Research

PAST RESEARCH AT HUAWEI

Designing trustworthy AI systems

With the advancement in AI technology, users often make decisions based on predictions suggested by the AI systems. However, the underlying models that produces AI system predictions to users are often opaque (also known as black-box model) and can contain flaws (e.g., bias in data set and prediction), thus leading to possible undesirable decisions made by users (e.g., unfair decisions). Thus, it is important to ensure AI technology and systems are trustworthy in the whole AI lifecycle, from the early design stage, development, deployment to final use. At Huawei, I led the UX research team to develop a roadmap that integrates Huawei AI trustworthy principles into product design and lifecycle, to create a framework that includes factors contributing users’ trust in AI products, and to provide engineers and product teams with tailor-made guidelines of how to design trustworthy AI cloud products.

UX evaluation that monitors UX and provides actionable insights to product teams for UX improvement

UX evaluation with software products is an essential aspect of supporting a product lifecycle and the success of the product. Generally, UX evaluation has two purposes: (i) evaluate if the product meets users’ needs and goals; (ii) provide product teams and designers with insights on which aspects of the product need improvement for better UX. At Huawei, I led and developed a two-tier framework that evaluates UX across 200+ Huawei Cloud products. In this framework, I defined metrics that can identify which part of UX suffers and provide actionable insights to the corresponding teams to improve product design. This work was accepted as a paper in the international conference on Human-Computer Interaction 2023.

In order to measure UX in a holistic way, I utilized both quantitative methods (e.g., data-driven machine learning approach, survey, users’ behavioral measures) and enterprise design thinking, qualitative (e.g., enterprise design thinking, users interviews, usability studies, heuristic evaluation) to identify unmet users’ needs, goals and painpoints. In addition, I also provided product teams with guidelines and instructions of how to interpret UX metrics and information gathered by different UX research methods.