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stoyanovich committed Nov 20, 2023
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79 changes: 64 additions & 15 deletions _bibliography/papers.bib
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Original file line number Diff line number Diff line change
Expand Up @@ -312,9 +312,9 @@ @article{bynum2022interactive
title = {An Interactive Introduction to Causal Inference},
journal = {VISxAI: Workshop on Visualization for AI Explainability},
year = {2022},
pdf = {https://lbynum.github.io/interactive-causal-inference},
site = {https://lbynum.github.io/interactive-causal-inference},
publisher = {IEEE},
keywords = {workshop,education},
keywords = {workshop,education,playground},
preview = {heircare.png},
bibtex_show={true}
}
Expand Down Expand Up @@ -409,8 +409,9 @@ @article{1894Showreel
@article{AllAboard,
author = {Falaah {Arif Khan} and Lucius Bynum and Amy Hurst and Lucas Rosenblatt and Meghana Shanbhogue and Mona Sloane and Julia Stoyanovich},
title = {{All Aboard! Making AI Education Accessible}},
journal = {Center for Responsible AI, New York University},
pdf = {http://r-ai.co/AllAboard},
keywords = {panel,education},
keywords = {panel,education,weareai},
year = {2023},
bibtex_show={true},
preview={allaboard.png},
Expand All @@ -428,9 +429,10 @@ @inproceedings{DBLP:conf/chi/BellNS23
pages = {554:1--554:4},
publisher = {{ACM}},
year = {2023},
site = {https://dataresponsibly.github.io/algorithmic-transparency-playbook/},
pdf = {https://doi.org/10.1145/3544549.3574169},
doi = {10.1145/3544549.3574169},
keywords = {panel,policy,explanability,education},
keywords = {panel,policy,explanability,education,playbook},
addendum = {peer-reviewed course},
preview = {playbook.png},
bibtex_show={true},
Expand Down Expand Up @@ -528,7 +530,7 @@ @article{bell_nov_stoyanovich_2023
journal={Data \& Policy},
publisher={Cambridge University Press},
year={2023},
keywords = {journal,policy,explainability,education},
keywords = {journal,policy,explainability,education,playbook},
bibtex_show={true},
preview = {transparency.png}
}
Expand Down Expand Up @@ -1763,6 +1765,7 @@ @article{DBLP:journals/pvldb/RosenblattHHLLM23
biburl = {https://dblp.org/rec/journals/pvldb/RosenblattHHLLM23.bib},
keywords = {journal,privacy},
bibtex_show={true},
code = {https://github.com/DataResponsibly/SynRD},
preview={epistemic.png},
selected={true},
author+an = {1=self;3=self;4=self;6=self;7=self;8=self;10=self}
Expand Down Expand Up @@ -2097,8 +2100,20 @@ @article{weareaicomic_vol1
journal = {We are AI Comic Series},
volume = {1},
year = {2021},
pdf = {https://dataresponsibly.github.io/we-are-ai/comics/},
keywords ={public,education},
pdf = {http://bit.ly/we-are-aicomicsvol1},
keywords ={public,education,comics,english},
preview = {1-cover.png},
bibtex_show={true}
}

@article{weareaicomic_vol1_sp,
author = {Julia Stoyanovich and Falaah Arif Khan},
title = {What is AI? (Spanish Edition)},
journal = {We are AI Comic Series},
volume = {1},
year = {2021},
pdf = {http://dataresponsibly.github.io/we-are-ai/comics/vol1_es.pdf},
keywords ={public,education,comics,spanish},
preview = {1-cover.png},
bibtex_show={true}
}
Expand All @@ -2109,9 +2124,21 @@ @article{weareaicomic_vol2
journal = {We are AI Comic Series},
volume = {2},
year = {2021},
pdf = {https://dataresponsibly.github.io/we-are-ai/comics/},
pdf = {http://bit.ly/we-are-ai_comics_vol2_en},
preview = {2-cover.png},
keywords ={public,education},
keywords ={public,education,comics,english},
bibtex_show={true}
}

@article{weareaicomic_vol2_sp,
author = {Julia Stoyanovich and Falaah Arif Khan},
title = {Learning from data (Spanish Edition},
journal = {We are AI Comic Series},
volume = {2},
year = {2021},
pdf = {http://dataresponsibly.github.io/we-are-ai/comics/vol2_es.pdf},
preview = {2-cover.png},
keywords ={public,education,comics,english},
bibtex_show={true}
}

