AI now occupies a central place in our societies, influencing a wide range of sectors, from healthcare and education to marketing and legal systems.
However, far from being neutral tools, AI systems can reproduce or amplify existing biases, or even create new ones. These systematic distortions can impact decisions, behaviours and interactions, thereby undermining fairness and the trust that users place in these technologies.
In this article, we analyse the different types of biases present in AI systems, their potential consequences, and best practices to identify, mitigate and govern them responsibly.
1. What is an AI-related bias?
According to ISO/IEC 24027:2021, bias refers to a systematic difference in the treatment of objects, persons or groups compared to others.
In AI, biases may arise at all stages of the system lifecycle:
- Data collection and selection,
- Algorithm design,
- Model parameterisation,
- Interpretation and use of results.
These biases can therefore undermine the fairness of decisions and infringe upon fundamental rights.
2. The different types of bias in AI systems
Biases in AI systems can take multiple forms and originate from various sources, making their identification and management complex.
Algorithmic biases
Algorithmic biases arise when an automated decision-making system produces systematically imbalanced outcomes. They may be linked to:
Methodological choices
Example: automated recruitment algorithm
A CV-screening system is trained using data from past recruitment processes. If, methodologically, designers choose to predict the “ideal profile” based on the company’s historical decisions, the algorithm may favour candidates resembling those previously recruited (for example, predominantly men). This methodological choice reproduces and amplifies an existing bias, systematically excluding certain groups despite their qualifications.
Socio-historical legacies
Example: credit scoring system
A financial risk assessment algorithm relies on historical loan data. However, these data reflect decades of structural discrimination (differentiated access to credit based on social or geographical background). By learning from this socio-historical legacy, the system tends to assign lower scores to certain groups, not due to their actual creditworthiness, but because of past inequalities embedded in the data.
Technical or computational constraints
Example: facial recognition
A facial recognition system is trained on a dataset largely composed of light-skinned faces, as such data are more readily available and less costly to collect. This technical constraint leads to poorer performance for individuals with darker skin tones, generating higher error rates and systematic bias in the results.
Human cognitive biases
The cognitive biases of designers, developers and users also influence the design and interpretation of AI-generated outputs. Among the most common cognitive biases are:
- Confirmation bias: the tendency to seek information that confirms pre-existing beliefs.
- Anchoring bias: the influence of an initial piece of information on subsequent decisions.
- Representativeness bias: excessive interpretation of a situation or group based on a limited sample.
Data-related biases
Biases may also originate from the datasets used to train AI models. If these datasets are poorly represented, incomplete or imbalanced, the system may generate erroneous results for certain groups.
For example, a computer vision model primarily trained on images taken in sunny conditions may fail when confronted with images captured in extreme weather conditions.
3. What are the consequences of AI-related biases?
Biases present in AI systems are not without effects. Depending on when and how they intervene throughout the AI lifecycle, they can alter the quality and proper functioning of systems, with repercussions for both individuals and organisations that design or deploy them:
- Discrimination: biased decisions may lead to systemic discrimination, affecting groups of people based on criteria such as gender, ethnic origin or age.
- Loss of trust: users may lose trust in AI systems if they perceive them as unfair or opaque.
- Reputational damage and legal liability: organisations deploying biased systems risk legal action, fines and reputational harm.
4. How to identify and detect AI-related biases?
To mitigate biases, it is crucial to implement a robust framework for identifying and managing biases throughout the AI systems lifecycle.
This involves:
Establishing a bias detection framework
From the design stage, it is essential to define a framework including:
- Statistical analyses,
- Human rights impact assessments,
- Human reviews and internal audits.
Analysing sources of bias
Analysing training, testing and validation data helps identify systemic biases, particularly those affecting:
- Marginalised groups,
- Persons with disabilities,
- Underrepresented populations.
Testing, auditing and validating systems
After deployment, it is necessary to carry out:
- Testing under real-world conditions,
- External audits,
- Continuous assessments of fairness and robustness.
These also help detect unanticipated biases.
Using fairness indicators and tolerance thresholds
The following indicators help measure bias objectively:
- Statistical parity,
- Equality of error rates.
These indicators must be adapted to the context of use and regulatory requirements.
Assessing stakeholder impact
It is important to conduct an impact assessment to understand the potential effects of the system on end users and groups affected by biases.
5. Bias prevention and mitigation measures
Preventing and mitigating biases is essential to ensure that AI systems make fair decisions. Key measures include:
Defining objectives and risks
Organisations must clearly define the objectives of their AI, as well as the potential risks associated with biases in its design and use.
Analysing data representativeness and quality
Ensuring that the data used to train AI systems are representative of all users and relevant variables helps limit data-related biases.
Applying bias mitigation techniques
Techniques such as data balancing or algorithm adaptation can be used to reduce biases. These methods adjust systems to avoid discriminating against certain groups.
Assessing fairness and performance gaps
Evaluating fairness and measuring performance gaps between different groups are essential to ensure that the system is fair.
Robustness testing and continuous validation
Testing system robustness and performing regular checks help detect potential unanticipated biases after deployment.
Documenting Datasheets and Model Cards
Transparent documentation of dataset and model characteristics ensures traceability and improves understanding of potential biases.
6. Best practices for bias management for AI users
In addition to measures taken by AI system operators, users must also adopt a responsible approach to AI by being aware of biases and taking steps to manage them. Best practices include:
- Maintaining human oversight: it is important to oversee AI-generated decisions, especially in sensitive contexts.
- Reporting errors or biased content: vigilance and reporting of identified biases in used systems are essential.
- Becoming aware of one’s own cognitive biases: users should recognise personal biases that may influence interactions with AI.
- Comparing multiple tools and perspectives: using several AI tools and diversifying information sources helps avoid a biased view.
Managing AI Biases: Balancing Fairness and Performance
AI-related biases represent a major challenge for fairness and justice in our societies. If AI systems are poorly designed or misused, they can lead to unfair, discriminatory and harmful decisions.
However, through rigorous identification, proactive bias management and continuous monitoring, it is possible to reduce distortions and make AI more fair, reliable and beneficial for all.
As operators and users, it is our responsibility to ensure that AI serves ethical objectives and respects human rights.
Steering and Managing AI Biases with an AIMS
Managing AI biases cannot rely solely on ad hoc controls. It requires a structured, traceable and continuous approach, integrated throughout the entire lifecycle of AI systems.
Do you want to identify, mitigate and manage biases in your AI systems while ensuring their effectiveness and fairness?
Adopt the Naaia AIMS today to ensure fair, responsible and high-performing AI.