Addressing algorithmic bias is crucial because these systems are increasingly being used in decisions that affect people's lives.
Chiyana Simões
Chiyana Simões
September 19, 2024 — 6 minutes reading time
Demystifying algorithmic bias
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Algorithmic bias is a phenomenon that occurs when algorithms used to make decisions or predictions reflect biases present in the data or in the design of the system. Although artificial intelligence (AI ) fast food email list algorithms are designed to be objective, they can reproduce or even amplify existing discriminations and inequalities in society.
Examples of algorithmic bias have appeared in multiple areas, from recruitment systems that discriminate against candidates based on their gender or race, to credit granting algorithms that penalize certain socioeconomic groups. These problems arise when the data on which AI is trained is not sufficiently diverse or when the models are not designed to correct for these inequalities.
Addressing algorithmic bias is crucial because these systems are increasingly being used in decisions that affect people’s lives, from the workplace to the courts. If not managed properly, AI risks perpetuating injustices and inequalities, rather than addressing them.
Causes of bias in algorithms
Algorithmic bias has a variety of causes, many of which are linked to how AI algorithms are trained and developed. Below, we'll look at some of the main sources of this problem.
1. Faulty or incomplete training data
Data quality is critical to the performance of AI algorithms. When the data used to train an algorithm is biased or does not adequately represent real-world diversity, the model can learn biased patterns. For example, if a recruitment dataset has an underrepresentation of women or minorities, the algorithm might learn to value their applications less, thus replicating the biases of the past.
2. Inherent biases in mathematical models
Even if the data is of high quality, the mathematical models used to develop the algorithms can introduce biases. Some algorithms tend to favor majority trends or more frequent correlations, which can disadvantage minority groups or groups with atypical characteristics. This phenomenon is known as model bias and can arise simply because the algorithm optimizes for overall accuracy , without taking into account differences between groups.
3. Influence of human decisions on the design of the algorithm
Algorithms are not developed in a vacuum; they are created by humans who make decisions about which variables to include, how to define success or failure for a model, and how to train it. These decisions, conscious or not, can introduce bias into the system. If designers do not explicitly consider fairness and inclusion during the development process, the algorithm may end up reflecting the very inequalities we are trying to avoid.