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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>CS281: Ethics of Artificial Intelligence -- Stanford University </title>
<!-- bootstrap -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css">
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<link rel="stylesheet" type="text/css" href="style.css"/>
</head>
<div id="header">
<a href="http://stanford.edu/">
<img src="img/stanfordlogo.jpg" style="height:50px; float: right; margin-right: 20px;">
</a>
<h1><a href="https://stanfordaiethics.github.io/" style="color: black">Ethics of Artificial Intelligence</a></h1>
<div class='text-center'>
<h4>CS281 - Spring 2024</h4>
</div>
<hr/>
</div>
<div class="container" style="align-content: center">
<!-- <h2>Detailed Syllabus</h2>
<br>-->
<!-- <table id="schedule" class="center table table-bordered no-more-tables table-hover">-->
<table id="schedule" class="table table-bordered no-more-tables table-hover" style="align-self: center">
<thead class="active" style="background-color:#f9f9f9">
<th class="text-center">Week</th>
<th class="text-center">Date</th>
<th class="text-center">Lecture Topics</th>
<th class="text-center">Location</th>
<th class="text-center">Assigned</th>
<th class="text-center">Due (1:00 pm on the given day) </th>
</thead>
<tbody>
<tr>
<td>1</td>
<td align="center">
Apr 1
<br>
Apr 3
</td>
<td align="center">
Introduction [<a href="https://drive.google.com/file/d/1CF-nbObHZrE_Ob5Ve2RLxNMjsFkeGYi8/view?usp=sharing ">slides</a>]
<br>
<nobr>
Operationalized Fairness Criteria [<a href="https://drive.google.com/file/d/1l2kuemtM0ALwa6RKfEppA3D6GJXrpo-e/view?usp=sharing">slides</a>, <a href="https://drive.google.com/file/d/1tbC-Zmx2mCHOrq2dYSNHqQfeV6Xhl0Ba/view?usp=sharing"> slides-a </a>]
</nobr>
</td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center">
<a href="https://github.com/stanfordaiethics/hw1_public"> HW 1 release (Apr 3)</a>
</td>
<td align="center"></td>
</tr>
<tr>
<td>2</td>
<td align="center"> Apr 8 <br>Apr 10</td>
<td align="center">
<nobr>
NO CLASS
</nobr>
<br>
Calibration & Fairness [<a href="https://drive.google.com/file/d/1QnHSnuy6gl0vp3mLOjsH9U7gjVypy8s9/view?usp=sharing">slides-a</a>], Learning Group "Fair Models" [<a href="https://drive.google.com/file/d/114QisgklmvTIf5bK3QJC9B3FdIqfDm4j/view?usp=sharing">slides-a</a>]
</td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center"><a href="https://github.com/stanfordaiethics/exploratory_project_public">Exploratory project release (Apr 10)</a></td>
<td align="center"></td>
</tr>
<tr>
<td>3</td>
<td align="center"> Apr 15 <br>Apr 17</td>
<td align="center">
<nobr>
Guest Lecture by
<a href="https://dho.stanford.edu/"> Dan Ho</a>
<br>
Fair Models via Constrained Optimization [<a href="https://drive.google.com/file/d/1ZwD5xen9evKNOTfZtD7PklEbtIm_jitz/view?usp=drive_link">slides-a</a>],
Individual Fairness [<a href="https://drive.google.com/file/d/1BiNkkF9gSkdfLBtM-kChsW3_4rMq2Xt9/view?usp=sharing">slides-a</a>]
</td>
<td align="center">
<a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a>
<br>
<nobr>
</nobr>
</td>
<td align="center"></td>
<td align="center">
HW 1 (Apr 17)
</td>
</tr>
<tr>
<td>4</td>
<td align="center"> Apr 22 <br>Apr 24</td>
<td align="center">
Fairness Socio-Technical Analysis
<br>
Bias in NLP [<a href="https://drive.google.com/file/d/119DoZliJhkBdszjCgyIWn_oWDYqseQ_4/view?usp=sharing">slides-a</a>], Fairness and Causality [<a href="https://drive.google.com/file/d/1O4-ETrXo_Gkjatt7QmKshWGIvDz_hdOP/view?usp=sharing">slides-a</a>]
</td>
<td align="center">
<a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a>
</td>
<td align="center">
<nobr>
