Information Systems Research

Information Systems Research (ISR) is a multidisciplinary field studying the interaction between information technology and organizational processes. It encompasses technical, behavioral, and decision sciences to understand how information systems impact organizational effectiveness and societal well-level. In the context of AI, ISR explores ethical considerations, algorithmic bias, and human-AI collaboration. This research is critical for the development of standards like ISO 42001 and compliance with the EU AI Act, which require organizations to be able to explain and justify their AI-driven decisions. Unlike pure computer science research, ISR focuses on the 'human and organizational' element of technology deployment, making it essential for effective AI governance and risk management. For enterprises, this means moving beyond technical accuracy to ensure AI systems are ethical, legal, and socially acceptable.

ISR principles are applied through a three-stage framework: 1) Governance Design: Establishing ethical principles and AI usage policies based on ethical frameworks identified in ISR. 2) Risk Identification: Using frameworks like the NIST AI RTO (Responsible AI) to categorize risks into types like bias, transparency, and security. 3) Monitoring and Mitigation: Implementing KPIs to track AI performance and compliance. For example, a multinational company implementing AI in HR could use ISR-based bias-detection metrics to ensure compliance with the EU AI Act's high-risk AI requirements. This could be measured by a reduction in adverse impact ratios by up to 30% within the first year. The ultimate goal is to create a closed-loop system where AI risks are identified, mitigated, and reported according to ISO 42001 standards, ensuring long-term regulatory compliance and stakeholder trust.

Taiwan enterprises typically face three challenges: First, the 'Regulatory Knowledge Gap,' where companies struggle with the specific requirements of the EU AI Act and emerging local regulations. This can be solved by partnering with legal and technical experts to create a compliance roadmap. Second, 'Organizational Silos,' where IT teams and legal departments do not communicate effectively on AI risks. The solution is to establish a cross-functional AI Governance Committee. Third, 'Data Quality and Governance,' which is a major risk factor in AI bias. Companies should invest in data-centric governance and lineage tracking. The recommended priority is to be: Month 1: Inventory all AI use cases; Month 2: Define risk levels and control measures; Month 3: Implement the first compliant AI application. This structured approach ensures the company meets both international standards and local expectations within a year.

Winners Consulting Services Co., Ltd. specializes in Information Systems Research for Taiwan enterprises, delivering compliant management systems within 90 days. Free consultation: https://winners.com.tw/contact