The Franke Program in Science and the Humanities and John Templeton Foundation

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December 10, 2021

Today, society consumes tremendous amounts of information through various online channels from ride-sharing apps to online shopping, and these platforms are governed by decision-making algorithms that continuously learn about human behavior and their preferences through the data we generate as humans. For artificial intelligence algorithms to make future decisions, this generated data is crucial.  

November 18, 2021

A key objective of Artificial Intelligence (AI) techniques is to learn from data how patterns can be associated with prediction power. To be able to extract inference from data as a tool to be able to implicate, authorize, and supervise various aspects of society is groundbreaking. In addition, for the hiring and public services sectors, identities, demographic attributes, preferences, and predicting future behavior as related to criminal justice and lending are all possible applications for these algorithms.

October 18, 2021

Confederated developments in computer science, psychology, and other fields have created a real revolution in human understanding of causal relationships that have taken place throughout history. Causal modeling provided us with a precise, formal framework to represent causal relationships. This framework is undeniably influential and powerful due to its utility in raising concrete scientific questions.

September 24, 2021

As humans, we are always striving to deduce the cause of everything we go through in order to comprehend what has happened to us and to make predictions for what will happen to us in the future. Causation is an elusive concept we are trying to grasp across all domains of life and sciences in an attempt to understand the role it plays in causal models, decision making, logical thinking, reasoning, judgments, inductive inference, language, and learning. 

September 20, 2021

The study of computational biology, theoretical biology, and bioinformatics enable us to derive molecular and cellular components and their interactions. On this level of theory, computational biology aims to predict causal connections through correlations. This process of expressing the correlations of genes in the genome through millions of cells simultaneously results in very large data sets.