Using the concept of principal stratification from the causal inference literature, we introduce a new notion of fairness, called principal fairness, for human and algorithmic decision-making. The key idea is that one should not discriminate among individuals who would be … Continued
http://christopherbuckley.org/blog/why-successful-children-dont-innovate-an-evolutionary-perspective ‘Innovation’ has become something of a mantra, particularly amongst business elites and educators. We are surrounded by the results of innovation (novel technologies). It is regarded (rightly) as one of the distinguishing characteristics of our species: the ability to … Continued
As AI-enhanced technologies become common in a variety of domains, there is an increasing need to define and examine the trust that users have in such technologies. Given the progress in the development of AI, a correspondingly sophisticated understanding of … Continued
Trust is often cited as an essential criterion for the effective use and real-world deployment of AI. Researchers argue that AI should be more transparent to increase trust, making transparency one of the main goals of XAI. Nevertheless, empirical research … Continued
Sound scale construction is pivotal to the measurement of psychological constructs. Common item sampling procedures emphasize aspects of reliability to the disadvantage of aspects of validity, which are less tangible. We use a health knowledge test as an example to … Continued
As artificial intelligence becomes more powerful and a ubiquitous presence in daily life, it is imperative to understand and manage the impact of AI systems on our lives and decisions. Modern ML systems often change user behavior (e.g. personalized recommender … Continued
This paper documents differences across higher-education courses in the coverage of frontier knowledge. Comparing the text of 1.7M syllabi and 20M academic articles, we construct the “education-innovation gap,” a syllabus’s relative proximity to old and new knowledge. We show that courses … Continued
A growing number of oversight boards and regulatory bodies seek to monitor and govern algorithms that make decisions about people’s lives. Prior work has explored how people believe algorithmic decisions should be made, but there is little understanding of how … Continued
One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to … Continued
This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human … Continued