Sullivan, Emily and Kasirzadeh, Atoosa (2024) Explanation Hacking: The perils of algorithmic recourse. [Preprint]

Sullivan, Emily (2024) SIDEs: Separating Idealization from Deceptive ‘Explanations’ in xAI. [Preprint]

Sullivan, Emily (2023) Do ML models represent their targets? [Preprint]

Sullivan, Emily (2022) Inductive Risk, Understanding, and Opaque Machine Learning Models. [Preprint]

Sullivan, Emily (2022) How Values Shape the Machine Learning Opacity Problem. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 306-322.

Sullivan, Emily (2022) Link Uncertainty, Implementation, and ML Opacity: A Reply to Tamir and Shech. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 341-345.

Lawler, Insa and Sullivan, Emily (2020) Model Explanation versus Model-Induced Explanation. [Preprint]

Sullivan, Emily (2019) Universality Caused: The case of renormalization group explanation. European Journal for Philosophy of Science, 9 (36). ISSN 1879-4912

Sullivan, Emily (2019) Understanding from Machine Learning Models. British Journal for the Philosophy of Science. ISSN 1464-3537

Sullivan, Emily and Khalifa, Kareem (2018) Idealizations and Understanding: Much Ado About Nothing? [Preprint]