Responsible AI Governance and Algorithmic Accountability in Evaluation Training Course

Course Introduction

Artificial intelligence is increasingly influencing how organizations design programmes, allocate resources, assess performance, and evaluate public policies and development interventions. While AI can improve analytical efficiency and expand access to evidence, its use also creates important questions about fairness, transparency, accountability, privacy, and the reliability of automated decisions. The Responsible AI Governance and Algorithmic Accountability in Evaluation Training Course equips participants with the knowledge and practical skills needed to assess AI systems critically, establish effective governance arrangements, and ensure that AI-supported evaluation remains ethical, transparent, inclusive, and aligned with institutional responsibilities.

Monitoring and evaluation professionals are increasingly expected to assess not only whether programmes achieve their intended outcomes but also whether the technologies supporting those programmes operate fairly and responsibly. AI systems may influence beneficiary targeting, eligibility assessments, resource allocation, fraud detection, service prioritization, and performance measurement. If these systems rely on incomplete data, biased assumptions, or inappropriate decision rules, they may disadvantage particular populations or produce misleading conclusions. Participants will learn how to identify algorithmic risks, examine the assumptions behind AI-supported processes, and incorporate governance and accountability considerations into evaluation frameworks and assessment criteria.

The course introduces the foundations of responsible AI governance, including organizational accountability, risk-based oversight, algorithmic transparency, explainability, fairness, privacy protection, human supervision, and lifecycle management. Participants will explore how governance responsibilities can be distributed across technology providers, system developers, institutional managers, data owners, evaluators, and decision-makers. The training also examines practical governance instruments such as AI inventories, impact assessments, risk registers, model documentation, audit trails, review protocols, and incident reporting procedures. These tools help organizations move from broad ethical principles to operational controls that can be implemented, monitored, and evaluated.

A central component of the programme is algorithmic accountability: the ability to establish who is responsible for an AI-supported process, how it operates, what evidence supports its outputs, and what mechanisms exist to challenge or correct harmful results. Participants will examine approaches to assessing data quality, representativeness, disparate outcomes, model performance, and the adequacy of human review. They will learn how to evaluate AI systems throughout their lifecycle, from initial procurement and design through deployment, monitoring, modification, and retirement. Particular attention will be given to the difference between technical performance and social outcomes, since an accurate model may still produce unfair or unacceptable consequences in a specific institutional context.

Responsible AI governance also requires meaningful engagement with the people and communities affected by automated or AI-assisted decisions. Evaluation professionals must consider whether affected groups understand how systems influence decisions, whether they can question outcomes, and whether grievance mechanisms are accessible and effective. The course explores participatory evaluation, stakeholder consultation, accessibility, cultural and linguistic inclusion, and safeguards for vulnerable populations. Participants will also examine emerging challenges associated with generative AI, autonomous agents, opaque third-party models, synthetic data, and rapidly changing AI services. These topics are considered through the practical lens of accountability, evidence quality, institutional readiness, and public trust.

By the end of the course, participants will be prepared to assess responsible AI practices, develop governance recommendations, and integrate algorithmic accountability into monitoring, evaluation, research, and institutional oversight. They will be able to formulate evaluation questions about fairness and transparency, select appropriate indicators, document risks, review mitigation measures, and communicate findings to technical and nontechnical stakeholders. The programme emphasizes evidence-based governance rather than compliance as a purely administrative exercise. Participants will leave with a practical framework for strengthening accountability, improving institutional AI oversight, and ensuring that technology-supported programmes deliver credible, equitable, and demonstrable value.

Duration

5 days

Who Should Attend

• Monitoring and evaluation professionals responsible for assessing AI-supported programmes, services, and institutional interventions.

• Monitoring, evaluation, accountability, and learning (MEAL) specialists seeking to incorporate responsible AI criteria into evaluation frameworks.

• AI governance officers and responsible technology specialists developing institutional policies and oversight procedures.

• Public-sector officials responsible for digital transformation, algorithmic decision-making, and accountable service delivery.

• Policy analysts evaluating the social, economic, and institutional implications of AI adoption.

• Data protection, privacy, and information governance professionals overseeing responsible data use.

• Risk management and compliance specialists assessing AI-related operational, legal, ethical, and reputational risks.

