Skip to content

Commit

Permalink
some reading qmds
Browse files Browse the repository at this point in the history
  • Loading branch information
@scolando
scolando committed Sep 27, 2024
1 parent 6c704f0 commit a286ed0
Show file tree
Hide file tree
Showing 12 changed files with 440 additions and 1 deletion.
2 changes: 1 addition & 1 deletion Reading-Tags.qmd
View file
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
---
title: "Reading Lists"
subtitle: 'readings are primarily pulled from the collated <a href="inside-syllabi.html"> data science ethics courses</a>.'
subtitle: 'The listed readings are primarily pulled from the collated <a href="inside-syllabi.html"> data science ethics courses</a> and are in no way exhaustive. For additional readings, see the references of the readings we list under each ethics topic.'
format:
html:
search: true
Expand Down
41 changes: 41 additions & 0 deletions readings/Alignment.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,41 @@
---
title: "Alignment"
categories:
- "Interactions with World"
- "Producing Objects"
- "Processing Objects as Data"
- "Ordering Data as Models to Represent the World"
- "Interpreting Models as Knowledge"
subtitle: "Readings on the moral alignment between human values and current data science practices."
format:
html:
citeproc: true
bibliography: bibliographies/Alignment.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Alignment-pdf.qmd" target = "_blank">Alignment Reading List PDF</a>
:::
:::

| Title | Citation |
|----------------------------------------------|--------------------------|
| Coded Bias (Documentary) | @CodedBias |
| The Alignment Problem: Machine Learning and Human Values | @Christian2021 |
| Artificial Intelligence, Values, and Alignment | @Gabriel2020 |
| Living Well Together Online: Digital Well-Being from a Confucian Perspective | @Dennis2023 |
| Envisioning Communities: A Participatory Approach Towards AI for Social Good | @Bondi2021 |
| Aligning Artificial Intelligence with Human Values: Reflections from a Phenomenological Perspective | @Han2021 |
| Challenges of Aligning Artificial Intelligence with Human Values | @Sutrop2020 |
| The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions | @Whittlestone2019 |
| Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function) | @Eckersley2019 |
| Risk Imposition by Artificial Agents: The Moral Proxy Problem | @Thoma2022 |

## References

::: {#refs}
:::
42 changes: 42 additions & 0 deletions readings/Bias.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,42 @@
---
title: "Bias, Fairness, and Justice"
subtitle: "Readings on the normative concepts of bias, fairness, and justice and how they are relevant considerations in data science."
categories:
- "Interactions with World"
- "Producing Objects"
- "Processing Objects as Data"
- "Ordering Data as Models to Represent the World"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Bias.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Bias-pdf.qmd" target = "_blank">Bias, Fairness, and Justice Reading List PDF</a>
:::
:::

| Title | Citation |
|------------------------------------------------------------------------------------------------------------|-------------------------|
| Are Algorithms Value-Free? | @Johnson2023 |
| Algorithmic Bias: on the Implicit Biases of Social Technology | @Johnson2020 |
| Algorithmic Bias: Senses, Sources, Solutions | @FazelpourDanks2021 |
| Fairness | @vredenburgh2023 |
| Just Machines | @Castro2022 |
| Big Data and Compounding Injustice | @Hellman2023 |
| On the Site of Predictive Justice | @LazarStoneForthcoming |
| Proceed with Caution | @ZimmermannStronach2021 |
| Semantics Derived Automatically from Language Corpora Contain Human-like Biases | @Caliskan2017 |
| The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems | @CreelHellamn2022 |
| Facebook has been Charged with Housing Discrimination by the US government | |

