True, Artificial Intelligence is a subfield of Computer Science. It is also true that if you’re a techie trained in the computer sciences, AI is a very lucrative area to work in. But the reverse may not be true: if you work in AI, you don't necessarily have to be a techie, for there are a host of other roles in this field.

Let’s explore the various career opportunities for non-techies in Artificial Intelligence.

• AI Ethicist: This is the person who questions the fundamental issues of fairness and bias in every AI team that is building algorithms that will impact a large number of people. As predictive models begin making important decisions, from hiring decisions to loan decisions, it becomes paramount to ensure that they are built on the right values. Algorithms are prone to mirroring the bias that we as humans have, but given the pace and reach of technology, the implications of algorithmic bias can be exponentially higher than those of human bias. Ethicists promote accountability and responsible use of AI. Anyone who isn't afraid to ask the tough moral, philosophical and critical questions is fit to be an AI ethicist, regardless of their academic background.

Read also: How bias in AI systems affect various domains

• Linguist and writer: The rising scope and applications of conversational AI have made this new role prominent in the field. The language expertise of linguists and writers is required while building chatbots and machine translation tools. Language experts also occupy important positions in teams conducting Natural Language Processing (NLP) research.

Read also: A day in the life of an AI translator, AKA editor or copywriter

• Domain Expert: There are two sides of any AI system: the software side and the domain side. While the IT expert understands the nuances of machine learning systems, it is the domain expert who will bring a deep understanding of the domain within which the system will operate. For instance, AI solutions built for industries such as agriculture, healthcare, finance etc. will require subject matter expertise in each of these areas. 

Read also: Domain expertise: The key ingredient for successful AI deployment

• UX designer and visual storyteller: Big data and Artificial Intelligence mutually support each other: in order to derive maximum insights from data, visual storytelling is a powerful tool heavily used in AI projects. Data visualisation lies at the intersection of data science and graphic design. UX design is another area that uses the services of visual designers, requiring design thinking and user-centric design.

Read also: Career advice for AI professionals – straight from the industry

• Analytics translator: This is the person who bridges the gap between business and data scientists. Translators are not necessarily dedicated analytics professionals, and they don't need deep technical expertise in programming or data modeling. As per estimates by McKinsey Global Institute, the demand for analytics translators may reach 2-4 million by 2026 in the United States alone.

Read also: AI skilling does not necessarily have to come from an engineering background

The world of Artificial Intelligence is very vast and that it's only reserved for engineers and IT geeks is a misconception – a STEM degree is not a passport to career in AI. #AIForAll means that this is an inclusive field, and if you are truly passionate about AI, there is a place for you.

Photo by Vlada Karpovich from Pexels