<a href="https://github.com/stanfordaiethics/hw2_public">HW 2 release (Apr 24)</a>
<br>
</td>
<td align="center"> Socio-tech summary (Apr 24) </td>
</tr>
<tr>
<td>5</td>
<td align="center"> Apr 29 <br>May 1</td>
<td align="center">
Guest Lecture by
<a href="https://cs.illinois.edu/about/people/faculty/lbo"> Bo Li</a>
<br>
Explainability and Transparency [<a href="https://drive.google.com/file/d/1yG4V0eScW_EaInPURYv5dNgn8Po11ozT/view?usp=sharing">slides-a</a>], Feature Attribution and LIME [<a href="https://drive.google.com/file/d/10Jxy40io_ER-iXy_E_5MWioYQwv7uM_R/view?usp=sharing">slides-a</a>]
</td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center"></td>
<td align="center"> <nobr>
Exploratory project (Apr 29) <br> Final project proposal (May 3)
</nobr></td>
</tr>
<tr>
<td>6</td>
<td align="center"> May 6 <br>May 8</td>
<td align="center">
Shapley Values & SHAP [<a href="https://drive.google.com/file/d/17P9GxW0kO7FXMNpXFD544eHtYbaG6Kvj/view?usp=sharing">slides-a</a>], Saliency Maps [<a href="https://drive.google.com/file/d/1f8GFd7xpQhTdf8ynXR5R5_b51gYL8d4J/view?usp=sharing">slides-a</a>], Exemplar-Based Explanations [<a href="https://drive.google.com/file/d/1pCpGzxvLy9vs1A_fRga0gGU71x31laU0/view?usp=sharing">slides-a</a>]
<br>
Explainability Socio-Technical Analysis<nobr></td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center"></td>
<td align="center">
</td>
</tr>
<tr>
<td>7</td>
<td align="center">
<nobr>
May 13
</nobr>
<br>
May 15
</td>
<td align="center">
Concept-Based Explanations [<a href="https://drive.google.com/file/d/1nJui83OCo9jeei1wuev60DCDzPp_31Dc/view?usp=sharing">slides-a</a>], Counterfactual Explanations [<a href="https://drive.google.com/file/d/1aBdZaKbOTUyBWvH-fpYCra9Tw2Db0_3x/view?usp=sharing">slides</a>]
<br>
Privacy and ML [<a href="https://drive.google.com/file/d/1KaXV-4hsR7_PPiN5dVVP-WWgLGsbqxSA/view?usp=sharing">slides</a>], Differential Privacy [<a href="https://drive.google.com/file/d/1LDxFPt15jlTkfH7aMGtDOvBhrWBV73Pe/view?usp=sharing">slides-a</a>]
</td>
<td align="center">
<nobr>
<a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a>
</nobr>
<br>
</td>
<td align="center"> <a href="https://github.com/stanfordaiethics/hw3_public">HW 3 release (May 15)</a> </td>
<td align="center">
<nobr>
HW 2 (May 13) <br>
Socio-tech summary (May 15)
</nobr>
</td>
</tr>
<tr>
<td>8</td>
<td align="center"> May 20 <br>May 22</td>
<td align="center">
Differential Privacy -- continued
<br>
Learning with Differential privacy, Federated Learning [<a href="https://drive.google.com/file/d/1wkW1ALIiDuSiK7OyNVHrxvtdSCt0iZDo/view?usp=sharing">slides-a</a>]
</td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center"></td>
<td align="center">
Final project milestone (May 22)
</td>
</tr>
<tr>
<td>9</td>
<td> May 27 <br>May 29</td>
<td align="center">
Memorial Day -- No class
<br>
Large Language Models and the Ethics of AI: Impact, Gaps and Opportunities [<a href="https://drive.google.com/file/d/1AxxO5xLiRr2rEspO4Adi0opATEb4m853/view?usp=sharing">slides</a>]
</td>
<td align="center">
<br>
</nobr>
</td>
<td align="center"> </td>
<td align="center">
<nobr> HW 3 (May 29) </nobr></td></tr>
<tr>
<td>10</td>
<td> June 3 <br> June 5</td>
<td align="center">
Privacy Socio-Technical Analysis
<br>
Closing Discussion -- Trust in AI
</td>
<td align="center"><a href="http://campus-map.stanford.edu/?srch=380-380X"> 380-380X</a></td>
<td align="center"> </td>
<td align="center">
<nobr>
Socio-tech summary (June 5)
</nobr>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td colspan="6" style="text-align:center; vertical-align:middle;">
<b>
Poster session on June 6, 2:00 pm - 4:00 pm. AT&T Patio, Gates Building.
