Top 5 AI programming languages and their role in ethical AI

Programmers have a variety of options for AI development

Despite AI’s surge in popularity this decade, there’s no specific programming language created especially for AI.  This means that developers looking to create innovations in artificial intelligence will have to choose between already-existing programming languages.  Luckily, most of them adapt well to AI.


Released in 1991, Python is one of the most popular programming languages used for AI.  Python’s simple and easy-to-use syntaxes adapt well to neural language processing, deep learning and machine learning.  Frameworks are well-built and development time is shorter than other programming languages.

Python supports object-oriented, functional and procedural programming styles and works with a variety of pre-built libraries.  It’s also easy to test-run algorithms in Python.  Python is arguably the most popular programming language in AI and is used extensively in machine learning, data science and cybersecurity.


C++ is one of the precursors to Python and Java.  Introduced in 1983, C++ has become one of the fastest programming languages available, which is crucial in AI development.  C++ adapts well to machine learning and neural network building.

C++ supports a high level of abstraction, object-oriented principles, neural network implementations, genetic algorithms and mission critical systems.  It’s easy to learn and has a good STL collection.


A relative newcomer on the programming scene, Java is an object-oriented programming language marketed as WORA (“Write Once, Run Anywhere”) by its founder company, Sun Microsystems.  This adaptability across platforms has made it a popular choice for many programmers.

Java is portable, scalable, easy to use and easy to debug, if slightly slower than C++.  It features built-in garbage collection and is often praised for its user interaction and strong support for large-scale projects.  In terms of AI programming, Java is often used for neural networks, search algorithms, NLP and data analytics.


Lisp was created in 1959 by John McCarthy, who coined the term “artificial intelligence” and is credited as one of its founders.  The programming language inspired advancements in many of the other programming languages but Lisp itself is still extremely relevant today.

Lisp’s advantages include flexibility, fast prototyping, syntax uniformity, extensibility, machine translations, support for symbolic expressions, dynamic creation of new objects, automatic garbage collection, library of connection types, efficient coding and the possibility to run an interactive evaluation of components and recompilation of files while the program is running.  Lisp is popular in machine learning and inductive logic AI.


Another giant, Prolog was developed in 1972 and is just as well-regarded as Lisp.  Prolog uses a dedicated set of mechanisms with a small, flexible yet robust programming framework.

Prolog is known for its logic-based development, expert system implementation, easy rule implementation, list-handling mechanism, efficient pattern matching, tree-based data structuring, rapid prototyping and automatic backtracking.

Programming languages can play a role in ethical AI development

While programming languages are not the first thing people look to when they consider the ethics of AI, they do have a role to play in the issue.  The recent controversy over biased algorithms highlights the need for developers to be very careful about how they program their algorithms – and the programming language they choose can help them do so.

The need for transparent algorithms translates to a need for programming languages that allow developers to overcome the black-box issue currently plaguing the field of AI.