1. In the rapidly advancing landscape of artificial intelligence (AI) integration into consumer and enterprise products, the dual potential of AI to drive positive impact or cause unintended harm is unprecedented. For product managers (PMs), whose traditional role involved delivering functional and delightful user experiences, the new mandate is to proactively ensure that their systems are fair, safe, and understandable to users and other stakeholders. This research paper tackles the urgent need for an actionable bridge between aspirational high-level AI ethics principles and their concrete operationalization in the day-to-day product development lifecycle (Smith et al., 2025; Sun et al., 2024). Our central thesis is that engineering trustworthy AI systems is not solely a technical endeavor but is also squarely in the product manager’s domain and requires a well-structured, repeatable, and proactive risk management framework to manage the inherent tension and pushback of delivering innovation rapidly with high business value. This paper presents a comprehensive and actionable checklist and framework designed for product managers to effectively manage, often iteratively, the three critical dimensions of bias, safety, and transparency. Structured to guide decision-making and implementation from foundational governance pre-conditions to practical execution on the product team, the framework begins with foundational pillars, critical for the PMs to build a house on to construct trustworthy products – with the essential pre-conditions for establishing trust, including a clear AI Ethics Charter aligned with company values and Responsible AI (RAI) Team structures and Accountability Maps with clear owner designation (Smith et al., 2025). The checklist's content is structured into three major pillars which represent essential intervention zones for bias management along with safety and transparency considerations. For each of the pillars, we provide specific and actionable steps across various stages of the product lifecycle that span from building contextual and technical understanding to building robust solutions to managing the system in production. This includes: 1. Bias & Fairness Management: Provide actionable steps for assessing, mitigating, and continuously monitoring and auditing bias across the AI pipeline (Jacob, 2025). This includes guidance on implementing a thorough Contextual Risk Assessment to understand the characteristics and distribution of impacted user groups; Robust Data Provenance & Evaluation to source, document, and vet the datasets; Definition of Fairness Metrics & Guardrails and Formal Continuous Bias Monitoring & Mitigation plans for any unacceptable impacts discovered after product launch. 2. Safety & Harm Prevention: Identify strategies for assessing and mitigating both technical and social operational risks (Sun et al., 2024). This section covers Rigorous Failure Mode Analysis with tools like adversarial testing and stress testing for edge cases, Designing Human-in-the-Loop Intervention & Fallback Mechanisms to allow users or stakeholders to intervene, having a defined Incident Response Protocol when things do go wrong and cause harm, and Robust Security & Access Controls to prevent misuse. 3. Transparency & Explainability: Transparency and explainability are the operationalization of trust in the technology and managing expectations of the stakeholders on how it is working to help and where it will not. The amount of transparency and explainability is modulated by both the technical and operational constraints and the user and stakeholder needs (Olorunfemi et al., 2024). This section provides a framework for Determining Staged User Communication from simpler system status information to explanations of final decisions. It differentiates the internal technical aspects for explainability (for auditors) versus the user-facing aspect of transparency, and Recommendations for Documentation Standards for explainability that travel with the model such as model cards, model datasheets. The paper also emphasizes that the checklist is not a one-time box-checking activity, but an integrated process to be woven into agile product workflows and covers detail on how the PM can use it in specific product phases – from Scoping & Definition (set requirements and key thresholds) to Development & Testing (what to validate and how) to Launch & Monitoring (set up for continued operational oversight). Finally, the research also explores the organizational and trade-off issues that PMs will need to navigate and influence with their teams and stakeholders, such as how to advocate for the resources to do responsible AI product development, how to make principled trade-off decisions when two fairness metrics in conflict with one another, or how to make the business case and communicate the value of building trust as a key differentiator, risk mitigation, and long-term competitiveness strategy. In sum, this paper provides a crucial operational framework and tools to fill the ‘values into practice’ gap. It offers product managers an essential management process that breaks down the critical but high-level ethical considerations and abstract principles into specific and actionable steps that can be practically operationalized. It empowers product managers and their teams to build AI-powered products that are not only innovative and commercially viable but also socially responsible, trustworthy, and ultimately sustainable.