Bias

Bias is a systematic distortion or tendency that deviates judgement, perception or data processing from objectivity. It is a non-random, but predictable, error.

In the creative sectors and marketing, the term most often refers to cognitive biases (automatic mental prejudices that influence consumer behaviour) or data/algorithmic biases (distortions that influence the results of research, algorithms and artificial intelligence).

What is it used for / Why is it important

Understanding the concept of bias is fundamental for two main reasons:

1. For persuasion (Cognitive Biases): Knowing the public's cognitive biases is the basis of persuasive copywriting and UX design. It allows for structuring messages and interfaces that "hook" people's instinctive decision-making mechanisms (e.g., sense of scarcity, social proof) to guide them towards an action.

2. For objectivity (Data/Algorithmic Biases): Recognising one's own biases (personal or cultural) and those present in the data is vital to avoid them. It ensures that market research is correct, that A/B tests are valid and that AI tools do not produce distorted, discriminatory or ineffective results.

When is it used / In what context is it useful

The concept of bias is omnipresent and applies to almost all our sectors:

• Copywriting and Marketing: To exploit biases such as Anchoring (presenting a high price first to make the low one seem convenient) or Social Proof (using reviews and testimonials).

• UX/UI Design: To design purchasing flows that use Scarcity ("Only 2 rooms left") or the Default Bias (pre-selecting the preferred option).

• Market Research: To avoid Selection Bias (choosing a non-representative sample of interviewees) or Confirmation Bias (only seeking data that confirms one's initial thesis).

• Artificial Intelligence (AI): In verifying the datasets used to train an AI, to prevent the algorithm from learning and replicating human prejudices (e.g., gender or racial bias).

Practical example

An e-commerce website displays the message: "Over 5,000 people have already purchased this product this month". This is not just a piece of data, but the activation of the Bandwagon Effect.

Faced with this information, our brain instinctively thinks: "If everyone is buying it, then it must be a good product". We trust the judgement of the crowd to reduce the effort of choice, even if that product may not be the best for our specific needs.