Sample Published Papers



Machine Learning and Understanding

forthcoming, British Journal for the Philosophy of Science


Universality Caused:

The case of Renormalization Group Explanation

forthcoming, European Journal for Philosophy of Science


Idealizations and Understanding:

Much Ado About Nothing?

2019, Australasian Journal of Philosophy

with Kareem Khalifa


Can Real Social Epistemic Networks

Deliver the Wisdom of Crowds?
2019. Oxford Studies in Experimental Philosophy.
First author, with Sondag, M., Rutter, I., Meulemans, W., Cunningham, S., Speckmann, B., & Alfano M. 


Understanding: Not Know-How

2018,  Philosophical Studies 


Same, Same, but Different: 

Algorithmic Diversification of Viewpoints in News

2018, ACM UMAP’18 Adjunct Proceedings  

Second author, with Nava Tintarev, Dror Guldin, Sihang Qiu, and Daan Odjik





Other

 

I worked on a whitepaper commissioned by the Dutch Government on the influence of social media on the 2018 Dutch Municiple Elections 


I was interviewed by Danish media on understanding and machine learning models (in Danish) 


                            








Upcoming Talks 


2019 May: "Model-Based Explanations"

          Lund University; Sweeden


2019 June: "Explainability Frameworks of Algorithms" Cambridge University


                            






Research Theme



​My research focuses on the ways that technology mediates knowledge, understanding, and our practice of giving and receiving explanations.


This work in involves the theoretical and philosophical foundations of scientific explanation and their potential for enabling understanding. My work also involves applying these theoretical foundations toward use cases in computer science and computer human interaction, such as explanatory frameworks for recommender and decision support systems that promote epistemic values, such as diversity of viewpoint, credibility detection, critical thinking, and understanding. 



Emily Sullivan

Working Papers

(Drafts available upon request)



Complexity in Explanaible AI
In this comprehensive computer science literature review I develop an explanation taxonomy for various types of algorithms based on complexity the risk of the application domain. 


Annotating Bias in Credibility Datasets

Working with a team of computer science and communication science researchers, we analyze various news credibility datasets that are used for automated news credibility assessment for potential biases in the dataset.


Formal Network Epistemology

Working with the DHEPCAT team at TU Delft and TU/e, we argue in favor of a formal graph-theoretical approach to testimonial networks and apply this approach to discussions of vaccine safety on Twitter.


Ethical pitfalls for natural language processing in psychology

Working with colleagues, we outline the ethical pitfalls in using NLP technology in psychology research. 


Understanding and Grasping
I survey the extent to which a theory of grasping has been put forward in the understanding literature and make progress toward building such a theory.​