain, stakeholders, and pain points ( if any ). After conducting in-depth research, followed by intensive discussions on what are problem statement, target users and business model be, we were all able to agree on a refined problem statement that would define our approach through the rest of the process. After generating insights on the needs, motivations, and goals of urban farmers, we felt we were in a better position to design something that would better align with and serve their needs.

Insight 1: There are many uncertainties in the urban farming process and even the experts just learn as they go.

Insight 2: People involved in farming enjoy planning phase. It’s hard for volunteers to understand it.

Insight 3: People come for the hands on, tactile experience of farming and to be a part of a community. They do not want automation to take that away from them.

Insight 4: New volunteers come and go. There is not much retention of volunteers over long periods.

Insight 5: Farmers want to tell the story about their produce to the consumers.

An intensive, but standard design thinking approach until this point. But here is where the approach changes.

After a few lectures on AI capabilities, and how to use them, we had in our hands (and had the mandate to use) a tool with “unlimited” potential and capabilities that gave us the freedom to go more broad and creative on our approach to solving the problems uncovered in the research phase. We did a follow up research to understand how this AI technology is currently being used in the field of farming, and what can we learn further from them.

Agricultural Robots — Companies are developing and programming autonomous robots to handle essential agricultural tasks such as harvesting crops at a higher volume and faster pace than human laborers.

Predictive Analytics — Machine learning models are being developed to track and predict various environmental impacts on crop yield such as weather changes.

Crop and Soil Monitoring — Companies are leveraging computer vision and deep-learning algorithms to process data captured by drones and/or software-based technology to monitor crop and soil health.

Not only did this allow us to understand the application of AI technology better, it also allowed us to understand how and why our approach to the problem was different from the products that existed in the market. AI powered agricultural products in the current market are focused on increased yield, automation of tasks, and increased revenues, while the users we were dealing with ( urban farming volunteers) had motivations on the lines of community involvement, hands on tactile experience, learning, and sustainable lifestyle practices, and an overall engaging and fun experience.

Having learned and adopted what could be applicable from current farming products, we brainstormed on right intervention through the right kind of technology for our target users. We focused more on AI for engagement, community experience, learning, and most importantly, AI for storytelling, in the urban farming domain.

Throughout this ideation stage, the structure of approach, and the Mural board we used to consolidate ideas (as a part of the course) kept our ideas in check in terms of feasibility. Although we did not have to demonstrate the AI algortihms we are going to use, or build them (neither did we possess the skills to), we had to provide a high-level understanding of how we were expecting our solution to work, the datasets we were going to use (Data being the backbone for AI), how were were addressing the issues of accessibility and bias ( which are surprisingly easy to creep in in AI systems due to biased data, and hard to justify/correct due to black box nature of the system), and what technologies were we using. This not only allowed us to look at design through an AI lens, but also the practicality and issues in terms of real work business and ethics lens.

The journey through this project has been an application of Design Thinking approach, but with AI tools that expanded the capabilities and creativity we as designers were working with. The major learning has not been what AI is, and what it can do (although it has been a big learning), but that as designers, on this journey of designing for newer technologies, we should not lose sight of adding the human/emotional factor to technologies and design solutions. We need to have an open mind, stay updated, and most importantly, know the reason behind each action, so that the design decisions we make are the best solutions for the given problem, and not end up accidentally creating negative effects we did not envision. We can only be as creative as our knowledge and mind allow us to, and constant learning is a way to keep those two sharp and up to date.

One aspect that I wish we could have explored is a class discussion on various Utopian/Dystopian futuristic literature that exists. Various writers, thinkers, designers and innovators have written alot about their visions on how the society would look like, and a discussion on that might have allowed us to better look at our ideas through a lens of futuristic societies. (although might have been highly unfeasible, but extremely interesting)

Source: Artificial Intelligence on Medium

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