• Internal auditors and assurance professionals reviewing AI controls, documentation, and accountability mechanisms.

• Data scientists and AI developers seeking to strengthen fairness testing, documentation, and human oversight practices.

• Programme managers overseeing technology-enabled projects and AI-supported beneficiary or resource allocation systems.

• Development practitioners working with government agencies, NGOs, donors, and international organizations.

• Research officers conducting studies on algorithmic fairness, digital inclusion, and the societal impacts of AI.

• Procurement and contract management professionals evaluating AI vendors, service providers, and third-party system risks.

• Human rights, inclusion, and safeguarding specialists examining the effects of automated systems on affected populations.

• Institutional leaders responsible for AI strategy, governance arrangements, accountability, and organizational risk oversight.

Course Objectives

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

• Explain the core principles of responsible AI governance and algorithmic accountability, including fairness, transparency, privacy, explainability, safety, and clearly assigned institutional responsibility.

• Identify algorithmic risks across the AI lifecycle and assess how data limitations, model design, deployment context, and organizational practices can influence evaluation findings and outcomes.

• Develop evaluation questions, criteria, and indicators that examine the fairness, accountability, transparency, inclusiveness, and effectiveness of AI-supported programmes and decision-making processes.

• Apply risk-based AI governance approaches to classify systems according to their intended use, affected populations, potential harms, operational significance, and required oversight measures.

• Assess data quality, representativeness, documentation, and potential sources of bias that may affect AI system performance across different demographic, geographic, linguistic, or socioeconomic groups.

• Examine appropriate methods for assessing algorithmic fairness, including group-level performance comparisons, error analysis, outcome disparities, and contextual interpretation of quantitative fairness measures.

• Evaluate transparency and explainability practices by reviewing system documentation, decision records, model limitations, user disclosures, and the accessibility of explanations for affected stakeholders.

• Design accountability mechanisms that define responsibilities, maintain audit trails, establish human review procedures, and enable affected individuals to question or appeal AI-supported decisions.

• Integrate privacy protection, stakeholder participation, accessibility, and human rights considerations into AI impact assessments, evaluation methodologies, and organizational governance processes.

• Develop an actionable AI governance improvement plan with prioritized recommendations, risk controls, monitoring indicators, reporting arrangements, and procedures for continuous review.

Comprehensive Course Outline

Module 1: Foundations of Responsible AI Governance and Algorithmic Accountability

• Understanding responsible AI principles, including fairness, transparency, privacy, reliability, safety, accountability, and respect for human rights in organizational and public-sector settings.

• Distinguishing AI governance from general data governance, cybersecurity, model validation, ethical guidance, and conventional compliance management.

• Examining how AI-supported decisions affect programme eligibility, resource allocation, public services, performance assessment, and evaluation conclusions.

• Identifying the roles of developers, vendors, data owners, institutional leaders, evaluators, regulators, and affected communities in maintaining accountable AI practices.

Module 2: AI Governance Frameworks, Policies, and Regulatory Contexts

• Comparing international AI governance approaches, risk-based frameworks, organizational standards, and emerging regulatory requirements relevant to responsible system deployment.

• Translating high-level ethical principles into operational policies, internal controls, approval processes, reporting responsibilities, and measurable governance requirements.

• Developing AI inventories, system classifications, ownership registers, documentation standards, and review schedules to improve institutional visibility and oversight.

• Assessing how legal obligations, sector-specific requirements, contractual terms, and institutional policies influence the governance of AI-supported evaluation activities.

Module 3: Algorithmic Risk Assessment and AI Impact Evaluation

• Identifying potential harms arising from AI use, including discriminatory outcomes, inaccurate classifications, privacy violations, exclusion, automation bias, and reduced access to services.

• Conducting context-sensitive AI impact assessments that examine intended uses, affected stakeholders, decision consequences, data dependencies, and available safeguards.

• Building algorithmic risk registers that document likelihood, severity, exposure, existing controls, residual risks, responsible owners, and mitigation deadlines.

• Prioritizing oversight according to system purpose, scale, sensitivity, degree of autonomy, reversibility of decisions, and potential consequences for affected individuals.

