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Articles 1 - 4 of 4
Full-Text Articles in Arts and Humanities
Rereading Albert Camus’ The Plague During A Pandemic: An African’S Review, Stephen O. Owino
Rereading Albert Camus’ The Plague During A Pandemic: An African’S Review, Stephen O. Owino
The Journal of Social Encounters
No abstract provided.
Rereading Albert Camus’ The Plague During A Pandemic: Of Plagues And Nazis: Camus’ Journey From Moral Nihilism, Stephen I. Wagner
Rereading Albert Camus’ The Plague During A Pandemic: Of Plagues And Nazis: Camus’ Journey From Moral Nihilism, Stephen I. Wagner
The Journal of Social Encounters
During our current pandemic, Albert Camus’ novel, The Plague, can serve readers well by illustrating and perhaps helping us resolve the feelings, options and decisions we are now facing. Indeed, Camus can help us learn much from our current situation.
Why Darwin Remains A Problem For Theism, John Houston
Why Darwin Remains A Problem For Theism, John Houston
Forum Lectures
Several recent works in theology have argued that evolutionary theory is compatible with theism. This, of course, is true: theism and evolutionary theory are indeed logically and metaphysically compatible. However, little is being demonstrated on behalf of theism when this conclusion is established. For, the logical and metaphysical compatibility of conceptual frameworks or narratives is a very low bar for attempting to analyze the world and its fundamental nature, and such compatibility tells us little about how the world really is. In this paper I focus on why Darwinian evolutionary theory, though logically and metaphysically compatible with theism, continues to …
Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval
Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval
CSB and SJU Distinguished Thesis
Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that …