人工知能学会研究会資料
SIG-KST-045-02(2023-11-24)

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Proposal of a Method to Articulate the Knowledge and Ideas in the Decision Making
Process in AI-Human collaboration
Yichuan Zhang
Supervisor: (Professor) Kazuo Hiekata
As AI technologies rapidly advance, knowledge transfer emerges as crucial for sustaining competitive advantage
through expertise sharing. While traditional methods play an important role, they face limitations in today's dynamicenvironment. This paper explores how AI is changing knowledge transfer approaches, especially between explicit andtacit knowledge forms.AI serves not only as a new medium but enables human-AI collaborative training. It facilitates
expertise conveyance beyond traditional boundaries. However, knowledge transfer also encounters new challenges in
the AI era, such as how to effectively codify, decode and explain knowledge through technology. By analyzing a casestudy on Tokyo Electric Power Company's demand forecasting adoption of AI-augmented methods, the paper
demonstrates how such novel approaches can address limitations faced previously. Insights gleaned provide guidanceon addressing knowledge transfer bottlenecks as AI continues to progress.
Keywords: Knowledge Transfer, Human-AI collaboration, Electricity demand forecasting, Unexpected event
forecasting, Decision-Making support.

1Introduction

Knowledge transfer is crucial for organizational
learning and competitiveness. However, it faces
challenges due to the tacit nature of knowledge anddifferences in contexts. This research explores an AIenabled, data-driven approach to knowledge transfer
using behavioral logs. It records experts' entire decision
processes during a prediction task and visualizes
workflows. This allows context preservation and process
understanding for recipients. The method is applied to anelectricity demand forecasting task combining domain
and technical knowledge. It demonstrates the potential of
human-AI collaboration in knowledge transfer andsharing．
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Literature Review

Spitzer & Goutier proposes a framework to utilize
human-AI collaboration for knowledge transfer fromexperts to novices through explicit and tacit knowledge[1]. Garavelli et al. provide a cognitive perspective on
defining knowledge technology properties based on therequirements of different knowledge transfer contexts [2].
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Objective

The objective of this study is to explore an AI-enabledapproach to capture task-specific expert knowledge(TSEK) through behavioral logging and workflow
analysis within a human-AI collaboration (HACI)
environment. It seeks to examine how HACI caneffectively log and contrast the decision-making
processes of experts and novices with/without technicalbackgrounds, in order to understand differences inknowledge manifestation and enable knowledge transferfrom experts to novices.
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Proposed Methods

This research methods bring a novel data-driven
approach to knowledge transfer using human-AIcollaboration. By logging experts' full decision processes
during task execution and leveraging AI techniques toreconstruct visual workflows, it achieves tacit knowledge
formalization and context preservation. The approachfacilitates knowledge absorption and application byenabling process understanding for recipients.
Moreover, the behavioral log analysis and workflow
optimization present new possibilities for dynamic
knowledge sharing platforms. This human-in-the-loop
method signifies an innovative direction to augment
traditional knowledge transfer limitations across diverse
domains involving both specialized and technicalknowledge.

Figure 1. Framework in HACI designed framework

The key components as shown in figure 1 of the
proposed knowledge transfer framework are: 1)
Behavioral logging of expert workflows and decision
making processes during task execution. 2) Encoding
logged data into structured datasets. 3) Applying AI
techniques to analyze encoded data and generatevisualizations of expert workflows. 4) Knowledge
recipients access visualizations to enable process
comprehension and facilitate absorption of tacitknowledge in support of their decision making. 5)
Continuous refinement of workflows through iterative
analysis of user behaviors to reflect evolving bestpractices.
By leveraging AI to generate visualizations of logged
expert workflows, this approach helps reconstructknowledge networks for learners. In contrast to
traditional methods, it aims to capture implicit
knowledge as sharable contextualized workflows while
maintaining the ability to address learning challenges.
The workflow visualizations also support application oftransferred knowledge and accumulation of new skills
through decision making. Overall, this human-AI
knowledge transfer framework enhances traditional
methods by addressing limitations in transferring tacitknowledge and absorbing complex processes.

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4.1 Experiment procedure

In this study, time series forecasting was selected as thedemonstration task to integrate knowledge transfer andHACI. Currently, many forecasting tasks involve humanAI collaboration where AI plays an assisting role while
humans are primary decision-makers. The goal is toachieve more accurate predictions using forecast
accuracy as the evaluation metric. Domain knowledge
will be incorporated through scenarios assumingindividuals with relevant expertise can perform better.
The experiment involves preprocessing raw time series
data by segmenting into predictive features. An initial
GAM model will be trained and forecasts generated.
Participants with domain/technical knowledge will
review background materials and forecast results. They
can iteratively adjust machine forecasts through aninterface, applying knowledge to select adjustment
factors, time periods, methods and intensities. Surveys
will capture decision criteria after each adjustment cycle.
Upon completion, an overall process feedback surveywill also be collected. This aims to systematicallyevaluate benefits of combining machine learning withhuman judgment and expertise in knowledge transfer.
5This research visualized the expert's full forecasting
workflow from the behavioral logs, reconstructing thedecision process. Recipients often lack clarity on therequired steps, sequencing, and motivations.
Visualizing the logged workflows provides useful
understanding of the overall forecasting process andindividual steps, enhancing real-world decision support
for inexperienced recipients. Leveraging complete datalogs to visualize workflows improves process
comprehension.
As shown in Figure 2, the workflows from twoparticipants with technical knowledge were recorded
from start to finish. Their decision rationale was also
captured at key points. Notably, the two subjects
exhibited very different workflows, owing to dissimilar
mental models of the overall task. The varied
workflows also reflect differences in applying tacitknowledge, which is challenging to accurately capture,especially remotely. Figure 3 shows anothervisualization supporting workflow understanding by
mapping process steps chronologically. This not only
depicts experts' decision flows but also reveals timespent. With both workflow and timing details, theEvaluation Scenario of Current Research Method

5.1 Case study of electricity demand forecastingThis research uses electricity demand forecasting inTokyo as a case to validate the proposed knowledgetransfer approach. With complex real-world conditions,this prediction task integrates specialized domain
knowledge and technical skills. The task requires
participants to make prediction based on algorithmicprediction. All participants (� = 9) comes from
University of Tokyo with 6 of them have the techniquebackground in Artificial Intelligence.
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Preliminary of Results & Discussion

The self-designed user interface by Python logged all
behaviors in detail throughout the entire prediction task,
from start to finish. This enabled the collection of fullbehavioral log data as the participants executed the real-Fig. 3 Flowchart of participants workflow (2 Samples)

figure reflects the knowledge may have different
representation when considering of the time, especiallyfor expert user and non-expert users.
7Conclusion

In conclusion, this study contributes an AI-enabled
method to capture task-specific expert knowledge
through behavioral logging and workflow analysis
within a human-AI collaboration environment. The
preliminary case study demonstrates the potential for
HACI systems to log and contrast the decision-making
processes of experts versus novices with and without
technical backgrounds when time spent is considered.
ReferenceFig. 2 Flowchart of participants workflow (2 Samples)world forecasting workflow from their expertise. The AI
backgrounds of participants ensured understanding oftechnical factors while their completion of the task
incorporated specialized electricity domain knowledge.

1. Spitzer, P., Kühl, N., & Goutier, M. (2022). Training
novices: the role of human-ai collaboration andknowledgetransfer..
https://doi.org/10.48550/arxiv.2207.00497
2. A.Claudio Garavelli, Michele Gorgoglione; Barbara
Scozzi (2002). Managing knowledge transfer byknowledge technologies. , 22(5), 0–279.