AI Governance, Ethics and Algorithmic Accountability in Public Administration Training Course

Course Introduction

Artificial intelligence is increasingly influencing how public institutions deliver services, allocate resources, assess information, manage employees, detect risks, and support administrative decisions. The AI Governance, Ethics and Algorithmic Accountability in Public Administration Training Course provides public-sector professionals with advanced knowledge and practical frameworks for governing AI responsibly while protecting institutional integrity, public trust, individual rights, and administrative accountability.

The programme examines AI governance from the perspective of public administration, where technology operates within complex legal, ethical, institutional, and social environments. Participants will explore how governments can establish clear responsibilities for AI systems, determine appropriate levels of human oversight, document algorithmic processes, assess risks, monitor performance, and create mechanisms through which automated or AI-assisted decisions can be reviewed, challenged, corrected, and audited.

A central theme is algorithmic accountability. Participants will learn how to evaluate whether AI systems produce accurate, fair, explainable, consistent, and defensible outcomes, particularly where automated recommendations or classifications can affect citizens, employees, businesses, or access to public resources. The course addresses algorithmic bias, discrimination, opacity, automation bias, unreliable outputs, data limitations, model drift, and other challenges that can undermine responsible public administration.

The programme also explores practical AI governance structures and institutional controls. Participants will examine AI policies, governance committees, accountability matrices, impact assessments, algorithm registers, risk classifications, audit mechanisms, procurement requirements, documentation standards, incident management, and continuous monitoring. Particular emphasis is placed on creating governance arrangements that are practical enough to support innovation while sufficiently robust to manage high-impact and high-risk applications.

Ethical governance is integrated with privacy, cybersecurity, data governance, transparency, accessibility, public participation, and administrative justice. Participants will consider how governments can communicate AI use responsibly, protect sensitive information, maintain meaningful human involvement, and provide appropriate avenues for review and redress. The course also addresses emerging challenges associated with generative AI, AI agents, synthetic content, autonomous systems, and rapidly evolving algorithmic capabilities.

By completing the course, participants will be able to establish stronger governance and accountability frameworks for AI-enabled public administration. They will gain practical capabilities for identifying ethical and algorithmic risks, establishing institutional controls, assessing AI systems, strengthening transparency, designing human oversight, and building trustworthy AI practices that support innovation while preserving fairness, legality, accountability, and public confidence.

Duration

10 days

Who Should Attend

• Senior public administrators responsible for governance, institutional performance, administrative reform, and technology-enabled modernization.

• Permanent secretaries, directors, commissioners, agency heads, and senior government executives overseeing AI adoption and institutional accountability.

• Chief information officers, chief digital officers, chief technology officers, and government technology leaders responsible for AI governance.

• Chief data officers, data-governance managers, analytics leaders, and information-management professionals overseeing public-sector data and AI systems.

• Legal advisers, regulatory specialists, compliance officers, and policy professionals responsible for interpreting AI-related obligations and institutional requirements.

• Ethics officers, governance specialists, integrity professionals, and public-sector accountability practitioners responsible for responsible AI adoption.

• Internal auditors, external assurance professionals, risk managers, and control specialists evaluating AI systems and administrative processes.

• Privacy and data-protection officers responsible for protecting personal information used by government AI applications.

• Cybersecurity and information-security professionals managing risks associated with AI platforms, models, data, and government systems.

• AI specialists, data scientists, algorithm developers, enterprise architects, and technical teams supporting public-sector AI initiatives.

• Procurement and contract-management professionals acquiring AI technologies, platforms, systems, and implementation services.

• Human-resource and organizational-development leaders managing AI-related workforce changes, professional responsibilities, and institutional capability development.

• Policy analysts, public-service managers, monitoring and evaluation specialists, and programme leaders using AI in government decision and service processes.

• Consultants, development partners, advisers, and trainers supporting responsible AI governance and public-sector institutional transformation.

Course Objectives

• Develop advanced understanding of AI governance, ethics, algorithmic accountability, and their importance to responsible public administration and institutional legitimacy.

• Establish governance frameworks that clearly define AI ownership, accountability, decision rights, oversight responsibilities, risk ownership, and escalation procedures.

• Identify and evaluate ethical risks involving algorithmic bias, discrimination, opacity, privacy, surveillance, automation bias, unreliable outputs, and unequal impacts.

• Develop practical methods for assessing algorithmic systems for fairness, accuracy, reliability, transparency, explainability, robustness, accessibility, and accountability.

• Design effective human-oversight mechanisms that preserve meaningful administrative judgment, review, intervention, escalation, and responsibility throughout AI-supported processes.

• Apply AI impact assessments and risk-classification approaches to determine appropriate governance, monitoring, documentation, and assurance requirements for different use cases.

• Establish algorithmic accountability mechanisms including registers, documentation, audit trails, model records, performance monitoring, review procedures, and accountability matrices.

• Strengthen transparency practices that enable government institutions to explain appropriate aspects of AI use, system purpose, decision processes, limitations, and available safeguards.

