Cambridge
Academy
In London

Introduction

As OD teams increasingly use AI for surveys, listening systems, talent insights, and change enablement, they must ensure ethical use, protect privacy, prevent bias, and maintain trust with employees and stakeholders. This practical program equips OD leaders with governance frameworks, risk controls, and implementation tools to use AI responsibly across OD diagnostics, workforce analytics, and intervention design.

 Course Objectives

By the end of this course, participants will be able to:

·        Understand responsible AI principles and how they apply to OD and people-related decisions

·        Protect privacy and confidentiality in employee data and AI-enabled listening systems

·        Identify, assess, and mitigate bias risks in OD analytics and AI-driven recommendations

·        Establish governance, controls, and human oversight for AI use in OD workflows

·        Design transparent communication and consent practices to sustain employee trust

·        Build a responsible AI roadmap for OD, including policies, monitoring, and audit readiness

Target Audience

This course is designed for:

·        OD leads, HR/OD business partners, and organizational effectiveness professionals

·        People analytics, HRIS, and employee listening leaders

·        Change management and culture transformation leaders using AI-enabled insights

·        Compliance, risk, legal, and internal audit professionals supporting HR/OD governance

·        Leaders involved in workforce planning, talent, and engagement programs

Course Outlines

Day 1: Responsible AI Foundations for OD & Trust Principles

·        Where AI is used in OD: listening, analytics, workforce planning, and intervention support

·        Responsible AI principles: fairness, transparency, accountability, and safety

·        OD-specific risks: surveillance perception, misinterpretation, and over-automation

·        Stakeholder expectations: employees, leadership, unions/works councils, regulators

·        Activity: OD AI use-case inventory + risk/benefit assessment

Day 2: Privacy, Consent & Confidentiality in People Data

·        Employee data sensitivity: what counts as personal and sensitive data in OD contexts

·        Privacy-by-design: minimization, purpose limitation, retention, and access controls

·        Consent and transparency: notice, opt-in/opt-out considerations, and trust-building

·        Secure handling: anonymization/pseudonymization concepts, aggregation, and safe reporting

·        Workshop: Create a privacy and confidentiality checklist for an AI-enabled listening program

Day 3: Bias Risks, Fairness Testing & Mitigation Methods

·        Common bias sources: sampling bias, measurement bias, labeling bias, and historical inequities

·        Fairness concepts for OD: representation, disparate impact, and subgroup analysis

·        Testing and validation: drift, false positives, and “spurious correlations” in people insights

·        Mitigation strategies: data improvements, constraints, human review, and policy guardrails

·        Practical activity: Bias risk assessment for an OD use case + mitigation plan and review gates

Day 4: Governance, Controls & Human-in-the-Loop Operating Model

·        Governance design: roles, decision rights, approvals, and escalation paths

·        Controls and assurance: documentation, audit trails, validation protocols, and evidence retention

·        Vendor and tool governance: third-party risk, contracts, and security requirements

·        Human-in-the-loop: when AI recommends vs. decides; accountability for outcomes

·        Case study: Governance response to an AI-related OD incident (privacy complaint or biased insight)

Day 5: Communication, Monitoring & Responsible AI Roadmap for OD

·        Responsible communication: explaining AI use, limitations, and safeguards to employees

·        Monitoring plan: quality, bias drift, privacy incidents, and user feedback loops

·        Measurement: trust indicators, adoption, value delivered, and risk reduction metrics

·        Implementation roadmap: pilots, training, policies, and governance cadence