AI Fairness - How AI Affects Our Everyday Life
Artificial intelligence models are increasingly being used in key decisions, such as hiring, judicial risk assessments, and credit scoring. Existing and even reinforcing biases often emerge through the training data in the algorithms, as research can show. As AI becomes increasingly integrated into everyday life, it is crucial to ensure that these technologies work fairly and equitably. But what exactly is AI fairness, and why is it important? - Article by AI researcher Shih-Chi Ma
Definition of AI fairness
AI fairness refers to the principle that AI systems should operate without bias or discrimination to ensure fair treatment of all individuals, regardless of characteristics such as gender, ethnicity, or socioeconomic status. To achieve this, careful attention must be paid to data selection, the design of the algorithms, and the transparency of the AI processes. Fairness criteria are usually divided into group fairness and individual fairness. Group fairness focuses on statistical parity between different demographic groups, such as gender, and ensures that positive or negative outcomes are similarly distributed across these groups. In contrast, when it comes to individual fairness, people who are similar in relevant respects also receive similar results. This emphasizes the consistency of AI-driven decisions.
Impact of unfair AI systems
When AI systems exhibit bias or discrimination, it can have far-reaching consequences for individuals, companies, and society as a whole.
Social Inequity: Biased AI can perpetuate existing societal prejudices and increase discrimination against historically marginalized groups. This can lead to further economic, systematic disadvantage, and social inequalities in critical areas such as hiring, lending, and law enforcement.
Reinforcement of stereotypes: Biased AI systems can reinforce harmful stereotypes and maintain the negative perception and treatment of certain groups. The gender association of, for example, "nurse" with women and "engineer" with men in AI language models clearly shows such prejudices. These can lead to discriminatory hiring practices and social perceptions that hinder equal opportunities.
Legal implications: Companies and organizations using AI systems that lead to biased outcomes may face legal challenges and fines. Regulations such as the EU AI Act and the General Data Protection Regulation (GDPR) set strict requirements for fairness and non-discrimination in automated decision-making.
Business risks: Biases in AI systems can lead to flawed decisions, financial losses, and weakened public trust. In concrete terms: companies that fail to address bias in their AI-driven services risk reputational damage if discriminatory practices are uncovered. This is accompanied by mistrust among customers and a decline in market share and profitability.
Damage to public trust: Unfair AI systems undermine public trust in technology and politics. When citizens perceive AI as unfair or discriminatory, they are more skeptical of its use, slowing adoption and severely limiting the potential benefits of AI for various sectors, from healthcare to public administration.
Real-world Cases Highlighting AI Fairness Issues
The risks of unfair automated decision-making are not only theoretical - but have already been shown in practice in cases with serious consequences. One example is the Dutch child benefit scandal, where a fraud detection system used by the Dutch tax and customs administration disproportionately targeted parents with dual nationality or foreign-sounding names. Thousands of families have been falsely accused of fraud and asked to repay large amounts, leaving many in financial hardship. The systemic bias in the algorithm led to accusations of institutional discrimination, ultimately resulting in significant political fallout. Another well-known case is Amazon's AI-powered hiring tool, which was found to discriminate against women. The system, trained on previous applicants' resumes, learned patterns that favoured male applicants, and penalizing resumes that included women-related terms such as references to women's colleges. Amazon eventually retired the system after recognizing its inherent bias.
Current EU regulations on AI fairness
The European Union has taken significant steps to regulate AI systems to ensure they operate fairly and transparently. The EU AI Act introduces a risk-based framework, categorizing AI applications into four categories based on their potential risks. High-risk AI systems, such as those used in hiring, credit scoring, and public administration, must meet strict requirements for fairness, transparency, and accountability. Companies and organizations using these systems must demonstrate that their AI models do not contain discriminatory biases and that failure to comply with these requirements may result in significant fines.
Alongside the AI Act, the General Data Protection Regulation (GDPR), in effect since 25 May 2018, also plays a crucial role in addressing fairness in AI. The GDPR requires that personal data must be processed lawfully, fairly, and transparently, meaning AI systems based on personal data must not lead to discriminatory or unfair results. In addition, individuals have the right to challenge automated decisions with significant impact, strengthening accountability in AI-driven processes.
Conclusion: The Imperative of AI Fairness
AI fairness is not just a technical issue, but requires a cultural shift in how we develop, deploy, and evaluate AI. Cases like the Dutch child support scandal and Amazon's hiring algorithm have shown us what happens when fairness is disregarded—real people are suffering, institutions are losing credibility, and public trust in AI is waning. Regulations such as the EU AI Act and the General Data Protection Regulation are an important step towards monitoring and mitigating these risks, but true AI fairness requires not only a regulatory requirement, but an imperative, a basic social motivation.
