Computational complexity theory examines the intrinsic difficulty of algorithmic problems by classifying them into hierarchies according to the resources—typically time and space—required for their ...

Part of “Complexity Theory,” a column on the tangled questions of our technological age. “Addressing algorithmic bias is like hygiene,” she told me. “You don’t brush once, you don’t floss once, you do ...

Communication complexity examines the minimum volume of information exchange required among distributed agents to compute a function of their combined inputs. Established nearly four decades ago, this ...

Sorting algorithms are a common exercise for new programmers, and for good reason: they introduce many programming fundamentals at once, including loops and conditionals, arrays and lists, comparisons ...

Computer scientists are looking to evolutionary biology for inspiration in the search for optimal solutions among astronomically huge sets of possibilities. Creationists love to insist that evolution ...

Companies can benefit significantly from algorithms based on advanced analytics and machine learning, but it’s often all too easy to overlook the risks that may come with them. The rise of advanced ...

For better accountability, we should shift the focus from the design of these systems to their impact. Describing a decision-making system as an “algorithm” is often a way to deflect accountability ...