</b>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td colspan="6" style="text-align:center; vertical-align:middle;">
<b>
Project final report due June 10, 1:00 pm.
</b>
</td>
</tr>
<!-- <tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">-->
<!-- <td align="left"></td>-->
<!-- <td align="left"></td>-->
<!-- <td colspan="3" style="text-align:center; vertical-align:middle;">-->
<!-- Poster Presentation: <b>Day</b>: December 03, 2021 - <b>Time</b>: Session A: 11am - 1pm, Session B: 1:30pm - 3:30pm - <b>Location</b>: McCaw Hall (Alumni Center)-->
<!-- </td>-->
<!-- </tr>-->
<!-- <tr>-->
<!-- <td>12</td>-->
<!-- <td> Dec 06 </td>-->
<!-- <td align="center">-->
<!-- Finals Week-->
<!-- </td>-->
<!-- <td align="center"></td>-->
<!-- </tr>-->
<!--<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">-->
<!-- <td align="left"></td>-->
<!-- <td align="left"></td>-->
<!-- <td colspan="3" style="text-align:center; vertical-align:middle;">-->
<!-- Final Project Reports: Due Tuesday, December 07, 2021-->
<!-- </td>-->
<!--</tr>-->
</tbody>
</table>
</div>
<!--<hr/>-->
<!--<div class="container sec">-->
<!-- <h2>Additional Reading: Surveys and Tutorials</h2>-->
<!-- <br>-->
<!-- <ol>-->
<!-- <li><a href="http://yang-song.github.io/blog/2021/score/">Generative Modeling by Estimating Gradients of the Data Distribution</a> Yang Song. Blog post on score-based generative models, May 2021.</li>-->
<!-- <li><a href="https://arxiv.org/abs/2101.03288">How to Train Your Energy-Based Models.</a> Yang Song and Diederik P. Kingma. February 2021. </li>-->
<!-- <li><a href="https://ermongroup.github.io/generative-models/">Tutorial on Deep Generative Models.</a> Aditya Grover and Stefano Ermon. International Joint Conference on Artificial Intelligence, July 2018.</li>-->
<!-- <li><a href="https://sites.google.com/view/cvpr2018tutorialongans/">Tutorial on Generative Adversarial Networks.</a> Computer Vision and Pattern Recognition, June 2018.</li>-->
<!-- <li><a href="https://www.youtube.com/watch?v=JrO5fSskISY">Tutorial on Deep Generative Models.</a> Shakir Mohamed and Danilo Rezende. Uncertainty in Artificial Intelligence, July 2017.</li>-->
<!-- <li><a href="https://www.youtube.com/watch?v=AJVyzd0rqdc">Tutorial on Generative Adversarial Networks.</a> Ian Goodfellow. Neural Information Processing Systems, December 2016.</li>-->
<!-- <li><a href="https://www.cs.cmu.edu/~rsalakhu/papers/annrev.pdf">Learning deep generative models.</a> Ruslan Salakhutdinov. Annual Review of Statistics and Its Application, April 2015.</li>-->
<!-- </ol>-->
<!--</div>-->
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