Training Course on Ethics and Fairness in the Use of Artificial Intelligence in Recruitment - Virtual Learning
Course Objectives
Introduce participants to ethical considerations in AI-driven recruitment.
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Enable understanding of algorithmic bias and ways to detect and correct it.
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Develop skills to evaluate AI systems in HR.
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Build internal policies for responsible and fair AI use.
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Promote transparency in candidate decision-making.
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Improve HR professionals’ ability to protect applicant data.
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Train participants on tools and mechanisms to ensure digital fairness throughout recruitment.
Target Audience
Target Audience
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HR and Organizational Development Managers
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Recruitment and Talent Acquisition Officers
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Data Science and AI Analysts
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HR and Digital Transformation Consultants
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Professionals interested in digital governance and data protection
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Compliance and Quality Officers
Importance of Ethics in AI Recruitment
AI in recruitment offers numerous benefits but can lead to unfair decisions if applied incorrectly. AI systems learn from historical data that may include inherited biases. Without correction, these systems can replicate biases, such as favoring certain genders or educational backgrounds. Organizations must adopt clear methodologies based on:
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Fairness and non-discrimination
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Transparency in AI decisions
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Candidate privacy protection
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Accountability for errors
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Effective data and algorithm governance
These principles are increasingly mandated by international regulations and impact organizational reputation and candidate trust.
Course Outline
Introduction to AI in Recruitment
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Evolution of AI in HR
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Modern applications: ATS, behavioral assessment tools, video analysis, predictive models
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Impact on speed and accuracy of candidate selection
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Ethics in Digital Systems
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Difference between human and digital ethics
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Core principles: fairness, transparency, privacy, accountability
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Importance of a clear ethical framework
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Challenges of Bias and Its Types
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Data bias: learning from unfair history
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Algorithmic bias: flaws in model design
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Interpretation bias: unintelligible outputs
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Global examples of bias leading to system withdrawal
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Measuring Fairness in Algorithms
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Statistical fairness metrics
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Outcome analysis and group comparison
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Bias detection tests
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Data rebalancing techniques
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Transparency and Explaining AI Decisions
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Why candidates must understand AI decisions
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Explanation techniques: LIME, SHAP
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Practical applications for HR and candidates
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Data Protection and Legal Compliance
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GDPR and other data protection laws
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Applicant data challenges
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Data retention and deletion policies
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Risks of unnecessary data collection
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Designing Ethical AI Policies
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Writing internal policy documents
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Defining roles and responsibilities
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Auditing and continuous monitoring of algorithms
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Global governance models
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Practical Applications and Case Studies
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Rebuilding fair recruitment systems
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Analyzing biased scenarios and solutions
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Comparing traditional vs. AI recruitment systems
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Creating remediation plans for biased models
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Learning Outcomes
By the end of the course, participants will be able to:
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Evaluate AI recruitment systems ethically
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Reduce bias and enhance fairness in recruitment stages
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Design policies governing AI usage
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Explain AI decisions transparently
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Protect applicant data and ensure compliance
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Build candidate trust in digital recruitment systems
This course and other professional programs are available at Dubai Premier Training Centre.
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Virtual Schedule
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