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Articles by Casey Doyle
Listening to algorithms: The case of self‐knowledge
REFERENCES (2003). Privacy and the question of technology. Law and Philosophy, 22(2), 119–166. (2019). What we epistemically owe to each other. Philosophical Studiesge, 176, 915–931. (2006). Pattern recognition and machine learning: Information science and statistics. Springer. (2008). Two kinds of self-knowledge. Philosophy and Phenomenological Research, 78(1), 133–164. https://doi.org/10.1111/j.1933-1592.2008.00235.x (1996). Our entitlement to self-knowledge.
Celebrating our fourth anniversary of Data Science at Microsoft
So much can happen in a single year, but over four years… truly amazing things can happen! Here at DS@M, exactly four years to the day from when we started, we’ve surpassed 4,500 followers and published nearly 200 articles by scores of our data scientists and related professionals dedicated to telling the ongoing, always evolving story of how data science is done at Microsoft, from nuts-and-bolts foundational topics to the very latest LLM developments — and everything in between! Those of you...
Hierarchical effects facilitate spreading processes on synthetic and empirical multilayer networks
Loading metrics Open Access Peer-reviewed Research Article Casey Doyle , Thushara Gunda, Asmeret Naugle Casey Doyle, Thushara Gunda, Asmeret Naugle Published: June 9, 2021 https://doi.org/10.1371/journal.pone.0252266 Figures Abstract In this paper we consider the effects of corporate hierarchies on innovation spread across multilayer networks, modeled by an elaborated SIR framework.
Machine Learning model governance at scale
Author Peter Saddow is joined for this article by co-author Daniel Yehdego Today, predictive models help many companies run their business-critical processes. These predictive models are probabilistic — they involve chance variation — and they depend on some underlying assumptions to make them work properly. Chief among these are 1.) that the data flowing into them is distributed in a certain way and 2.) that underlying business scenarios are unchanged from the time the models were created.
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