Expand All @@ -2122,7 +2149,7 @@ @article{weareaicomic_vol3
volume = {3},
year = {2021},
pdf = {https://dataresponsibly.github.io/we-are-ai/comics/},
keywords ={public,education},
keywords ={public,education,comics,english},
preview = {3-cover.png},
bibtex_show={true}
}
Expand All @@ -2136,7 +2163,7 @@ @article{weareaicomic_vol4
pdf = {https://dataresponsibly.github.io/we-are-ai/comics/},
bibtex_show={true},
preview = {4-cover.png},
keywords ={public,education}
keywords ={public,education,comics,english}
}

@article{weareaicomic_vol5,
Expand All @@ -2147,7 +2174,7 @@ @article{weareaicomic_vol5
year = {2021},
pdf = {https://dataresponsibly.github.io/we-are-ai/comics/},
preview = {5-cover.png},
keywords ={public,education},
keywords ={public,education,comics,english},
bibtex_show={true}
}

Expand All @@ -2158,7 +2185,7 @@ @article{rdscomic_vol1
volume = {1},
year = {2020},
pdf = {https://dataresponsibly.github.io/comics/},
keywords ={public,education},
keywords ={public,edu,comics,english},
preview = {mirror.png},
bibtex_show={true}
}
Expand All @@ -2170,7 +2197,7 @@ @article{rdscomic_Vol2
volume = {2},
year = {2021},
pdf = {https://dataresponsibly.github.io/comics/},
keywords ={public,edu},
keywords ={public,edu,comics,english},
preview = {fairness_and_friends.png},
bibtex_show={true}
}
Expand Down Expand Up @@ -2560,11 +2587,12 @@ @article{LewisStoyanovich21
title = {Teaching Responsible Data Science},
journal = {International Journal of Artificial Intelligence in Education (IJAIED)},
year = {2021},
keywords = {journal,education},
keywords = {journal,education,rds},
addendum = {Special Issue: The FATE of AI in Education: Fairness, Accountability, Transparency, and Ethics},
doi = {https://doi.org/10.1007/s40593-021-00241-7},
pdf = {https://doi.org/10.1007/s40593-021-00241-7},
preview = {apples.png},
site = {https://dataresponsibly.github.io/rds/},
bibtex_show={true}
}

Expand Down Expand Up @@ -2645,3 +2673,24 @@ @inproceedings{DBLP:conf/eaamo/ArifKhanMS22
selected={true}
}
@article{medium_rds,
author = {Mary Oliver},
title = {Responsible Data Science: Charting New Pedagogical Territory},
journal = {Medium},
html = {https://nyudatascience.medium.com/responsible-data-science-charting-new-pedagogical-territory-9628c94134d},
year = {2020},
month = {02},
day = {17},
keywords ={press,rds},
preview = {medium.png}
}

@article{weareai_course,
author = {Julia Stoyanovich and Eric Corbett},
title = {We are AI: Taking Control of Technology},
journal = {Center for Responsible AI, New York University},
site = {https://dataresponsibly.github.io/we-are-ai/},
year = {2021},
keywords={education,weareai},
preview = {weareai.png}
}
14 changes: 7 additions & 7 deletions _layouts/bib.html
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Expand Up @@ -137,20 +137,20 @@
{%- if entry.bibtex_show %}
<a class="bibtex btn btn-sm z-depth-0" role="button">Cite</a>
{%- endif %}
{%- if entry.html %}
<a href="{{ entry.html }}" class="btn btn-sm z-depth-0" role="button">read online</a>
{%- endif %}
{%- if entry.site %}
<a href="{{ entry.site }}" class="btn btn-sm z-depth-0" role="button">online resource</a>
{%- endif %}
{%- if entry.pdf %}
{% if entry.pdf contains '://' -%}
<a href="{{ entry.pdf }}" class="btn btn-sm z-depth-0" role="button">PDF</a>
{%- else -%}
<a href="{{ entry.pdf | prepend: '/assets/pdf/' | relative_url }}" class="btn btn-sm z-depth-0" role="button">PDF</a>
{%- endif %}
{%- endif %}
{%- if entry.supp %}
{%- if entry.html %}
<a href="{{ entry.html }}" class="btn btn-sm z-depth-0" role="button">read online</a>
{%- endif %}
{%- if entry.site %}
<a href="{{ entry.site }}" class="btn btn-sm z-depth-0" role="button">online resource</a>
{%- endif %}
{%- if entry.supp %}
{% if entry.supp contains '://' -%}
<a href="{{ entry.supp }}" class="btn btn-sm z-depth-0" role="button">Supp</a>
{%- else -%}
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135 changes: 72 additions & 63 deletions _pages/education.md
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Expand Up @@ -11,8 +11,6 @@ horizontal: false