• Integrate AI governance with privacy, cybersecurity, data governance, procurement, internal controls, risk management, audit, administrative justice, and institutional compliance.

• Develop responsible procurement and vendor-management requirements that address algorithmic transparency, data ownership, security, performance, auditability, model changes, and accountability.

• Prepare institutions to govern emerging technologies such as generative AI, autonomous AI agents, multimodal systems, synthetic media, and increasingly sophisticated decision-support technologies.

• Build practical AI governance implementation roadmaps that strengthen public trust, protect rights, support innovation, and establish sustainable institutional accountability.

Comprehensive Course Outline

Module 1: Foundations of AI Governance in Public Administration

• Evolution of artificial intelligence in public administration and the growing need for institutional governance, accountability, oversight, and responsible implementation.

• Core concepts of AI governance, algorithmic accountability, responsible AI, ethical technology, administrative responsibility, transparency, and public-sector trust.

• Distinguishing AI used for administrative assistance, decision support, prediction, classification, automation, and high-impact government decision processes.

• Strategic challenges and opportunities involved in governing AI across ministries, departments, agencies, regulatory institutions, municipalities, and public services.

Module 2: Public-Sector AI Governance Frameworks

• Designing institution-wide AI governance frameworks that establish policies, responsibilities, oversight structures, approval processes, and accountability requirements.

• Establishing AI governance committees and multidisciplinary structures connecting executive leadership, legal, technical, data, risk, ethics, procurement, and operational functions.

• Developing AI policies, standards, procedures, guidance, control requirements, documentation rules, and escalation mechanisms for government institutions.

• Integrating AI governance into existing enterprise governance, risk management, internal controls, audit, compliance, strategic planning, and institutional accountability frameworks.

Module 3: Algorithmic Accountability and Administrative Responsibility

• Understanding algorithmic accountability and how public institutions remain responsible for decisions supported, recommended, or influenced by AI systems.

• Defining accountability across AI lifecycles, including system design, procurement, deployment, configuration, operation, monitoring, maintenance, and retirement.

• Developing accountability matrices that identify system owners, business owners, technical teams, risk owners, oversight bodies, and executive decision-makers.

• Establishing mechanisms for documenting decisions, investigating incidents, assigning responsibility, correcting errors, and demonstrating institutional accountability.

Module 4: AI Ethics and Public Values

• Applying ethical principles such as fairness, dignity, autonomy, proportionality, transparency, inclusion, accountability, safety, and public benefit to government AI systems.

• Examining ethical challenges arising when AI systems influence eligibility, prioritization, enforcement, resource allocation, recruitment, inspection, or public-service decisions.

• Balancing innovation and administrative efficiency with individual rights, procedural fairness, public interest, institutional responsibility, and societal consequences.

• Developing practical ethical review processes that enable government institutions to identify concerns early and incorporate responsible safeguards into AI initiatives.

Module 5: Algorithmic Bias, Fairness and Discrimination

• Understanding sources of algorithmic bias arising from data, model design, historical inequalities, proxy variables, sampling problems, human decisions, and deployment environments.

• Applying fairness assessment methods to identify potentially unequal outcomes across population groups, geographic areas, socioeconomic contexts, or service categories.

• Designing mitigation strategies involving data improvement, model selection, threshold adjustments, human review, monitoring, testing, and governance interventions.

• Establishing continuous fairness monitoring to detect changing impacts, unintended discrimination, data shifts, model drift, and emerging disparities after deployment.

Module 6: Transparency, Explainability and Interpretability

• Understanding different levels of AI transparency and determining what information should be communicated to executives, employees, affected individuals, oversight bodies, and the public.

• Applying explainability and interpretability approaches appropriate to different AI models, administrative contexts, decision risks, and stakeholder information needs.

• Developing documentation that explains system purpose, data sources, intended uses, limitations, performance characteristics, human controls, and significant risks.

• Establishing transparent communication practices that avoid misleading claims while providing meaningful information about AI-supported administrative processes.

Module 7: Human Oversight and Administrative Decision-Making

• Designing human-in-the-loop, human-on-the-loop, and human-led decision models appropriate to the risk and significance of different government AI applications.

• Determining when government officials should review, challenge, override, validate, or reject AI recommendations before administrative action is taken.

• Managing automation bias and overreliance by developing verification procedures, decision prompts, escalation mechanisms, independent review, and professional accountability.

• Establishing human oversight standards for high-impact decisions involving citizens, employees, benefits, enforcement, licensing, regulation, and access to public resources.

Module 8: AI Impact Assessment and Risk Classification

• Conducting structured AI impact assessments covering purpose, affected stakeholders, data, technical characteristics, risks, benefits, governance, and potential societal consequences.

• Developing risk-classification systems that distinguish low-impact administrative applications from higher-risk systems requiring stronger safeguards and oversight.

• Establishing risk registers, control plans, mitigation measures, approval gates, monitoring requirements, and escalation procedures based on AI system characteristics.

