Randomized algorithms (intro)

Lecture 4: Quicksort, Randomized Algorithms | Introduction to Algorithms (SMA 5503) | Electrical Engineering and Computer Science | MIT OpenCourseWare

(Boosting randomized algorithms) Imagine that you have an algorithm for solving a decision problem.Suppose that the algorithm makes a decision at random and returns the correct answer with probability(1)/(2) δ with some δ > 0, that is, it is just a bit better than a random guess. To improve the performance,we run the algorithm n times and take the majority vote. Show that, for any ϵ∈ (0, 1), the answer iscorrect with probability at least 1 - ϵ, as long as n ≥(1)/(2δ^2)ln((1)/(ϵ)).For example,

Algorithmic Hiring: Assessing the Pros and Cons - IQ PARTNERS

These are the flaws of AI in hiring and how to tackle them

These are the flaws of AI in hiring and how to tackle them

Balancing Covariates In Randomized Experiments: The Gram--Schmidt Walk Design

Analyzing & Interpreting the Results of Randomized Experiments

Part 1: Randomized Designs for Clinical Trials