Module 4: Data Governance, Quality, Representativeness, and Bias

• Evaluating data provenance, completeness, accuracy, relevance, timeliness, labeling quality, and representativeness before relying on data for AI-supported analysis.

• Identifying historical bias, sampling gaps, proxy variables, measurement errors, missing populations, and institutional practices that may reproduce unequal outcomes.

• Assessing how linguistic differences, cultural contexts, disability, geography, and socioeconomic conditions can affect data coverage and algorithmic performance.

• Establishing data documentation, access controls, retention policies, quality assurance checks, and corrective procedures to improve the reliability of AI-supported evaluation.

Module 5: Algorithmic Fairness, Explainability, and Transparency

• Exploring fairness concepts and quantitative measures, including group-level error comparisons, selection rates, calibration, and the limitations of comparing fairness metrics.

• Evaluating whether explanations of AI-supported outputs are accurate, understandable, relevant to users, and appropriate for the decisions or risks involved.

• Reviewing model documentation, system cards, decision logs, known limitations, user disclosures, and other records that support transparency and informed oversight.

• Interpreting technical fairness results alongside institutional context, stakeholder experiences, decision consequences, and applicable ethical or legal requirements.

Module 6: Accountability Mechanisms, Human Oversight, and Redress

• Defining decision ownership, approval authority, escalation routes, and accountability boundaries across AI development, procurement, deployment, and evaluation.

• Designing meaningful human oversight processes that allow qualified reviewers to question outputs, correct errors, override recommendations, and document their decisions.

• Establishing accessible complaint, appeal, correction, and redress mechanisms for individuals or communities adversely affected by AI-supported decisions.

• Maintaining audit trails, decision records, incident reports, and review documentation that support independent scrutiny and continuous institutional learning.

Module 7: Integrating Responsible AI into Monitoring and Evaluation

• Developing evaluation questions and assessment criteria that examine whether AI-supported programmes are relevant, effective, equitable, transparent, and accountable.

• Selecting indicators to monitor system performance, disparate outcomes, complaint resolution, human intervention, stakeholder trust, and the effectiveness of mitigation measures.

• Applying mixed-methods evaluation to combine model performance data, administrative records, user feedback, interviews, case studies, and contextual evidence.

• Distinguishing technical accuracy from real-world effectiveness by assessing unintended consequences, distributional effects, implementation conditions, and changes over time.

Module 8: Privacy, Security, Human Rights, and Stakeholder Participation

• Applying privacy-by-design principles, data minimization, purpose limitation, access controls, and appropriate safeguards to AI-supported research and evaluation processes.

• Examining human rights and inclusion considerations when AI affects access to employment, education, healthcare, financial services, public benefits, or other essential opportunities.

• Designing participatory approaches that involve affected communities, frontline staff, domain experts, and civil society in assessing AI system impacts and safeguards.

• Evaluating whether communication, consent arrangements, explanations, and grievance procedures are accessible to people with different language, literacy, and accessibility needs.

Module 9: Emerging Governance Challenges in Generative AI and Autonomous Systems

• Assessing risks associated with generative AI, including hallucinations, fabricated references, inconsistent outputs, information leakage, and difficulty tracing generated conclusions.

• Examining autonomous AI agents and multi-step workflows for permission management, human approval gates, operational monitoring, and accountability when actions span multiple systems.

• Evaluating third-party AI services, opaque models, model updates, vendor dependencies, and contractual controls that influence institutional oversight and accountability.

• Exploring emerging practices in continuous AI monitoring, algorithmic auditing, synthetic data governance, incident reporting, and independent assurance of deployed systems.

Module 10: Governance Implementation, Auditing, and Continuous Improvement

• Conducting a practical review of an AI system or use case to identify governance gaps, assess risks, examine documentation, and formulate evidence-based recommendations.

• Designing AI audit and assurance plans that define scope, criteria, evidence requirements, testing procedures, reporting arrangements, and follow-up responsibilities.

• Establishing performance dashboards and governance indicators for fairness, reliability, transparency, incident resolution, human oversight, and implementation progress.

• Preparing an institutional improvement roadmap with prioritized actions, assigned responsibilities, capacity-building requirements, review timelines, and mechanisms for periodic reassessment.

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.