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<h1>COMP2261 - Bias in AI Module</h1>
</header>
<h2>Location</h2>
<p>The physical classes for this module are going to be as follows:</p>
<ul>
<li>Fridays: 09:00 - 10:00 (effective hours from 09:05 to 09:50)</li>
<ul>
<li><a href="https://www.dur.ac.uk/encore/enabledrooms/">Physical Location:</a>TBC</li>
<li><a href="">Zoom Broadcast (only accessible for Durham University accounts):</a>URL</li>
</li>
</ul>
</ul>
<h2>Structure</h2>
<p>For each lecture session, you should read a paper (as listed below). I will use this paper as the basis of
the discussions in the session. I know reading a paper can be boring and tedious sometime but here is an
instruction (I guess it is rather trivial) on <a href="./reading/security/0 - How to Read Paper.pdf">how to
read a paper.</a></p>
<p>The overall summary and learning objectives of the module is available in <a
href="https://duracuk-lb01-production.terminalfour.net/study/modules/undergraduate/comp2261.php">here</a>.
</p>
<div class="table-wrapper">
<table class="alt">
<thead>
<tr>
<th> Date </th>
<th> Session Topic </th>
<th> Reading </th>
<th> Additional sources </th>
</tr>
</thead>
<tbody>
<tr>
<td> 1 </td>
<td> Discrimination in law and society </td>
<td> <a href="./reading/bias/1 - The impact of AI on business and society _ Financial Times.pdf">The
impact of AI on business and society (2020)</a> </td>
<td> <a href="./reading/bias/1 - Review into bias in algorithmic decision-making.pdf">Independent UK
government report into bias in algorithmic decision-making (2020)</a></td>
</tr>
<tr>
<td> 2 </td>
<td> Trust issues of Artificial Intelligence </td>
<td> <a
href="./reading/bias/2 - The relationship between trust in AI and trustworthy machine learning technologies.pdf">The
relationship between trust in AI and trustworthy machine learning technologies (2020)</a>
</td>
<td> <a href="./reading/bias/2 - Mozilla Creating Trustworthy AI.pdf">Creating Trustworthy AI
(2020)</a> </td>
</tr>
<tr>
<td> 3 </td>
<td> Sources and causes of machine bias </td>
<td> <a href="./reading/bias/3 - A Survey on Bias and Fairness in Machine Learning.pdf">A Survey on
Bias and Fairness in Machine Learning (2021) </a></td>
<td> <a
href="./reading/bias/3 - A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle.pdf">A
Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle
(2021) </a></td>
</tr>
<tr>
<td> </td>
<td> Coursework release </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td> 4 </td>
<td> Mathematics of fairness </td>
<td> <a href="./reading/bias/4 - On Formalizing Fairness in Prediction with Machine Learning.pdf">On
Formalizing Fairness in Prediction with Machine Learning (2018) </a></td>
<td> </td>
</tr>
<tr>
<td> 5 </td>
<td> Mathematics of fairness (cont.) </td>
<td> <a
href="./reading/bias/5 - On the apparent conflict between individual and group fairness.pdf">On
the apparent conflict between individual and group fairness (2020) </a></td>
<td> <a
href="./reading/bias/5 - Machine learning fairness notions Bridging the gap with real-world applications.pdf">Machine
learning fairness notions: Bridging the gap with real-world applications (2021)</a></td>
</tr>
<tr>
<td> 6 </td>
<td> Fair machine learning + pre-processing methods </td>
<td> <a
href="./reading/bias/6 - Data preprocessing techniques for classification without discrimination.pdf">Data
preprocessing techniques for classification without discrimination (2012) </a></td>
<td> </td>
</tr>
<tr>
<td> 7 </td>
<td> inProcessing methods </td>
<td> <a
href="./reading/bias/7 - Fairness-aware Classifier with Prejudice Remover Regularizer.pdf">Fairness-aware
Classifier with Prejudice Remover Regularizer (2012)</a> </td>
<td> <a
href="./reading/bias/7 - Learning Adversarially Fair and Transferable Representations.pdf">Learning
Adversarially Fair and Transferable Representations (2018)</a> </td>
</tr>
<tr>
<td> 8 </td>
<td> Post-processing methods </td>
<td> <a href="./reading/bias/8 - Equality of Opportunity in Supervised Learning.pdf">Equality of
Opportunity in Supervised Learning (2016)</a> </td>
<td> </td>
</tr>
<tr>
<td> 9 </td>
<td> Counterfactual fairness </td>
<td> <a href="./reading/bias/9 - Counterfactual Fairness.pdf">Counterfactual Fairness (2017)</a> </td>
<td> </td>
</tr>
<tr>
<td> 10 </td>
<td> Fairness mitigation in action </td>
<td> <a
href="./reading/bias/10 - Addressing bias in big data and AIfor health care A call for open science.pdf">Addressing
bias in big data and AI for health care: A call for open science (2021)</a> </td>
<td> <a href="./reading/bias/10 - Delayed Impact of Fair Machine Learning.pdf">Delayed Impact of Fair
Machine Learning (2018)</a> </td>
</tr>
</tbody>
</table>
<h2>Book Recommendations</h2>
<p>Our discussion will be guided by papers, monographs, and lecture notes that are available online.
Recommended (online) textbook: Barocas, Solon, Moritz Hardt, and Arvind Narayanan. Fairness and Machine
Learning , 2018, Available online: <a href="https://fairmlbook.org/">https://fairmlbook.org/</a>.
</p>
<h2>Time Management</h2>
<p>TBC</p>
<h2>Coursework</h2>
<p>This submodule is assessed by a piece of summative coursework.</p>
</section>
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</section>
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