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Aligning AI with Ethical and Strategic Management: A Multi-Case Exploration of Drucker’s Human-Centered Principles
Get PDFThe strategic adoption of Artificial Intelligence (AI) within organizational management significantly enhances operational efficiency and innovation but simultaneously introduces complex ethical considerations. This study applies Peter Drucker's established human-centered management principles—ethical responsibility, decentralized decision-making, knowledge empowerment, customer-centric innovation, and continuous organizational learning—to analyze and compare the AI governance strategies of JPMorgan Chase, Amazon, and Waymo. Utilizing a robust qualitative multi-case methodology informed by Yin (2018) and Eisenhardt and Graebner (2007), the research highlights distinctive strategic implementations: Amazon's robust decentralization fostering agile, customer-focused innovation; JPMorgan Chase's stringent ethical oversight and regulatory compliance frameworks; and Waymo's comprehensive safety protocols combined with intensive employee empowerment initiatives. Findings extend Drucker’s theoretical insights to contemporary digital contexts, proposing practical frameworks that support ethically responsible, strategically innovative, and human-centered management of AI technologies in diverse organizational environments
Idea Generation With a Majority of AI Teammates: Exploring Productivity Effects, Process Gains and Process Losses
Get PDFLeveraging AI agents as teammates has gained momentum in research and practice. Yet, how human team members react to multiple AI teammates (AITMs), particularly when they are the majority, is underexplored. Therefore, we conducted an exploratory study applying experimental methods, in which 66 participants collaborated in teams of three humans and four AITMs on an idea generation task. Our results show that the AITMs were highly productive, with half of the top ideas being AI-generated, and contributed to process gains for human team members. However, we also observed process losses, such as attention blocking or reduced human self-efficacy. While participants were open to future collaboration with multiple AITMs, they preferred team compositions with an equal or fewer number of AITMs than humans. We conclude that an optimal team composition balances productivity gains with the need to mitigate process losses that arise in AI-majority teams and maintain human engagement
Integrating Blockchain and Ontologies in Artificial Intelligence Regulation
Get PDFArtificial intelligence has evolved to possess advanced cognitive abilities, leading to a heightened awareness of ethical and legal dilemmas and potential threats. The training of AI models involves processing data that may violate established legal and moral norms, prompting widespread concern. It is essential to closely monitor and document the deployment of AI models to prevent illicit activities. Despite the establishment of various standardized frameworks for AI governance, achieving transparency and uniformity remains a persistent challenge. This study proposes a novel framework that combines blockchain technology with ontologies to provide a regulation-approval record showing that AI models have passed compliance auditing. The decentralized nature of blockchain ensures no authority over the trained models. Moreover, developing a concise ontology to save AI models in compliance will reduce the cost of keeping the data on the blockchain. Adherence to this framework provides a roadmap for ensuring transparency, semantic traceability, and unified standards of compliance with international ethical and legal standards
Do Two AI Physicians Equal One Human Physician in Online Healthcare Consultations?
