Recently, one of the questions that almost kept me awake at night was the level of AI knowledge or understanding a non-IT person (including myself) should have.
AI has been with us for many years already, without most of us realizing it, but the AI explosion started only a couple of years ago, triggered by GenAI. We find ourselves in a situation where everybody, for good or bad, is talking about it, as if, all of a sudden, everyone became an AI expert.
Actually, are we really AI literate? Or do we simply want to have a say to avoid being left out of the current hot debate? And what is AI Literacy in the first place?
A new literacy comes roughly every decade: computer literacy, information literacy, financial literacy, media literacy, digital literacy, data literacy, and here we have it—AI literacy.
The fact is that, given its “novelty”, there is not yet an established framework for AI Literacy. It is an ongoing talk. If you are interested in learning about it anyway, to save you time, you can see the very informative (working) framework by Long and Magerko below.

I prefer to channel the focus about it from the following perspectives:

– If you put yourself in the shoes of your (potential or actual) employer, what kind of AI literacy would they expect from employees?
Do they care if you acquire AI literacy per se —let’s say, as an informed citizen who knows, for instance, the ethical implications of GenAI (please don’t take me wrong, they are important)— more than they care about you becoming more efficient in the workplace?
Those who master the art of teaming up with AI —and actually any other tool that makes work more efficient— will undoubtedly remain relevant.

– As someone at the age of choosing a career, what is that you need to understand the most to make sure you can land your very first job sooner than the average?
Exactly! The more you understand AI’s strengths and limitations, the more strategic you will become in allocating time to mastering the skills that machines cannot compete with.

How AI literate we should become is probably a debate that will last for some time. It is a discussion that might never be settled, considering we don’t yet know how much AI will evolve. One thing is certain: in this age of an increasing number of pseudo-experts, one has to learn to slow down and see things critically. One has to put extra effort into distinguishing facts from speculation.

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Long and Magerko’s take on AI Literacy (extract)

{“ What is AI Literacy? Competencies and Design Considerations; Duri Long and Brian Magerko; 2020“}

Definition: set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.

Competencies

Competency 1 (Recognizing AI) Distinguish between technological artifacts that use and do not use AI.

Competency 2 (Understanding Intelligence) Critically analyze and discuss features that make an entity “intelligent”, including discussing differences between human, animal, and machine intelligence.

Competency 3 (Interdisciplinarity) Recognize that there are many ways to think about and develop “intelligent” machines. Identify a variety of technologies that use AI, including technology spanning cognitive systems, robotics, and ML.

Competency 4 (General vs. Narrow) Distinguish between general and narrow AI

Competency 5 (AI’s Strengths & Weaknesses) Identify problem types that AI excels at and problems that are more challenging for AI. Use this information to determine when it is appropriate to use AI and when to leverage human skills.

Competency 6 (Imagine Future AI) Imagine possible future applications of AI and consider the effects of such applications on the world.

Competency 7 (Representations) Understand what a knowledge representation is and describe some examples of knowledge representations. (for example, in AI, an image is represented as a matrix of float values in which each value represents the color of a pixel)

Competency 8 (Decision-Making) Recognize and describe examples of how computers reason and make decisions.

Competency 9 (ML Steps) Understand the steps involved in machine learning and the practices and challenges that each step entails. (ML is not fully automated, they require human intervention)

Competency 10 (Human Role in AI) Recognize that humans play an important role in programming, choosing models, and fine-tuning AI systems.

Competency 11 (Data Literacy) Understand basic data literacy.

Competency 12 (Learning from Data) Recognize that computers often learn from data (including one’s own data).

Competency 13 (Critically Interpreting Data) Understand that data cannot be taken at face-value and requires interpretation. Describe how the training examples provided in an initial dataset can affect the results of an algorithm.

[Robots and AI]

Competency 14 (Action & Reaction) Understand that some AI systems have the ability to physically act on the world. This action can be directed by higher-level reasoning (e.g. walking along a planned path) or it can be reactive (e.g. jumping backwards to avoid a sensed obstacle).

Competency 15 (Sensors) Understand what sensors are, recognize that computers perceive the world using sensors, and identify sensors on a variety of devices. Recognize that different sensors support different types of representation and reasoning about the world.

Competency 16 (Ethics) Identify and describe different perspectives on the key ethical issues surrounding AI (i.e. privacy, employment, misinformation, the singularity, ethical decision making, diversity, bias, transparency, accountability).

Competency 17 (Programmability) Understand that agents are programmable