The paper identifies technical challenges in AI

The gap

Evidence profile

Sourced from the recommendations and inline gaps and limitations and future work and limitations section and stated research gap of the source papers, classified as general, spanning 6 journals. Those papers have been cited 2 times in total.

Supporting evidence — 8 representative gaps

• A Transformative Relationship: Artificial Intelligence Influence in the Business World (2026) · International Journal of Science and Research (IJSR) · doi

  Organizations should develop a clearly defined AI strategy aligned with their long-term business goals. Investment in employee training, upskilling, and reskilling programs is essential to support effective human–AI collaboration. Strong AI governance mechanisms should be implemented to address ethical challenges such as bias, transparency, and data privacy.

  generalrecommendationsevidence 5/5
  Keywords: organizations develop clearly defined strategy aligned long term business goals investment employee training upskilling reskilling
• Artificial Intelligence-Powered Smart City Transformation: A Framework and Comparative Case Study Analysis (2026) · Artificial Intelligence for Sustainable Cities · cited 2× · doi

  Future research should examine co-creation strategies, digital democracy platforms, and participatory governance models that let people actively participate in urban innovation and decision-making. However, further investigation is needed to comprehend the moral ramifications of AI-based governance systems, including concerns about algorithmic transparency, data security, and public accountability.

  generalinline gapsevidence 5/5
  Keywords: governance future examine creation strategies digital democracy platforms participatory models people actively participate urban innovation
• ALGORITHMIC GOVERNANCE AND THE CRISIS OF LEGAL MORALITY: REVISITING THE HART–FULLER DEBATE IN AUTOMATED DECISION-MAKING (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  ▪ Algorithmic systems used in governance must remain subject to constitutional scrutiny and judicial review. ▪ Governments should adopt mandatory transparency and explainability requirements for automated decision-making systems. ▪ Human oversight must remain central in all high-impact legal and administrative decisions. ▪ Independent regulatory bodies should monitor algorithmic systems for discrimination, bias, and procedural unfairness. ▪ Citizens affected by automated decisions must possess effective rights of appeal and access to understandable explanations. ▪ Legal education and judicial training should include technological literacy to ensure effective oversight of AI systems. ▪ International legal frameworks should develop common principles governing ethical and accountable AI governance.

  generalrecommendationsevidence 5/5
  Keywords: governance systems algorithmic automated legal human morality must constitutional transparency procedural accountability hart fuller remain
• Governing Algorithms: Transparency, Digitalization, and Risk in European Public Administration – A Comparative Study (2026) · ADMINISTRATIE SI MANAGEMENT PUBLIC · doi

  information structures strongly influence user decision-making, suggesting that transparency should be understood not only as a legal requirement, but also as a practical mechanism for building confidence in digital systems (Křečková et al., 2025).The introduction of explainable AI (XAI) and open data initiatives is frequently cited as a means of enhancing accountability and public trust (Toledo, 2026). Nevertheless, a critical examination reveals this approach. Transparency alone may not be sufficient to ensure accountability, particularly when: citizens lack the technical expertise to interpret algorithmic processes; institutions fail to provide meaningful explanations; and transparency mechanisms are implemented superficially. Thus, transparency should not be viewed as an end in itself, but as part of a broader framework that includes institutional capacity, legal enforcement, and public engagement. Algorithmic risk is increasingly recognized as a key challenge in public administration. The literature identifies several types of risks, including discrimination, privacy violations, and systemic bias. Importantly, recent studies argue that these risks are not inherent to AI technologies but are the result of governance failures (Toledo, 2026). This perspective shifts the analytical focus from technology to institutions, emphasizing the role of regulatory frameworks and oversight mechanisms. Empirical cases, such as those documented in European contexts, demonstrate that even advanced digital administrations can produce harmful outcomes when governance structures are inadequate. This highlights the importance of integrating ethical and legal considerations into AI deployment. Comparative research provides valuable insights into how different governance models influence algorithmic outcomes. Similar comparative, indicator-based approaches have recently been applied in digitalization research, for example, in EUlevel analysis showing that digital entrepreneurial ecosystems can be examined through panel data and clustering techniques to identify differentiated country patterns and broader sustainability effects (Khatami et al., 2024). Studies show that variations in digital maturity, regulatory frameworks, and institutional capacity lead to different configurations of risks and benefits. For example, highly digitalized countries such as Estonia are often associated with effective governance models, while other contexts reveal tensions between innovation and accountability. However, the literature remains fragmented, with limited cross-country analyses integrating multiple variables simultaneously. This represents a significant gap, particularly in understanding how digitalization, AI adoption, transparency, and risk interact over time.

  generallimitationsevidence 5/5
  Keywords: transparency digital governance legal accountability public algorithmic risks structures influence toledo particularly institutions mechanisms broader
• Representation Governance: Institutional Control, Protocol Coordination, and Allocative Authority in AI-Mediated Markets (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The scope of this theoretical framework is limited to the representation layer—the infrastructures that determine data quality, provenance, verifiability, machine readability, and semantic consistency of representations. The applicability of AI-specific regulatory frameworks—including but not limited to the EU AI Act, ISO/IEC 42001, or other AI governance standards—depends on the concrete implementation, the specific role of the actor, the system architecture, and the applicable jurisdiction.

  generalinline gapsevidence 5/5
  Keywords: limited specific scope theoretical framework representation layer infrastructures determine quality provenance verifiability machine readability semantic
• Rethinking the future (2026) · JPM - Journal of Perspectives in Management · doi

  The rapid advancement and widespread adoption of artificial intelligence (AI) have expanded its influence across economic activities, public services, and organizational processes (OECD, 2024; Maslej et al., 2024). As AI systems become increasingly integrated into public administration and organizational decision-making, governance has emerged as a multidisciplinary concern encompassing ethical considerations, institutional accountability, regulatory oversight, and risk management (Batool et al., 2025; Zaidan & Ibrahim, 2024). Accordingly, AI governance encompasses the policies, institutional arrangements, regulatory mechanisms, standards, and organizational practices that guide the AI lifecycle while balancing innovation with transparency, accountability, fairness, privacy, and human oversight (Mohamed et al., 2025; Prem, 2023).

  generalfuture workevidence 5/5
  Keywords: organizational public governance institutional accountability regulatory oversight rapid advancement widespread adoption artificial intelligence expanded influence
• The Impact of Artificial Intelligence on Consumer Behaviour and E-Commerce Trends (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Algorithmic biases and data privacy concerns are significant limitations of AI in e-commerce. The paper highlights the need for businesses to comply with regulations, such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR). The study emphasizes the importance of auditing AI systems regularly to minimize bias and ensure fairness in algorithms.

  generallimitations sectionevidence 5/5
  Keywords: algorithmic biases data privacy concerns significant limitations e-commerce
• Transparency Discourse on Digital Platforms: A Comparative Textual Analysis of Platform Reports and Regulatory Texts in the EU and Türkiye (2026) · Lectio Socialis · doi

  The study identifies a gap in the literature regarding the ways in which platforms construct transparency and accountability through their reports. The research highlights the need for greater transparency and accountability in content moderation decisions. The study argues that there is a lack of understanding of how algorithmic governance operates and how it can be made more transparent and accountable.

  generalstated research gapevidence 5/5
  Keywords: study identifies gap literature regarding ways platforms construct

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