Optimising Project Portfolios through Responsible AI and Ethical Compliance

International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublica[email protected]m 200 Optimising Project Portfolios through Responsible AI and Ethical Compliance Adeyinka G. Ologun 1,2 , Rukayat Abisola Olawale 3 , Olatunji Bolanle Blessing 4 , Ijeoma Chioma Mordi 5 , Ngozi Blessing Umoru 6 , Sandra A Palmer 7 , Kemi K.Oladapo 8 1 Department of Business School, University of Wolverhampton, England, United Kingdom. 2 Faculty of Business and Media, Selinus University of Sciences and Literature, Italy. 3 School of Management Sciences, Babcock University, Ilishan Remo, Ogun State, Nigeria, 4 Department of Marketing, Kwara State Polytechnic, Ilorin, Nigeria 5 Department of Information, Intellectual Property Law, University of Lagos, Nigeria 6 Department of Social Science Education, University of Nottingham, Nottingham, United Kingdom 7 Department of Social Science Education, Leading Learning & Teaching, The University of Dundee, U.K. 8 MBA with Project Management, Abertay University, Bell Street, Dundee, DD1 1HG, United Kingdom, *Corresponding author, E-mail: [email protected] Internaonal Journal of Research in Management Fields Available online on hp://rspublicaon.com/IJRMF/IJRMF.html ISSN (P) 2577-1876 (O) 2577-4274 ARTICLE INFO ABSTRACT ©2025 RS Publication Paper ID: IJRMF69479F98D36DA Published: 2025-12-22 DOI: https://dx.doi.org /10.5281/zenodo.18 021350 Page No: 200-216 This study develops and validates the Unified Responsible AI Governance Framework for Project and Portfolio Management (URAI-PM), designed to balance innovation with ethical and regulatory compliance in AI-enabled projects. Using a mixed-methods approach, the research combined systematic literature synthesis (n = 624 studies screened), expert validation (N = 18), and simulation testing across 40 AI-driven projects. The mathematical model optimised portfolio performance using a multi-objective function that incorporated ethical compliance, innovation indices, and risk penalties, and was solved via the NSGA-II algorithm. Results show that URAIPM improved the Ethical Risk Detection Rate (ERD) from 0.41 to 0.78 (+90%) and increased Innovation Throughput (IT) from 0.63 to 0.74 (+17%), while overall Portfolio Value (V) rose by 37%, with a convergence error below 10⁻³. The Governance–Innovation Balance Index (GIBI) stabilised near 1.02 ± 0.08, confirming equilibrium between oversight and creativity. Expert ratings (α = 0.89 reliability) validated its practicality. URAI-PM offers a reproducible, quantitative foundation for integrating Responsible AI governance into modern project management ecosystems. Keywords: Responsible Artificial Intelligence, Project Portfolio Management, Ethical Governance, Innovation Optimization. Risk Management, AI Compliance Framework Cite This Paper: Adeyinka G. Ologun, Rukayat Abisola Olawale, Olatunji Bolanle Blessing, Ijeoma Chioma Mordi, Ngozi Blessing Umoru, Sandra A Palmer and Kemi K.Oladapo (2025). "Optimising Project Portfolios through Responsible AI and Ethical Compliance". INTERNATIONAL JOURNAL OF RESEARCH IN MANAGEMENT FIELDS (IJRMF), vol. 9, no. 6, 2025, pp. 200-216,. DOI: https://dx.doi.org/10.5281/zenodo.18021350 International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 201 1. Introduction In recent years, Artificial Intelligence (AI) has evolved from a niche technology into a foundational enabler across industries. Its adoption has expanded into domains traditionally reliant on human judgment, including project and portfolio management (PPM), where AI now supports decisionmaking, risk forecasting, resource allocation, and portfolio optimisation. These tools promise reduced uncertainty, automated routine tasks, and improved predictability of outcomes. However, such benefits are accompanied by significant ethical, regulatory, and governance challenges. As AI increasingly augments or replaces human decisions in project environments, it introduces risks related to bias, opacity, data privacy, and accountability gaps. Consequently, integrating AI into PPM requires not only technical capabilities but also robust, responsible governance. Embedding responsible AI (RAI) within project environments presents a dual challenge: sustaining innovation and agility while ensuring compliance with evolving ethical, legal, and stakeholder expectations. Many existing AI governance frameworks emphasise high-level principles—such as fairness, transparency, accountability, and security—but struggle to translate these principles into operational project contexts, for example, in determining when audits should occur or how ethical trade-offs should be managed [1]–[3]. At the same time, traditional PPM practices prioritise scope, cost, time, and value, with limited built-in sensitivity to AI-specific ethical concerns. As a result, organisations face a dilemma: overly cautious governance may suppress innovation, whereas ad hoc or fragmented AI adoption exposes projects to reputational, regulatory, and operational risks. AI use within PPM settings is particularly complex. AI systems are iterative, data-intensive, opaque, and highly context-dependent. Unlike conventional software, AI introduces uncertainty