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<title>Software Engineering at Oxford | AI Governance </title>

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<table cellpadding="0" cellspacing="0" border="0"><tr><td style="width: 75px"><div style="width: 75px;height: 75px"><div style="width: 55px;height: 55px; margin: 10px;-moz-box-shadow: 0px 5px 5px rgba(0,0,0,0.3), -2px 1px 5px rgba(0,0,0,0.3), 2px 1px 5px rgba(0,0,0,0.3); -webkit-box-shadow: 0px 5px 5px rgba(0,0,0,0.3), -2px 1px 5px rgba(0,0,0,0.3), 2px 1px 5px rgba(0,0,0,0.3); box-shadow: 0px 5px 5px rgba(0,0,0,0.3), -2px 1px 5px rgba(0,0,0,0.3), 2px 1px 5px rgba(0,0,0,0.3);-moz-border-radius: 8px;-webkit-border-radius: 8px; border-radius: 8px;" id="CMSBg"><div style="font-size: 14px; font-weight: bold; text-align:center; position: relative; top: 20px; background-color: transparent; color: white"> </div></div></div></td><td style="vertical-align:middle"><h1>AI Governance</h1></td></tr></table>
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	<h4>Summary </h4>
	<p><p>Artificial Intelligence is reshaping how organisations operate and transforming the nature of work. This module critically examines the ethical, organisational, and societal implications of AI adoption. It explores the opportunities and challenges of automation and intelligent systems, with particular attention to the risks and failure modes of AI, their underlying causes, and strategies for prevention and mitigation. The course also investigates how AI intersects with fundamental rights—such as privacy, fairness, and accountability—and surveys the evolving regulatory and governance frameworks designed to promote responsible AI development. Finally, it addresses the impact of AI on the labour market, offering insights into how organisations can adapt and manage the workforce in an AI-enabled economy.</p>
<h4>Objectives</h4>
<p><p>By the end of the module, students will be able to:</p>
<ul>
<li>Explain why governance is foundational to responsible AI adoption.</li>
<li>Identify key risks and failure modes of AI systems and evaluate mitigation strategies.</li>
<li>Critically assess global regulatory approaches to governing AI.
</li>
<li>Analyse sources of algorithmic bias and apply strategies for mitigation.
</li>

<li>Understand and compare different approaches to AI governance within organisations.</li>

<li>Evaluate the labour market shifts driven by AI and propose strategies for workforce management in AI-enabled organisations.</li>

</ul></p>
<h4>Contents</h4>
<p><ul>
<li>Philosophical and Ethical Foundations: Key failure modes of AI systems. Core ethical theories (deontology consequentialism and virtue ethics) applied to AI</li>
<li>Principles of Responsible AI: Fairness, non-discrimination, transparency, accountability, justice. Representational and allocative harms in algorithmic systems.</li>
<li>Algorithmic Bias and Mitigation: Origins and types of bias in data and models. Real-world manifestations of bias. Technical vs. non-technical mitigation strategies and their limitations.</li>
<li>Privacy in the Age of AI. Types of data and related privacy concerns. Practical and legal mechanisms for data protection. Emerging legal challenges in AI-driven data ecosystems.</li>
<li>Regulatory Frameworks and Global Policy: Key frameworks: EU AI Act, NIST, OECD, and ISO. Trends in global AI policy and governance.</li>
<li>AI Governance within Organisations. Structures and processes for responsible AI adoption. Risk management, auditability, and accountability mechanisms.</li>
<li>AI and the Future of Work: Structural shifts in labour demand and task automation. Skills, roles, and organisational strategies in AI-enabled workplaces.</li>
</ul></p>
<h4>Requirements</h4>
<p>A good understanding of key AI methods. </p>

	
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