An algorithm is a procedure used for solving a problem or performing a computation. Algorithms act as an exact list of instructions that conduct specified actions step by step in either hardware- or software-based routines. This interesting topic came to us from eWeek in their article, “AI vs. Algorithms.”

Even the smallest of artificial intelligence (AI) designs need basic instructions in order to function. And that’s where algorithms enter. Algorithms are widely used throughout all areas of IT. In mathematics and computer science, an algorithm usually refers to a small procedure that solves a recurrent problem. Algorithms are also used as specifications for performing data processing and play a major role in automated systems.

Algorithms are no doubt a huge part of today’s world. By giving our daily tech tools the descriptive instructions they need to carry out specific tasks, we’re able to automate many processes that would otherwise use up valuable human attention. Algorithms help form the intense calculation that has led to some of the greatest discoveries in medicine, science, engineering, and other areas. 

It is difficult to understand AI decision making. Most organizations fall short. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact, and its potential biases. Explainability is vital when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.