not only in project outputs but also in algorithmic behaviour over time. For instance, an AI-based resource optimiser may reallocate resources unpredictably as data distributions shift, while riskprediction models may misclassify rare events if not continuously retrained. These dynamics intensify ethical concerns surrounding accountability, robustness, and alignment with organisational values. Research on AI ethics identifies technological uncertainty, biased or incomplete data, and failures in managerial oversight as primary sources of ethical risk in AI International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 202 decision-making [4]. A project-level governance framework must therefore mediate between agility and control, innovation and assurance. Although AI has been widely applied in project risk management—such as predicting delays, cost overruns, and resource conflicts—the ethical dimensions of these applications remain underexplored. Much of the literature on AI in project management focuses on algorithmic performance or automation benefits, often acknowledging challenges related to data quality and explainability, but rarely integrating these concerns into comprehensive governance structures [5]– [7]. Even studies advocating “ethical AI in projects” often fail to integrate ethical principles holistically across portfolio decision-making processes. Industry practice mirrors this fragmentation: AI is frequently adopted in silos, governance mechanisms are applied inconsistently, and ethical oversight is deferred to peripheral reviews, undermining scalability and coherence. To address these gaps, this study proposes the Unified Responsible AI Governance Framework for Project and Portfolio Management (URAI-PM). The framework embeds ethical, legal, and regulatory checks throughout the project lifecycle and portfolio decision gates without unduly constraining innovation. It is designed to support both project teams and oversight bodies, such as Project Management Offices (PMOs), enabling responsible management of AI-driven projects at scale. By integrating compliance, transparency, risk mitigation, and innovation enablement, URAI-PM reframes responsible AI from an afterthought into a core dimension of PPM practice. This study makes five key contributions. First, it synthesises interdisciplinary insights from AI governance, PPM theory, ESG principles, and ethics literature to develop a governance architecture tailored specifically to project portfolios [1][8][9]. Second, it introduces a layered conceptual framework comprising Ethical Compliance, Governance Integration, Transparency and Accountability, and Innovation Enablement, supported by feedback and agility mechanisms. Third, it develops practical toolkits—such as audit templates, ethical risk-scoring methods, and dashboards—to operationalise responsible AI governance in real project settings. Fourth, the framework is validated through expert interviews and simulation testing in a project portfolio context, assessing its impact on innovation throughput and ethical risk detection. Fifth, the study International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 203 proposes a maturity model to guide organisations in staging their adoption of responsible AI governance within PPM environments. The broader context motivating this research reflects AI’s growing role as a strategic lever in project-based organisations. AI-driven forecasting, anomaly detection, scheduling optimisation, and portfolio selection are increasingly viable as data infrastructures mature. Reviews of AI in project and supply-chain domains document rapid growth in experimentation and deployment [5][10]. Yet these deployments are frequently associated with stakeholder mistrust, regulatory scrutiny, and system failures arising from opaque or biased AI decisions. While AI has demonstrated potential to enhance project performance and early risk detection [5][19], persistent challenges—such as data quality, model generalisation, user trust, and integration with human oversight—underscore that AI adoption is not a plug-and-play upgrade but a governance-intensive transformation. Ethics scholarship further highlights that AI risks extend beyond technical error to include systemic bias, reinforcing feedback loops, lack of auditability, and automation complacency, where human oversight deteriorates over time. Although transparency, accountability, fairness, and privacy are widely recognised ethical principles, significant gaps remain in operationalising them within organisational practice [11]–[13]. This governance gap is particularly acute in AIenabled project environments, where decisions are time-bound, politically negotiated, and resource constrained. Existing AI governance frameworks—including NIST AI RMF, ISO 42001, IEEE 7000, and OECD principles—remain largely generic and insufficiently aligned with project operations [1][14][15]. They provide organisational-level guidance but offer limited direction on how governance checkpoints should align with project lifecycles or portfolio decision gates. Similarly, emerging unified governance models operate at a macro level and do not treat projects as primary units of governance [16]. As a