Description: Algorithmic fairness refers to the principle of ensuring that algorithms operate fairly, without biases that may negatively affect certain groups of individuals. This concept is fundamental in the development and implementation of artificial intelligence (AI) and machine learning systems, where automated decisions can significantly impact people’s lives. Algorithmic fairness seeks to ensure that all individuals are treated fairly, regardless of their race, gender, sexual orientation, socioeconomic status, or other personal characteristics. This involves not only eliminating biases in the data used to train algorithms but also creating models that are transparent and accountable. The relevance of algorithmic fairness has grown in the last decade as AI has been integrated into various areas such as hiring, criminal justice, and healthcare. The lack of algorithmic fairness can lead to the perpetuation of existing inequalities and the creation of new forms of discrimination, highlighting the need for an ethical approach in the design and implementation of algorithmic technologies.
History: The term ‘algorithmic fairness’ began to gain attention in the 2010s, in a context where the use of algorithms in critical decision-making was rapidly expanding. Events such as the Cambridge Analytica scandal in 2016 and the increasing scrutiny over the use of algorithms in criminal justice and hiring led to a greater focus on AI ethics. In 2018, the first conference on algorithmic fairness was held in New York, marking a milestone in the academic and public discussion on the topic.
Uses: Algorithmic fairness is applied in various areas, including hiring, where the aim is to avoid biases in personnel selection systems; in criminal justice, to ensure that crime prediction algorithms do not discriminate against certain groups; and in healthcare, where algorithms are used to diagnose diseases and allocate resources. It is also applied in the financial sector to ensure that credit models do not perpetuate racial or socioeconomic inequalities.
Examples: An example of algorithmic fairness is the use of algorithmic auditing tools that analyze AI systems to identify and correct biases. In hiring, companies have implemented selection processes that use AI to fairly assess candidates. In the judicial system, certain algorithms have faced criticism for their biases, leading to a debate about the need to reform their use.
