Bias, Fairness, and Model Behaviour
- Keira Redmond
- Feb 2
- 2 min read
Updated: 3 days ago
by Niharika Deokar
Artificial Intelligence (AI) systems, including machine-learning-powered ones, are increasingly influencing everyday decisions, from recommendations and automation to decision-support tools. While these systems can deliver significant value, they also inherit patterns from the data and rules on which they are built. Handling biases has become more important in the design and engineering of artificial intelligence (AI) systems due to their increasing applications in everyday lives [1].
Bias
As AI/Machine Learning systems are trained on real-world data that often reflects existing inequalities or incomplete perspectives, they can unintentionally produce biased or unfair outcomes. This can be tackled and avoided using better data choices, design decisions, or modelling assumptions during development.
How Data Bias Shows Up in AI Models
Bias can emerge at multiple stages, including:
Data collection, where some groups may be underrepresented
Historical data, which may reflect past inequalities
Feature selection and labelling, which can unintentionally favour certain outcomes
Data
Data is a foundational component of both AI and Machine Learning systems. The quality, scope, and representativeness of data directly influence how systems behave in real-world settings. Purpose-driven data collection, clear documentation of data sources, and transparency around limitations help reduce unintended bias. When data practices are responsible, AI systems are more likely to produce outcomes that are consistent, reliable, and equitable.
Fairness
Fairness in AI focuses on ensuring that systems behave equitably across different users and contexts. While fairness can be defined in different ways depending on the use case, the underlying aim is to minimise unjustified differences in outcomes.
Measuring and mitigating unfair outcomes
Building fairer AI systems involves continuous evaluation and improvement. Some common practices include:
Assessing system performance across different user groups
Improving data representativeness and balance
Applying fairness-aware evaluation metrics
Introducing human oversight for high-impact decisions
Fairness should be considered throughout the AI lifecycle from design and data collection to deployment and monitoring.
Model Behaviour
AI system design often involves balancing multiple priorities, including performance, fairness, explainability, and usability.
Trade-offs between accuracy, fairness, and usability
Highly accurate models may be less interpretable, while more transparent systems may sacrifice some performance. Similarly, improving fairness may require trade-offs in optimisation or system complexity. Responsible AI development acknowledges these trade-offs and makes intentional, context-aware decisions based on risk, impact, and user needs. The goal is manage trade-offs responsibly.
Final Thoughts
Addressing biases in AI/Machine Learning algorithms and systems is crucial to ensure fairness and transparency in models [2]. By understanding how bias arises, actively measuring and mitigating unfair outcomes, and thoughtfully balancing design trade-offs, organisations can develop AI systems that better serve diverse users. Responsible AI is about ensuring that the systems we build align with ethical principles and real-world expectations.
References
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