result, organisations are left to improvise, leading to inconsistency and oversight gaps. This study therefore, addresses the following research objective: to conceptualise, operationalise, and validate a unified Responsible AI governance framework tailored for project and portfolio International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 204 management, enabling ethical AI adoption without undermining innovation. Guided by five research questions, the study employs a design science approach integrating systematic literature synthesis, framework design, toolkit development, expert validation, and simulation-based evaluation. Overall, this research bridges theory and practice by embedding responsible AI governance directly into PPM systems. The URAI-PM framework elevates governance from a reactive audit function to a co-equal dimension of portfolio performance, offering PMOs, executives, and policymakers a structured pathway to achieve trustworthy, scalable, and innovation-supportive AI adoption in project-centric organisations. 2. Methodology The methodological design adopts a mixed-methods, design-science approach, integrating systematic literature synthesis, conceptual model development, expert validation, and computational simulation. This section (i) describes the reproducible process for identifying and synthesising relevant literature, (ii) outlines the development stages of the Unified Responsible AI Governance Framework for Project and Portfolio Management (URAI-PM), and (iii) introduces the mathematical model used to optimise ethical compliance and innovation performance in AIenabled PPM. The approach prioritises transparency, reproducibility, and rigour while grounding framework development in both theory and empirical insight [20], [21]. Figure 1 Design Science Research Phases for Developing the URAI-PM Framework International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 205 Research design overview The study follows a Design Science Research (DSR) paradigm, which emphasises the iterative creation and evaluation of artefacts—such as frameworks, models, and toolkits—to address practical problems through theory-informed design [22]. URAI-PM was developed through five interlinked phases (Fig. 1): (1) systematic literature identification and synthesis, (2) conceptual modelling and framework construction, (3) toolkit design and mathematical modelling, (4) expert validation and refinement, and (5) simulation testing. Feedback loops connect each phase to support continuous refinement and alignment between conceptual design and implementation needs [23]. Phase 1: systematic literature identification and synthesis A reproducible protocol was implemented to capture literature on Responsible AI (RAI), AI governance, and AI-enabled PPM. The review followed PRISMA 2020 guidelines, ensuring traceable inclusion and exclusion processes [24]. Searches were conducted in IEEE Xplore, Scopus, SpringerLink, and Web of Science, supplemented by grey literature searches in Google Scholar, ResearchGate, and SSRN for policy and standards sources (e.g., OECD, ISO, NIST) [25]. A Boolean query combined terms for Responsible/ethical AI, governance, and PPM (including risk and portfolio management), and was applied to titles, abstracts, and keywords for publications from 2018–2025 [26]. Screening proceeded in stages: 72 duplicates were removed; 340 records were excluded at title/abstract stage; and 137 full texts were assessed, yielding 28 studies meeting criteria of (i) explicit RAI or ethical AI content, (ii) organisational or project-management context, and (iii) a governance/assessment/compliance mechanism. Included studies were coded in NVivo 14 using a hybrid inductive–deductive approach, where deductive codes reflected established governance domains (ethics principles, compliance mechanisms, stakeholder roles) and inductive codes captured emergent patterns (e.g., agility integration, transparency tooling) [27]. Four thematic meta-categories were derived—ethical compliance mechanisms, governance integration processes, transparency and accountability tools, and innovation enablement strategies—which directly informed URAI-PM’s layered architecture. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 206 Phase 2: conceptual modelling and framework construction Using the synthesised themes, a layered governance framework was constructed through DSR iteration [22]. URAI-PM comprises four interconnected layers: (1) an Ethical Compliance Layer aligning project activities with regulatory and normative principles (e.g., EU AI Act, ISO 42001); (2) a Governance Integration Layer embedding compliance checkpoints into agile sprint cycles and portfolio decision gates; (3) a Transparency and Accountability Layer enabling audit trails, explainability dashboards, and traceability records; and (4) an Innovation Enablement Layer ensuring governance supports adaptive experimentation rather than constraining it. The framework was cross-mapped against standards such as NIST RMF, OECD principles, and ESG-AI approaches to ensure regulatory relevance and operational feasibility in project environments [8], [14]. Phase 3: toolkit design and mathematical modelling To operationalise URAI-PM, a