## References

::: {#refs}
:::
43 changes: 43 additions & 0 deletions readings/Causation.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,43 @@
---
title: "Causation"
subtitle: "Readings on causation as it applies to data science, and more specifically data science ethics. A primary focus of these readings is causal inference and socially sensitive attributes (e.g., race and gender)."
categories:
- "Interactions with World"
- "Ordering Data as Models to Represent the World"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Causation.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Causation-pdf.qmd" target = "_blank">Causation Reading List PDF</a>
:::
:::

| Title | Citation |
|-------------------------------------------------|----------------------|
| The Metaphysics of Causation (Stanford Encyclopedia of Philosophy) | @scheines |
| The Problem of Induction (Stanford Encyclopedia of Philosophy) | @sep-induction-problem |
| Causation | @scheines |
| Causal Diagrams for Empirical Research | @Pearl |
| Disparate Causes, Pt. I | @Hu2019 |
| Variation Semantics: When Counterfactuals in Explanations of Algorithmic Decisions are True | @pittphilsci20626 |
| Causal Discovery Algorithms: A Practical Guide | @Malinsky2018 |
| On the Explanatory Depth and Pragmatic Value of Coarse-Grained, Probabilistic, Causal Explanations | @Kinney2018 |
| Fairness in Decision-making --- the Causal Explanation Formula | @Zhang2018 |
| Evaluations of Causal Claims Reflect a Trade-Off Between Informativeness and Compression | @Kinney2022 |
| The Use and Misuse of Counterfactuals in Ethical Machine Learning | @causal-inf-use |
| Eddie Murphy and the Dangers of Counterfactual Causal Thinking About Detecting Racial Discrimination | @Eddie-murphy |
| \"But What Are You *Really*?\": The Metaphysics of Race | @Mills1998 |
| What is \"Race\" in Algorithmic Discrimination on the Basis of Race? | @Hu |

## References

::: {#refs}
:::
43 changes: 43 additions & 0 deletions readings/Characterizations.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,43 @@
---
title: "Characterizations of Data and Data Science"
subtitle: "Readings on how to characterize data, data science, artificial intelligence, and data science ethics."
categories:
- "Interactions with World"
- "Producing Objects"
- "Processing Objects as Data"
- "Ordering Data as Models to Represent the World"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Characterizations.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Characterizations-pdf.qmd" target = "_blank">Characterizations of Data and Data Science Reading List PDF</a>
:::
:::

| Title | Citation |
|----------------------------------------------|--------------|
| Data and Society: A Critical Introduction | @Beaulieu-Leonelli |
| Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon | @Boyd2012 |
| What Distinguishes Data from Models? | @Leonelli |
| Raw Data is an Oxymoron (Introduction) | @Gitelman2013 |
| On Being a Data Skeptic | @ONeil2013 |
| The Epistemological Foundations of Data Science: A Critical Review | @Desai2022 |
| Artificial Intelligence: A Guide for Thinking Humans (Chapter 1) | @Mitchell2020 |
| The Invention of Ethical AI: How Big Tech Manipulates Academia to Avoid Regulation | @Ochigame |
| Digital Ethics as Translational Ethics | @Danks2022 |
| The Trouble with Algorithmic Decisions: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making | @Zarsky2016 |
| What is Computer Ethics? | @Moor1985 |
| What is Data Ethics? | @DS-ethics-def |

## References

::: {#refs}
:::
37 changes: 37 additions & 0 deletions readings/Consent.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,37 @@
---
title: "Consent"
subtitle: "Readings on informed consent in data science practices, why it ethically matters, and how to grapple with the practical challenges of obtaining informed consent in data science."
categories:
- "Interactions with World"
- "Producing Objects"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Consent.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Consent-pdf.qmd" target = "_blank">Consent Reading List PDF</a>
:::
:::

| Title | Citation |
|--------------------------------------------------|----------------------|
| Informed Consent (Stanford Encyclopedia of Philosophy) | @sep-informed-consent |
| Privacy Self-Management and the Consent Dilemma | @Solove |
| The Ethics of Consent: Theory and Practice | @ethics-consent |
| Middletown, a Study in Contemporary American Culture | @Middletown1929 |
| A Belmont Report for Health Data | @Parasidis2019 |
| Naturalizing Coercion: The Tuskegee Experiments and the Laboratory Life of the Plantation | @Rusert2019 |
| What Makes Personal Data Processing by Social Networking Services Permissible? | @wolmarans_voorhoeve_2022 |
| What's Wrong with Automated Influence | @automated-influence |

