Fairness in AI: How can we address bias to build equitable systems?

One of the principles of Responsible AI is fairness, which refers to the ability to ensure that AI systems do not systematically favor or discriminate against individuals or groups. In this article, we explore some methods for ensuring and measuring fairness in data and AI systems.

D.H. Lawrence stated that ethics, equity, and justice remain constant despite the passage of time. These are enduring concerns, though social and cultural contexts can shape our definitions of what is deemed ethical, equitable, and just. In today’s world, AI fairness has become a key issue, especially as we address bias and strive to design equitable systems. The challenge of AI fairness, bias, and equitable system development reflects our ongoing pursuit of justice in the digital age.

When applying these concepts to artificial intelligence (AI) systems, we must recognize them as universal principles. This understanding leads to the idea of responsible AI, which defines essential principles for ethical design, development, and implementation.One key principle is fairness, which emphasizes the importance of ensuring that AI systems do not unjustly favor or discriminate against individuals or groups.

Check out here some specific types of bias that can affect fairness in AI

Fairness is maintained through several key pillars:

Equality of opportunity, which means creating systems that avoid unjust biases in access or participation for various groups.
Equality of performance, aimed at ensuring that systems function fairly regardless of individuals’ protected characteristics.
Bias control, which encompasses strategies for identifying and addressing biases throughout the system’s lifecycle, employing specific methods such as guardrails.

AI also has biases

Bias is an inherent aspect of human life. It develops from our experiences, surroundings, and the social norms that mold us. These biases influence our decisions; specifically, when we design and train artificial intelligence systems ,biases can seep into the technology.

Let’s take an example: a facial recognition system trained mostly with images of white people. This will lead to more errors in identifying people from other ethnic groups. The same goes for hiring algorithms: if the employee history is dominated by men, the AI may learn to favor candidates with similar characteristics perpetuating gender disparities without anyone explicitly programming it to do so.

Bias in AI is both a technical problem and a social problem. It can creep into the data, but also into the decisions made at every stage of the development cycle: from problem definition to model design, variable selection or the way results are evaluated. Its consequences can be serious, as it can reinforce existing stereotypes and negatively affect the lives of people who are trapped in a system that does not recognize them fairly.

This is why it is critical for AI developers to go beyond creating efficient models: they must actively question what biases they may be incorporating, even unconsciously. To this end, having diverse teams is key, as they bring different perspectives and help identify blind spots that might go unnoticed in homogeneous environments.

How to ensure fairness in data?

Data fairness is the first step in ensuring that AI systems are fair. If the data on which a model is trained is biased or unbalanced, the model will reflect and amplify these biases, regardless of the efforts made in later stages of development.

The initial step in detecting bias in the data is to perform a statistical parity analysis. This involves making sure that the model results are evenly distributed among different demographic groups, with no one group being systematically favored or disfavored.

Consequently, representation audits should be conducted, verifying that data from minority groups are not under- or misrepresented. This may require expanding the data to include more examples of these groups or reviewing the model characteristics to ensure that they do not inadvertently favor one group over another.

Another important approach is correlation analysis, which identifies whether there are undesirable relationships between sensitive attributes (such as gender, ethnicity or age) and other characteristics of the data set.

Once a bias in the data has been identified, the next step is to mitigate it. This may include techniques such as rescaling data to balance underrepresented groups, eliminating biased variables, or augmenting data by generating additional examples without distorting the nature of the data. However, it is important to remember that while data corrections can reduce bias, they alone are not sufficient; training practices and constant monitoring are just as crucial.

How can we ensure fairness in the models?

AI models themselves can also be a source of bias. Even if the data is unbiased and representative, the model may introduce bias during its training, particularly if the techniques and algorithms used are not designed to mitigate disparities between groups. Often, this is due to how the algorithms weight features during the learning process or how they interpret patterns found in the data.

The first step in assessing potential biases in the data is to quantify statistical parity, which involves checking that the rates of positive results are similar among the different protected groups and ensuring that none are systematically favored or disadvantaged.

To address biases in models, techniques such as feature reweighting can be applied, in which the weights of model features are adjusted to prevent sensitive attributes (such as gender or ethnicity) from having a disproportionate impact on decisions. Another option is to adjust the decision thresholds, so that the model does not favor one group over another based on one variable, but allows for more equitable decisions between groups.

Fairness in generative AI

Unlike traditional AI, which often focuses on specific use cases, generative AI systems, such as large language models, are implemented in very diverse contexts, which magnifies the risk of generating biased outputs.

For example, a generative model that creates job descriptions could unintentionally reflect historical biases, such as associating leadership with men and caring with women, thus perpetuating stereotypes and inequalities.

The main problem is not only the bias in the data, but how these models, by maximizing their utility (e.g., seeking greater interaction, clicks or user satisfaction), can generate content that, although apparently neutral, reinforces harmful social norms or disproportionately benefits certain groups.

This highlights the need to go beyond detecting biases in the data and actively guide the model’s “emergent values” to avoid propagating harmful narratives, privileging certain points of view or marginalizing underrepresented voices.

To ensure equity in generative AI, it is necessary to conduct ongoing audits of the data and results. This involves adjusting model objectives to include equity constraints, testing the model in a variety of real-world contexts to identify hidden disparities, and, most importantly, involving ethicists, policymakers, and impacted communities in the design and evaluation process..

Fairness measurement: Overview of metrics

Equity metrics help to identify biases in the results, as well as guide the process of improving and adjusting the models. Some metrics are:

Statistical Parity Guarantees equal acceptance rates across various demographic groups. If one group significantly differs in scores compared to another, this metric suggests a potential bias may be present.
Equal Opportunity Ratio. Measures whether qualified individuals from various groups have an equal likelihood of receiving a positive prediction. This is especially useful in contexts such as hiring or credit, where opportunities should be distributed fairly.
Predictive parity. Ensures that predictions are equally accurate across groups. For instance, if a model predicts loan repayment rates, predictive parity guarantees that accuracy remains consistent across demographic categories.
Disparate impact analysis. Evaluates if the results of an AI system impact one group more than others by comparing impact thresholds.

Although these metrics provide a way to address biases, it is common for them to conflict. For example, optimizing for equality of opportunity could compromise predictive parity.

Achieving true equity requires finding a balance between these metrics, taking into account the specific context and societal values guiding the use of AI.

In addition, metrics alone are not enough. It is vital to perform audits to detect biases or unwanted patterns in the model results, and to incorporate human oversight during the evaluation and adjustment process to identify problems that could not be detected by quantitative metrics.

Conclusion: socio-technical analysis!

AI systems do not exist in a vacuum; they interact with the social, cultural, and economic contexts in which they operate. Therefore, a holistic approach is necessary to identify hidden power dynamics and unintended consequences that might go unnoticed in a purely technical assessment.

This is where sociotechnical analysis comes in, emphasizing the need for community engagement, contextual assessments, and interdisciplinary collaboration that combines technical, ethical, and social scientific expertise.

Incorporating these methods throughout AI development guarantees that systems are not only efficient but also fair and in tune with societal needs. This technical challenge becomes a socio-technical one, where recognizing biases empowers us to create much more equitable and inclusive models.