• Integrating AI impact assessments into project approval, procurement, implementation, deployment, monitoring, major system changes, and periodic institutional review.

Module 9: Data Governance, Privacy and Algorithmic Accountability

• Establishing data governance practices that ensure information used by AI systems is accurate, relevant, appropriately sourced, secure, governed, and suitable for intended purposes.

• Addressing privacy risks associated with personal information, sensitive records, employee data, citizen information, surveillance technologies, and automated profiling.

• Applying privacy-by-design and data-minimization principles to government AI systems throughout development, deployment, operation, and retirement.

• Establishing controls for data access, retention, classification, sharing, provenance, consent where applicable, security, correction, and responsible secondary use.

Module 10: AI Assurance, Auditing and Monitoring

• Designing AI assurance frameworks combining technical testing, ethical review, governance assessment, cybersecurity validation, privacy evaluation, and operational performance monitoring.

• Developing algorithm registers, model documentation, audit trails, system inventories, performance records, impact assessments, and evidence repositories for accountable AI management.

• Conducting algorithmic audits to assess accuracy, fairness, robustness, explainability, security, compliance, data quality, and alignment with intended use.

• Establishing continuous monitoring for model drift, changing data, performance degradation, unexpected impacts, incidents, user behavior, and emerging governance risks.

Module 11: Responsible AI Procurement and Vendor Accountability

• Translating responsible AI governance requirements into procurement specifications covering transparency, security, fairness, performance, explainability, privacy, and auditability.

• Evaluating vendors and AI systems according to technical capability, governance maturity, data practices, model limitations, security controls, and accountability arrangements.

• Establishing contractual provisions for data ownership, intellectual property, audit rights, model updates, incident reporting, performance standards, security, and service continuity.

• Managing third-party algorithmic risks through due diligence, supplier monitoring, independent testing, contractual controls, performance reviews, and practical exit strategies.

Module 12: AI Incident Management, Redress and Administrative Justice

• Developing procedures for identifying, reporting, investigating, escalating, documenting, and responding to AI-related errors, failures, incidents, and harmful outcomes.

• Establishing accessible review, complaint, appeal, correction, and redress mechanisms for individuals affected by AI-supported administrative decisions.

• Designing incident-response frameworks that identify root causes, assess impacts, apply corrective actions, communicate appropriately, and prevent recurrence.

• Integrating AI incident management with existing government complaint systems, internal investigations, administrative review, audit, legal processes, and public accountability mechanisms.

Module 13: Generative AI and Emerging Governance Challenges

• Governing generative AI applications involving automated drafting, content creation, research, summarization, knowledge retrieval, coding, communication, and administrative assistance.

• Addressing hallucinations, fabricated sources, prompt injection, confidential-data exposure, intellectual-property concerns, misinformation, synthetic content, and inappropriate reliance.

• Developing institutional policies for generative AI use that define permitted activities, prohibited applications, verification requirements, information-security controls, and employee responsibilities.

• Examining emerging governance challenges associated with AI agents, autonomous workflows, multimodal systems, synthetic data, and increasingly capable foundation models.

Module 14: AI Governance, Cybersecurity and System Resilience

• Understanding cybersecurity threats affecting AI systems, including data poisoning, model manipulation, adversarial inputs, prompt attacks, unauthorized access, and information leakage.

• Integrating cybersecurity controls into AI governance through identity management, access controls, encryption, monitoring, testing, secure development, and incident response.

• Establishing resilience arrangements for AI system failures, model errors, infrastructure outages, data-quality problems, supplier disruptions, and technology dependencies.

• Developing continuity and fallback mechanisms that ensure critical government services remain functional when AI systems become unavailable, unreliable, or unsafe.

Module 15: Governance of Emerging and High-Impact AI Systems

• Assessing governance implications of predictive policing, automated eligibility assessment, biometric technologies, algorithmic inspections, fraud detection, and other high-impact applications.

• Developing proportional governance approaches that match oversight intensity, transparency, human review, assurance, and monitoring with potential impact and risk.

• Examining emerging issues involving autonomous AI agents, AI-enabled surveillance, synthetic media, deepfakes, automated persuasion, digital identity, and algorithmic influence.

• Developing institutional foresight capabilities to anticipate new ethical, legal, social, technological, and administrative risks created by rapidly advancing AI capabilities.

Module 16: Integrated AI Governance and Accountability Framework

• Developing an institution-specific AI governance framework integrating strategy, ethics, accountability, risk management, privacy, cybersecurity, data governance, procurement, and assurance.

• Creating an AI accountability roadmap covering governance structures, policies, risk classifications, impact assessments, monitoring, auditing, incident management, and public communication.

• Designing executive governance dashboards that track AI systems, risk exposure, assurance findings, incidents, performance, compliance, responsible-use indicators, and corrective actions.

• Presenting a practical capstone framework demonstrating how public institutions can govern AI responsibly while supporting innovation, protecting rights, strengthening accountability, and maintaining public trust.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.