I. Introduction to Bias in Algorithms
Artificial intelligence (AI) has rapidly become an integral part of our lives, as algorithms are used to automate a wide range of tasks and support decision-making in various domains. However, despite their potential benefits, these algorithms also pose significant risks to fundamental rights, particularly in terms of bias. Bias in algorithms refers to the tendency of these systems to produce outputs that result in disadvantage for specific groups, such as women, ethnic minorities, or people with disabilities.
II. How AI systems become biased
The development of bias in AI systems can be traced back to the presence of bias and discrimination in society and the data and texts used for developing AI models. As machines and technology are developed and used by humans, any bias present in human decision-making is transferred to the machines. Two case studies in predictive policing and offensive speech detection highlight how and where bias may occur and result in discrimination.
III. The dangers of feedback loops in machine learning
Machine learning algorithms use data to identify patterns and make predictions about new data. Some machine learning models, such as batch models or online learning models, continue to learn even after deployment. In these cases, predictions made by the system influence the data fed back into the system, leading to feedback loops. Feedback loops can result in a ‘winner takes all’ situation and cause the algorithm to overestimate results, leading to bias.
IV. The impact of feedback loops in predictive policing algorithms
Predictive policing algorithms, used to determine which neighbourhoods should be patrolled, are based on existing crime data. The observed and reported crimes in a neighbourhood can influence the police’s behaviour, leading to a self-fulfilling prophecy where the system detects more crime in a particular neighbourhood. This reinforces the system’s belief that there is more crime in that neighbourhood, leading to overpolicing in some neighbourhoods while underpolicing others.
V. Conclusion and the importance of mitigating bias in algorithms
Feedback loops can have a significant impact on algorithms, leading to bias and the creation of self-fulfilling prophecies. The simulation study in this article highlights the importance of considering the potential for bias in machine learning systems and taking mitigation measures to limit the influence of feedback loops. Ensuring that algorithms are fair and unbiased in their predictions and recommendations is crucial for protecting fundamental rights and promoting equality in our society.
