Fairness in AI refers to the equitable treatment of all individuals by AI systems, regardless of their individual characteristics. It is about ensuring that AI systems don’t reproduce, perpetuate, or amplify societal biases, and deliver equitable outcomes for all users.


Imagine you’re playing a game with your friends. It would not be fun if the game always favored some friends over others just because of where they lived or what they liked to eat, right? That’s what fairness in AI tries to avoid - making sure everyone gets a fair go.

In-depth explanation

In the context of artificial intelligence (AI) and machine learning (ML), ‘Fairness’ is a complex and multilayered concept that aims to ensure unbiased, equitable and justifiable outcomes from AI systems. It primarily engages with biases that could be present in the data, models, and overall system. Bias can creep into AI systems in various ways, such as through skewed datasets, unintended feedback loops, or even just through the way the AI system is set up.

Fairness necessitates that the predictions made by an AI system do not disproportionately advantage or disadvantage certain groups based on specific attributes such as race, gender, age, or any other characteristic. In reality, this can be challenging due to the ‘fairness-binary tradeoff’, which is the tension between optimizing an AI system to produce the most accurate predictions and ensuring these predictions are fair and unbiased.

There are various mathematical definitions and methods employed to ensure fairness in AI. These can largely be categorized into two groups: individual fairness and group fairness. Individual fairness suggests that similar individuals should be treated similarly by the AI system, while group fairness requires that the AI model’s outcomes be similar across different groups.

Procedural fairness is another crucial aspect that focuses on the fairness of the process followed rather than only the end results. It ensures that the decision-making process is transparent, responsible, and can be interpreted easily by humans.

Lastly, it’s important to note that fairness in AI is a complex issue and sometimes requires trade-offs. For example, one may need to sacrifice some degree of predictive accuracy to make the model’s outcomes more fair. Hence, fairness in AI often comes down to ethical decisions that need to be made based on the context and potential impact of the AI system.

Bias, Machine Learning (ML),, Algorithmic Bias, Procedural Fairness, Individual Fairness, Group Fairness, Fairness-Binary Tradeoff, Interpretability, Transparency, Ethical AI.