Get PDFWith the development of technology, two artificial intelligence (AI) physicians collaborating to interact with depressed patients in online healthcare consultations has become possible. However, little is known about whether two AI physicians are perceived to be better than one AI and how two AIs can mitigate the weaknesses of one AI versus one human in this context. Drawing on the stereotype content model, wisdom of crowd effect, and synergy effect, we build a research model to understand how different physician type affects patients’ intention to use the service. Results of an experiment show that, while one AI is less likely to evoke perceived accuracy and caring than one human physician, two AIs can mitigate such weaknesses of one AI. Further, there is no difference between two AIs and one human physician in enhancing perceived accuracy and caring. Perceived accuracy and caring increase trust, thereby enhancing intention to use the service. This research makes contributions by examining the influences of two AI physicians
A Review of EEG and Eye-Tracking Applications in Human-Robot Interaction Across Domains
Get PDFWith advances in electroencephalography (EEG) and eye-tracking, these physiological sensing methods have become vital in human-robot interaction (HRI) research. This review systematically examines their applications across education, healthcare, industry, and social domains, covering both single-sensor uses and integrated EEG–eye-tracking studies. We analyze experimental designs, data synchronization, signal fusion, technical challenges, and potential benefits. This review highlights the importance of multimodal physiological data in improving HRI quality and effectiveness. This review provides practical guidance for leveraging emerging technologies to enhance user experiences, stimulate innovation, and shape future research in intelligent interactive systems through multimodal sensing
Vibe Design: Human-in-the-loop AI Agents for UI Design with Large Language Models
Get PDFNew tectonic shifts are looming in the UI design process due to advances in LLMs. These shifts have led researchers to adopt LLM-based approaches to develop design systems. However, such systems often focus on narrow functions and rely on code-based data that limits real-world applicability. We propose Vibe Design, an agentic framework that enables designers to co-create and evaluate prototypes with the LLM-based agents. The framework consists of a design agent that supports UI creation and a user testing agent that generates usability feedback through simulated user interviews, guided by the Persona-Scenario-Goal methodology. We also incorporated a human-in-the-loop mechanism to enhance the reliability and quality of the LLM-generated responses. We successfully generated functional prototypes and extracted user requirements from diverse personas. This study provides insights into the use of LLMs for both UI generation and user feedback collection, offering practical implications for future design workflows
An Appliance-Agnostic Mode Identification Framework via Dynamic Programming Least Squares and Piecewise Regression for Non-Intrusive Load Monitoring
Get PDFAccurate appliance-level Load Monitoring (LM), particularly through Non-intrusive LM (NILM), is increasingly important for effective Demand-Side Management (DSM) in modern power systems. However, NILM techniques often struggle to achieve high disaggregation accuracy due to challenges in identifying underlying appliance operating modes. This paper presents an analytical, appliance-agnostic framework that integrates Dynamic Programming Least Squares (DPLS), piecewise linear regression, and rule-based clustering to robustly extract and characterize the transient signatures of appliances and estimate their associated mode characteristics especially the unique consumption trend inside each of the modes. Applied to granular sub-metered data from two representative refrigerators, the proposed method constructs concise mode dictionaries that capture key consumption behaviors—including steady-state, compressor transitions, and defrost events. The resulting structured mode dictionaries achieve over 98% variance explained (R^2), with the error of as low as 0.126 p.u., which can be readily adopted by NILM algorithms, improving disaggregation accuracy and generalization across appliance types
Bridging Flood Sensing and Traffic State Estimation for Roadside Flood Sensor Placement: A Bayesian Network-based Approach
Get PDFUrban flooding's impact on road networks and traffic is becoming a more serious problem in the context of climate change. Estimating traffic states under flood impacts requires information about road inundation. Roadside flood sensors are becoming an important source of this information. This study proposes a Bayesian Network-based approach to strategically place roadside flood sensors to enhance the information input for traffic state estimation. By integrating historical traffic speed data with flood-induced road closures, the method quantifies the uncertainty reduction in traffic estimation through entropy analysis and applies a greedy algorithm to optimize sensor placement. In the results, flood information from optimally placed sensors reduced the uncertainty of traffic states in the Bayesian Network, thereby helping traffic state estimation and prediction achieve more certain estimation results. Beyond proposing a sensor placement method, this study also enhances the information value of flood sensing and is expected to promote larger-scale application of roadside flood sensors
An Empirical Analysis of Design Principles and Functional Requirements for Data Trustees
Get PDFOrganizations increasingly rely on sharing data to drive collaboration and gain a competitive advantage. However, privacy, security, and regulatory compliance issues often prevent the sharing of sensitive information. Data trustees have emerged as a potential solution to these challenges, but there is still significant ambiguity about the key elements and best practices that define them and the related technologies. This lack of clarity hinders the development and adoption of data trustees, stalling progress in the field. In response, we conducted a comprehensive survey of the data trustee community and received 35 complete responses from experts in various data governance roles. Our findings reveal common trends, important needs, and expectations for data trustees. By synthesizing these insights, our study contributes to the practical and theoretical understanding of the design principles and functional requirements for data trustees. It provides guidance for industry practitioners and extends the evidence-based discourse in the academic community