## References

::: {#refs}
:::
44 changes: 44 additions & 0 deletions readings/Democracy.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,44 @@
---
title: "Democracy"
subtitle: "Readings and case studies on the applications of data science in democracy and the ethical implications of using statistical models in such contexts."
categories:
- "Interactions with World"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Democracy.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Democracy-pdf.qmd" target = "_blank">Democracy Reading List PDF</a>
:::
:::

| Title | Citation |
|-------------------------------------------------------|-----------------|
| Weapons of Math Destruction | @oneil2016 |
| Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor | @Eubanks2018 |
| Algorithmic Content Moderation: Technical and Political challenges in the Automation of Platform Governance | @Gorwa2020 |
| Artificial Intelligence: Risks to Privacy and Democracy | @Manheim2018 |
| Pop-up Political Advocacy Communities on Reddit.com: SandersForPresident and The Donald | @Mills2017 |
| Artificial Intelligence in Politics: Establishing Ethics | @Kane2019 |
| Hey Google, Leave those Kids Alone: Against Hypernudging Children in the Age of Big Data | @Smith2023 |
| Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence | @Buhmann2022 |
| Will Democracy Survive Big Data and Artificial Intelligence? | @Helbing2017 |
| Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media (Chapter 6) | @Gillespie2018 |
| Ex-Content Moderator Sues Facebook, Saying Violent Images Caused Her PTSD | @Hern2019 |
| Twitter and Tear Gas: The Power and Fragility of Networked Protest (Chapter 1) | @Tufekci2017 |
| You Think You Want Media Literacy: Do You? | @Boyd2018 |
| Machine Politics: The Rise of the Internet and a New Age of Authoritarianism | @Turner2019 |
| Judgment under Siege: The Three Body Problem of Expert Legitimacy | @Jasanoff2006 |
| In a Constitutional Moment: Science and Social Order at the Millennium | @Jasanoff2003 |

## References

::: {#refs}
:::
42 changes: 42 additions & 0 deletions readings/Explainability.qmd
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,42 @@
---
title: "Explainability, Intepretability, and Transparency"
subtitle: 'Readings on what is meant by "explainability" (and related terms like "transparency" and "interpretability") in data science and to what extent acheiving explainabilty (or transparency or interpretability) in algorithms is morally important.'
categories:
- "Interactions with World"
- "Ordering Data as Models to Represent the World"
- "Interpreting Models as Knowledge"
format:
html:
citeproc: true
bibliography: bibliographies/Explainability.bib
link-citations: true
csl: bibliographies/apa-6th-edition.csl
css: readings.css
---

::: link-2
::: link-2-header
#### {{< iconify icons8:pdf >}} <a href = "pdf-qmds/Explainability-pdf.qmd" target = "_blank">Explainability, Interpretability, and Transparency Reading List PDF</a>
:::
:::

| Title | Citation |
|------------------------------------------------------|------------------|
| Transparency in Complex Computational Systems | @Creel2020 |
| How the Machine "Thinks": Understanding Opacity in Machine Learning Algorithms | @Burrell2015 |
| Explainable AI: A Review of Machine Learning Interpretability Methods | @Linardatos2020 |
| Transparency's Ideological Drift | @Pozen2018 |
| Philosophy of Science at Sea: Clarifying the Interpretability of Machine Learning | @Beisbart2022 |
| The Right to an Explanation | @Vredenburgh2021 |
| Algorithmic and Human Decision Making: For a Double Standard of Transparency | @Gunther2022 |
| The Mythos of Model Interpretability | @Lipton2016 |
| Epistemic Values in Feature Importance Methods: Lessons from Feminist Epistemology | @Hancox2021 |
| The Fate of Explanatory Reasoning in the Age of Big Data | @Cabrera2020 |
| Interpreting Interpretability: Understanding Data Scientists' Use of Interpretability Tools for Machine Learning | @Kaur2020 |
| "Explaining" Machine Learning Reveals Policy Challenges | @Coyle2020 |
| Why Should I Trust You? Explaining the Predictions of Any Classifier | @Ribeiro2016 |

## References

::: {#refs}
:::
Loading

0 comments on commit a286ed0

Please sign in to comment.