This is why you may find definitions of Human-centered design that vary a little, although the overall concept is always the same. In this blog, I’ll use the definition of Human-centered design process used by IDEO.org, one of the leading organisations in this area. Human-centered design is a process that consists of three phases: Inspiration, Ideation and Implementation.
Phase 1: Inspiration
This first phase is all about understanding how the user works and what the user needs. You shouldn’t worry about data availability or possible AI techniques you could use. Your only objective should be to drop any possible preconceived notions and expected outcome you may have and truly understand and internalize the needs, wants and challenges of the user. This would be done through interviews, surveys and direct or indirect observation. For example, say we want to create a tool to help automate part of an ATCO‘s job. You would not jump straight into trying to automate the complex process to safely manage the flow of air traffic. Instead, you would try to understand the different processes and actions a ATCO performs throughout the day and identify those areas in which an automated system could help him. The idea here is that you have a higher likelihood of success in providing a system that adapts and aligns itself with the decision-making process of an ATCO than with providing a system that totally upends this process and makes ATCOs the ones who have to adapt to it.
Phase 2: Ideation
This is the phase where we would start working with data. This starts with the Exploratory Data Analysis and entails visualizing, better understanding the domain and brainstorming any potential solutions to the problems identified in phase 1. You should not exclude any idea, as impossible or silly as it may seem (you never know!). One key aspect of this phase is prototyping. It is important that you create several low-fidelity prototypes (sketches, powerpoints, mockups) to test your ideas and gather real user feedback and insights. The ideation phase is characterised by these iterative cycles of prototypes and feedback gathering. This iterative process would be maintained even when transitioning to higher-fidelity prototypes to help ensure that the results always align with user needs and also helps prevent wasted time, effort and money. Going back to our ATCO example, in this phase, we would come up with different areas to automate in the ATCO’s decision-making process and create examples of future scenarios where these ideas would be implemented. We would then present these different scenarios to the ATCOs and collect feedback on how useful they see each idea — if it is something they need, if they would actually use it or if they may foresee possible limitations.
Phase 3: Implementation
Phase 3 is the last and usually the longest phase. Once you have a sound prototype that you and the users are happy with, it is time to build a pilot. You’ll go from a high-fidelity prototype to a pilot solution that will be tested with a limited release. Working with the stakeholders, we would select a relevant scenario as well as group of users to test the prototype solution. Similar to the previous phase, this one will not have a linear development and there would also be different iterations. Feedback and data would be gathered from these tests in order to evaluate the solution’s effectiveness, efficiency and usefulness. If needed, changes would be made to the pilot solutions and tested again. In the end, we would arrive at a prototype ready for production. Congratulations! Also during this phase, or earlier if possible, you would create the business model for the solution and prepare for the roll-out into the real world. With all this, and if done correctly, you will have covered the three areas mentioned by Peter Bull. To finish with our ATCO example, in this phase, we would test our prototypes within controlled groups either in simulations or in shadow-mode at airport towers or ACCs . Once we have evaluated its technical feasibility, we would create our business plan, searching for potential partners and interested clients. Finally, we would take our solution to ATCOs around the world to improve safety and efficiency in ATM.
While it’s true that no process is perfect, the principles of a Human-centered design process can bring lots of benefits to the world of data science and AI. Each project is different — no size fits all — so you should take these principles and adapt them to the needs and limitations of your project. All told, human-centered design will surely help increase your chances of success as most solutions being developed today will ultimately involve a human user in some way or another. Finally, human-centered design has the benefit of involving users from the beginning and all along the way. This helps to create a greater bond between the user and the solution, seeing it as theirs, as well as creating greater levels of trust and confidence. This will be a key stepping stone in developing a trustworthy AI. I hope you have enjoyed the blog and don’t forget to visit https://datascience.aero/ for more interesting posts!