<div id="banner-other" style="background-image: url('{{ "/assets/img/banner/M5-banner.png" | relative_url }}');"></div>

<!-- <h1 class="category" id="education">Education</h1> -->

Education stands as the cornerstone of our work at the Center for
Responsible AI. Understanding the profound implications of AI on
society necessitates a comprehensive and inclusive approach to
Expand All @@ -22,11 +20,12 @@ professionals, and the broader community. Our goal is to empower
learners with the knowledge and critical thinking skills to actively
shape the trajectory of AI technology.

<h4 class="category" id="rds">Responsible data science for university students</h4>
<h4 class="category" id="rds">Responsible data science</h4>

Responsible Data Science is a technical course that tackles the issues
of ethics, legal compliance, data quality, algorithmic fairness and
diversity, transparency of data and algorithms, privacy, and data
Responsible Data Science is a **comprehensive technical course for
university students**. This course tackles the issues of ethics,
legal compliance, data quality, algorithmic fairness and diversity,
transparency of data and algorithms, privacy, and data
protection. This course was developed and offered for the first time
in [Spring 2019](https://dataresponsibly.github.io/courses/spring19).
Since then, it has been offered every Spring to undergraduate and
Expand All @@ -40,84 +39,94 @@ slides, lab notebooks, and readers on the [course
website](https://dataresponsibly.github.io/rds/). All course
materials are publicly available online.

<h5>Read more about this course</h5>
<h5><b>Read more about this course</b></h5>

<div class="publications">
{% bibliography -f papers -q @*[keywords ^= *rds]%}
</div>

<!--
- **2023 Spring semester:**
- [DS-GA 1017 and DS-UA 202: Responsible Data Science](https://dataresponsibly.github.io/rds23/)
- Co-taught by: Julia Stoyanovich and [Elisha Cohen](https://www.elishacohen.com/)
- **2022 Spring semester:**
- DS-GA 1017 and DS-UA 202: Responsible Data Science
- Co-taught by: Julia Stoyanovich and [George Wood](http://gwood.me/)
- **2021 Spring semester:**
- DS-GA 1017 and DS-UA 202: Responsible Data Science
- Co-taught by: Julia Stoyanovich and [George Wood](http://gwood.me/)
- **2020 Spring semester:**
- DS-GA 3001.009: Special Topics in Data Science: Responsible Data Science
- Taught by: Julia Stoyanovich
- **2019 Spring semester:**
- DS-GA 3001.009: Special Topics in Data Science: Responsible Data Science
- Taught by: Julia Stoyanovich
-->

<h4 class="category" id="playground">The causal inference playground</h4>

[An interactive adventure through foundational causal inference
concepts](https://lbynum.github.io/interactive-causal-inference/). By
the end, you should have a good intuition for Causal Inference and be
able to use the ‘Causal Inference Playground’ in the conclusion to
reinforce causal inference concepts. It's also fun!
What is causal inference? And how can we use causal inference
techniques to answer questions about the real world? Take a tour
through our [Causal inference
playground](https://lbynum.github.io/interactive-causal-inference/)
for a rigorous and fun introduction to this topic!

*Published at VISxAI 2022: Workshop on Visualization for AI Explainability.*
<h5><b>Read more about this course</b></h5>

<div class="row mt-3">
<a href="https://lbynum.github.io/interactive-causal-inference/">
<div class="col-sm mt-3 mt-md-0">
{% include figure.html path="assets/img/causal_vis_ai.png" class="img-fluid rounded z-depth-1" %}
</div>
</a>
<div class="publications">
{% bibliography -f papers -q @*[keywords ^= *playground]%}
</div>

<h1 class="category" id="practitioners">Practitioners</h1>
<h4 class="category" id="playbook">The algorithmic transparency playbook</h4>

Welcome to 2033, the year when AI, while not yet sentient, can finally
be considered responsible. Only systems that work well, improve
efficiency, are fair, law abiding, and transparent are in use
today. It’s AI nirvana. You ask yourself: “How did we get here?”

