Fairness through unawareness

Definition

Fairness through unawareness is a principle in machine learning that suggests that one can achieve fairness by not using sensitive attributes, such as race or gender, in the model's decision-making process. This approach assumes that if these attributes are not included in the model, it can prevent biases that may arise from them. However, this method often overlooks the underlying systemic issues and does not address existing biases in the data used to train the model.

5 Must Know Facts For Your Next Test

1. Fairness through unawareness is often criticized because simply ignoring sensitive attributes does not eliminate bias in the training data.
2. This approach may lead to fairness in outcomes but fails to address systemic inequalities present in the society or dataset.
3. Ignoring sensitive attributes can create a false sense of fairness, leading stakeholders to believe that bias has been adequately addressed.
4. Fairness through unawareness does not guarantee that the algorithm will treat all individuals equally, as hidden biases might still persist in other features.
5. Alternative fairness strategies, such as counterfactual fairness, provide a more robust framework for assessing and mitigating bias in machine learning systems.

Review Questions

• How does fairness through unawareness attempt to achieve fairness in machine learning, and what are its limitations?
  • Fairness through unawareness tries to promote fairness by excluding sensitive attributes like race or gender from the model's training process. However, its limitations are significant; it does not account for biases embedded in the data itself or how these attributes might still influence other features. This can lead to misleading conclusions about the fairness of an algorithm since ignoring these attributes doesn't necessarily eliminate existing biases.
• Compare and contrast fairness through unawareness with counterfactual fairness. What makes counterfactual fairness a more desirable approach?
  • While fairness through unawareness simply ignores sensitive attributes to achieve fairness, counterfactual fairness goes further by considering how predictions would change if sensitive attributes were different. Counterfactual fairness is considered more desirable because it actively evaluates how decisions would vary under different circumstances, addressing potential biases more comprehensively. This helps ensure that an algorithm's outcomes are genuinely fair across different demographic groups.
• Evaluate the effectiveness of using fairness through unawareness in real-world applications. In what ways might this approach fail to address societal biases?
  • Using fairness through unawareness in real-world applications can be ineffective because it may create an illusion of fairness while systemic biases persist in the data used for training. For instance, if historical data reflects societal inequalities, merely omitting sensitive attributes won't resolve issues arising from those inequalities. This approach can inadvertently reinforce existing disparities rather than mitigate them, leading to outcomes that do not reflect true equity or justice in decision-making.

Related terms

Algorithmic Bias: A systematic and unfair discrimination in algorithms that leads to unfair treatment of certain groups based on their characteristics.

Disparate Impact: A legal doctrine that refers to policies or practices that may be neutral on their face but have a disproportionate adverse effect on a protected group.

Counterfactual Fairness: A fairness criterion where an algorithm's predictions should remain unchanged under counterfactual conditions where sensitive attributes are different.