Designing machine learning systems require new approaches for user experience designers as both practices do not always work in concurrence. The traditional design techniques can’t always be applied to machine learning systems as the latter uses indirect interactions based on language interpretation. They are also more evolving and dynamic as the artificial intelligence is primarily based on learning to adapt to users with time. As a consequence, it is important to understand the relevance of interaction design patterns with regards to user perception.
Often in organizations, machine learning and user experience teams work separately, even while building the same product. As both disciplines use different methods and concepts, bridging a gap between them usually involves conscious efforts.
Here are a few methods for effective collaboration between product design and machine learning.
Forming common goals
Collaboration is the key to forming goals for both disciplines which align with the product vision, user experience, and business strategy. User experience designers and machine learning experts should co-create product roadmaps which include user interface designs as well as data engineering functions. For example, traditional web-based systems include limited user input methods, whereas machine learning apps include visual, auditory, physical, environmental as well as abstract inputs at times. Including design solution from the beginning phase of development of a machine, learning app can ensure seamless user experience.
At times, forming shared goals also help teams to prioritize product decisions, for example, all the team members should be able to understand that improving copy of the notifications can increase the user engagements more than scaling machine learning model to generate personalized recommendations.
Prioritizing user-centered approach
Building a customer-facing approach requires prioritizing user experience and business problems which the client wishes to solve. Stating clear use cases help in strategizing user flows. This also helps teams in identifying vital points where machine learning can enhance product performance for better user experience and vice versa. Concrete and realistic inputs from data scientists, data engineers, and business experts help designers in developing iterations which are user-focused as well as technically scalable.
Clear identification of use cases helps in determining accurate key performance indicators which align with machine learning metrics. For example, an AI-based anomaly detection app can send notifications in case of anomalous behaviors. However, it is important to measure the user response to the sent notification to know the effectivity of the feature.
Merging quantitative and qualitative feedback
Data is not the only factor in the designing system that can achieve business goals. Data-abundant systems that generate big data such as e-commerce, telecom, CPG companies may rely heavily on data-based insights for business decisions. However, there are other domains where the amount of data is limited, and data alone cannot be relied on to make important decisions. This indicates the importance of qualitative data such as user feedback, user interviews, and user testing can give important insights which can help in forming the right product strategy. Such data offers deep insights into how users think, which feature works, and which doesn’t.
Machine learning models are only as good as the training data fed to them. While building new products or features, it is common to encounter unexpected behaviors and errors. Determining whether the data captured precisely reflect the user behavior or pain points that need to be addressed can result in saving a lot of time and efforts.
Furthermore, user interviews and testing bring mere data alive in a relatable way. They reflect the human stories and actual relations between the data and user behavior. It also helps in identifying noise in your data which would otherwise have been impossible to detect.
Going forward, organizations are going to increasingly rely on machine learning to gain data-insights and form product strategies. Inclusion of user experience with machine learning model building is the ideal way to win over users as well as in the effective optimization of organizational resources.