<div class="row mt-3">
<a href="https://dataresponsibly.github.io/algorithmic-transparency-playbook/">
<div class="col-sm mt-3 mt-md-0">
{% include figure.html path="assets/img/algorithmic_transparency.png" class="img-fluid rounded z-depth-1" %}
</div>
</a>
You may have played a major role! As more organizations use
algorithmic systems, there is a need for practitioners, industry
leaders, managers, and executives to take part in making AI
responsible. In our [Algorithmic Transparency
Playbook](https://dataresponsibly.github.io/algorithmic-transparency-playbook/)
course, we detail how to influence change and **implement algorithmic
transparency in your organization**.

<h5><b>Read more about this course</b></h5>

<div class="publications">
{% bibliography -f papers -q @*[keywords ^= *playbook]%}
</div>

### A Stakeholder-first Approach to Creating Transparency for Your Organization's Algorithms
<h4 class="category" id="weareai">We are AI: Taking control of technology</h4>

Welcome to 2033, the year when AI, while not yet sentient, can finally be considered responsible. Only systems that work well, improve efficiency, are fair, law abiding, and transparent are in use today. It’s AI nirvana. You ask yourself: “How did we get here?”
Artificial Intelligence (“AI”) refers to a growing world of
sophisticated computer programs that “learn” from data in order to
make decisions. Many of these AI systems are invisible to the public,
yet the results of the decisions they make (or help humans make) have
a huge impact on modern life.

You may have played a major role! As more organizations use algorithmic systems, there is a need for practitioners, industry leaders, managers, and executives to take part in making AI responsible. In our [Algorithmic Transparency Course](https://dataresponsibly.github.io/algorithmic-transparency-playbook/), we provide a playbook, detailing how to influence change and implement algorithmic transparency for your organization’s algorithmic systems.
Because of how important AI is in our lives, we should understand how
it works so that we can control it together! NYU R/AI has partnered
with [P2PU](https://www.p2pu.org/en/), a public education non-profit,
and with the [Queens Public
Library](https://www.queenslibrary.org/about-us/news-media/blog/2482)
to develop [We are AI: Taking control of
technology](https://dataresponsibly.github.io/we-are-ai/), a **public
education course on AI**.

This course is based on the [Algorithmic Transparency Playbook](https://dataresponsibly.github.io/algorithmic-transparency-playbook/resources/transparency_playbook_camera_ready.pdf), which is a free-guide to algorithmic transparency published by the New York University Center for Responsible AI.
This course is designed to be run as a learning circle: a facilitated
study group for people who want to meet regularly and learn about a
topic with others. There are no teachers or students in a learning
circle—it is a group where everyone learns the material together.

***
The goal of the course is to introduce the basics of AI, discuss some
of the social and ethical dimensions of the use of AI in modern life,
and empower individuals to engage with how AI is used and governed.

<h1 class="category" id="university">Public</h1>
<h5><b>Read more about this course</b></h5>

<h4 class="category" id="weareai">We are AI: Taking Control of Technology</h4>
<div class="publications">
{% bibliography -f papers -q @*[keywords ^= *weareai]%}
</div>

<h4 class="category" id="comics">Responsible AI comics</h4>

<h5><b>Read the comics (English)</b></h5>

<div class="row mt-3">
<a href="https://dataresponsibly.github.io/we-are-ai/">
<div class="col-sm mt-3 mt-md-0">
{% include figure.html path="assets/img/we_are_ai.png" class="img-fluid rounded z-depth-1" %}
</div>
</a>
<div class="publications-div">
{% bibliography -f papers -q @*[keywords ^= *comics && keywords ^= *english]%}
</div>
<p><h5><b>Read the comics (Spanish)</b></h5>

We have hosted a series of our popular [We Are AI](https://dataresponsibly.github.io/we-are-ai/) workshop with [Queens Public Library](https://www.queenslibrary.org/about-us/news-media/blog/2482) and Meta. These workshops aim to demystify AI and foster responsible AI use. Together, we're bringing education on AI ethics and practices to a targeted audience, promoting a more informed and inclusive technical workforce.
<div class="publications-div">
{% bibliography -f papers -q @*[keywords ^= *comics && keywords ^= *spanish]%}
</div>

<p><h5><b>Read the comics (other languages)</b></h5>

***
<div class="publications-div">
{% bibliography -f papers -q @*[keywords ^= *comics && keywords ^= *otherlang]%}
</div>
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