quantitative model was formulated to represent the compliance– innovation trade-off and enable portfolio optimisation. Each project i is characterised by a compliance score   ∈ [0,1]and an innovation index   ∈ [0,1]. Portfolio value is maximised by combining innovation and compliance benefits while penalising ethical risk   , with weights , , satisfying  +  +  = 1[28]. Ethical risk is modelled as a non-linear function of compliance deviation,   = (1 −   )  , where  > 1intensifies penalties for low-compliance projects, consistent with ethical risk escalation theory [29]. Practical resource and time constraints are enforced through binary project selection and organisational limits. System balance is assessed through the Governance–Innovation Balance Index (GIBI), defined as the ratio of aggregate compliance to aggregate innovation (with a small to avoid division by zero); values near 1 indicate equilibrium, while deviations indicate overor under-governance. Optimisation is performed using NSGA-II due to its suitability for nonlinear, multi-objective tradeoffs [30]. The algorithm iteratively evaluates fitness, ranks non-dominated solutions, applies crossover and mutation, and retains feasible portfolios until convergence (∣ Δ ∣< 10  ) or a maximum generation threshold. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 207 Phase 4: expert validation and refinement URAI-PM and its toolkit were validated using semi-structured interviews and Delphi rounds to ensure practical relevance and theoretical coherence [31]. Experts were purposively sampled for dual experience in AI governance and project delivery (N=18): public-sector project directors (6), AI ethics/compliance officers (5), academic governance scholars (4), and project assurance consultants (3). In Round 1, experts reviewed diagrams and tool prototypes and provided feasibility-focused feedback. In Round 2, they rated relevance, completeness, and implementability on a 5-point scale; consensus was defined as interquartile range < 1.0 [32]. Reliability analysis (Cronbach’s α = 0.89) indicated strong consistency. Feedback informed refinements including simplified terminology, a human-oversight role-definition module, and dynamic ethical-risk weighting. Phase 5: simulation and evaluation; validity and limitations Simulation testing used synthetic data calibrated to public-sector innovation portfolio characteristics (n=40 projects) and compared three conditions: baseline (no governance), partial RAI integration (audits at start/end), and full URAI-PM integration (continuous checkpoints). Across 100 runs, outcome metrics included Ethical Risk Detection Rate (ERD), Innovation Throughput (IT), GIBI, and portfolio value, with URAI-PM producing statistically significant improvements in ERD and IT (p < 0.01) relative to baseline [33]. Validity was strengthened through triangulation (literature, expert input, simulation) and standards cross-checking [14]. Reliability was supported by transparent documentation and reported parameter settings (Appendix A) [34]. Ethical safeguards included informed consent, anonymisation, and ethics approval. Limitations include the abstraction of simulation relative to organisational complexity, potential subjectivity in compliance/innovation scoring, and the possibility of local optima in NSGA-II depending on tuning. Nonetheless, the overall design provides a replicable pathway for integrating responsible AI governance into PPM through a validated, quantitatively grounded framework. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 208 3. Results 3.1. Quantitative simulation outcomes Simulation experiments compared three governance conditions across a portfolio of 40 AI-driven projects: Baseline (no governance integration), Partial Governance (audits only at initiation and closure), and URAI-PM Integration (continuous, multi-layer checkpoints embedded across the lifecycle). Key dependent variables were Ethical Risk Detection Rate (ERD), Innovation Throughput (IT), Governance–Innovation Balance Index (GIBI), and Portfolio Value (V) as defined in Eq. (1). Each condition was evaluated over 100 simulation runs using NSGA-II (population size 50, crossover 0.8, mutation 0.1). Figure 2 Layered Architecture of the URAI-PM Framework for Ethical Governance and Innovaon Enablement Results indicated statistically significant improvement under URAI-PM. Mean ERD increased from 0.41 (Baseline) to 0.78 (URAI-PM), representing a 90% improvement in ethical risk visibility. IT increased from 0.63 to 0.74, indicating that governance controls did not suppress project creativity or delivery flow. Portfolio Value improved from 42.7 to 58.6 on a normalised scale, supporting the claim that balancing innovation and compliance produces stronger aggregate portfolio performance [35]. The model converged within 150 generations, suggesting computational stability. International Journal of Research in Management Fields ISSN (P) 2577-1876 (O) 2577-4274 Available online on http://rspublication.com/IJRMF/IJRMF.html Volume 9 Issue 6 -2025 DOI: 10.5281/zenodo.18021350 Original Article ©2025 RS Publicaon, rspublicaonh[email protected]m 215 [22]. R. A. Olawale, O. O. Odesanya, P. T. 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