Mahei Li
Thumbnail Image
Title
Dr.
Last Name
Li
First name
Mahei
Email
mahei.li@unisg.ch

Filters

3
1
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Developing Lawful Technologies – A Revelatory Case Study on Design Patterns
    ( 2021)
    Dickhaut, Ernestine
    ;
    Li, Mahei
    ;
    Janson, Andreas
    ;
    Leimeister, Jan Marco
    Higher legal standards regarding the data protection of individuals, such as the European General Data Protection Regulation, increase the pressure on developing lawful systems. In the development of technologies, not only developers are involved. It also requires knowledge from other stakeholders, such as legal experts, that lack technical knowledge but are required to understand IT artifacts. We see two strings that can benefit from the use of design patterns: first, the well-known use of design patterns to support developers in case of recurring problems. Second, we see potential that legal experts, who have to interact with and understand complicated, novel technologies, benefit from the same patterns. We conduct a revelatory case study using design patterns to develop and assess a smart learning assistant. We scaffolded the case interpretation through the human-centered view of socio-materiality and provide contributions concerning the use of design patterns in the development and assessment of lawful technologies.
    Type: conference paper
    Journal: Hawaii International Conference on System Sciences (HICSS)
    Volume: 54
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/110912
  • Publication
    Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-based Textual Data Quality Enrichment
    ( 2023)
    Reinhard, Philipp
    ;
    Li, Mahei
    ;
    Dickhaut, Ernestine
    ;
    Peters, Christoph
    ;
    Leimeister, Jan Marco
    AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher pre-diction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of as-sessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems.
    Journal: International Conference on Design Science Research (DESRIST)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107924
  • Publication
    A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-Creation Perspective
    ( 2023)
    Reinhard, Philipp
    ;
    Li, Mahei
    ;
    Dickhaut, Ernestine
    ;
    Reh, Cornelius
    ;
    Peters, Christoph
    ;
    Leimeister, Jan Marco
    Artificial intelligence (AI) possesses the potential to augment customer service employees e.g. via decision support or solution recommendations. Still, its underlying data for training and testing the AI systems is provided by human annotators through human-in-the-loop configurations. However, due to the high effort for annotators and lack of incentives, AI systems face low underlying data quality. That in turn results in low prediction performance and limited acceptance by the targeted user group. Faced with the enormous volume and increasing complexity of service requests, IT service management (ITSM) especially, relies on high data quality for AI systems and in-corporating domain-specific knowledge. By analyzing the existing labeling process in that specific case, we design a revised to-be process and develop a conceptual model from a value co-creation perspective. Finally, a functional prototype as an instantiation in the ITSM domain is implemented and evaluated through accuracy metrics and user evaluation. The results show that the new process increases the perceived value of both labeling quality and the perceived prediction quality. Thus, we contribute a conceptual model that supports the systematic design of efficient and interactive labeling processes in diverse applications of reinforcement learning systems.
    Journal: International Conference on Design Science Research (DESRIST)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107925