Machine learning, a branch of artificial intelligence, has revolutionized various sectors by allowing systems to acquire knowledge from data and autonomously make judgments or forecasts with minimal human involvement. Although ML has great potential it’s not without its difficulties. One of the major concerns that has already manifested (and to a significant extent), is the potential to result in unjust or erroneous conclusions i.e., ML bias. Gaining a comprehensive understanding of the various forms of bias in ML, their origins, the possible adverse effects they can have on users and how to remedy such biases, is essential for the development of AI systems that are fair and dependable. This article explores the several forms of bias in ML, their sources, methods to prevent them, and the potential consequences for users in the future.
Categories of Machine Learning Bias
Bias in ML can be categorized into several unique types, each with its own characteristics and underlying causes:
- Sample bias refers to the situation where the data used to train a model does not accurately represent the population it is intended to be applied to. For example, if a facial recognition system is primarily trained on photos of persons belonging to a specific ethnic group, it may exhibit subpar performance when identifying individuals from different ethnic origins. Sample bias causes models to be accurate only for a specific subgroup of the population, leading to outcomes that are distorted. It often manifests in the form of racial, gender or socioeconomic bias. In 2021, a UK Uber Eats driver’s account was removed from the app following several instances of mismatch. The driver, who claimed that the increased verification checks implemented by Uber Eats deprived him of his income, reached an out-of-court settlement with Uber Eats earlier this year.
- Measurement bias occurs when the characteristics or variables utilized in the model are wrongly measured or documented. This can occur due to erroneous data collection procedures or instruments. For example, if an algorithm makes predictions about student achievement only based on standardized test scores, without taking into account socioeconomic considerations, it could create bias by underestimating the potential of children from underprivileged families.
- Algorithmic bias refers to the inherent bias that is introduced by the algorithm itself. It happens when the algorithm’s design or assumptions naturally prefer particular outcomes. For example, if an algorithm is created to forecast criminal behavior, it may inaccurately assess the likelihood of specific demographic groups being at danger if it relies on erroneous historical data that is influenced by cultural prejudices.
- Selection bias refers to the situation when the data used to train a model is not randomly selected, resulting in a sample that does not accurately represent the population. This frequently occurs when specific demographics are not adequately represented or are deliberately removed from the dataset. For instance, if an ML model designed to evaluate loan approvals is predominantly trained using data from persons with high incomes, it may exhibit worse performance when evaluating loan applications from individuals with lower incomes. According to a study conducted by Stanford University in 2023, Black taxpayers have a far higher likelihood of being audited compared to taxpayers of other racial backgrounds, with a ratio of approximately three-to-five times the likelihood.
- Confirmation Bias occurs when the model’s outputs are interpreted in a manner that validates pre-existing ideas or hypotheses. For example, if a predictive model repeatedly generates outcomes that match the designer’s anticipations, it could strengthen biased choices instead of questioning them. The adverse is also true, and confirmation bias is oftentimes difficult to overcome. In a study conducted by neuroscientists Jonas Kaplan, Sarah Gimbel & Sam Harris, in which they connected students with liberal political beliefs to a functional magnetic resonance imaging scanner, the researchers read out statements that the participants had previously expressed agreement with and then presented contradictory evidence, and measured the students’ brain activity. The amygdala was activated when political positions were refuted, while non-political claims did not produce any effect. This is the same region of the brain that is activated when a tiger attacks, triggering a “fight-or-flight” response. Our System 1 is propelled by the amygdala, which simultaneously suppresses the prefrontal cortex, which controls our System 2. As can be gleaned from this example, confirmation bias is particularly prevalent in situations where we possess a preconceived notion.
Factors contributing to bias in machine learning
The presence of bias in ML models typically stems from three main sources: the data used to train the model, the technique employed, or the specific context in which the model is implemented. Primary factors contributing to this issue are (i) historical bias in data; (ii) insufficient or biased data; and (iii) model assumptions:
(i) Historical Bias in Data: The data utilized for training ML models frequently mirrors pre-existing inequities and prejudices present in society. Historical hiring data may exhibit a predilection for male candidates in specific industries, resulting in an ML model that sustains gender bias in hiring determinations.
(ii) Insufficient or Biased Data: When datasets lack necessary information or exhibit a lack of diversity, they are unable to encompass the broad spectrum of variables required for a precise model. This can lead to projections that are influenced by prejudice and do not accurately apply to the wider population.
(iii) Model Assumptions: Algorithms rely on assumptions that may not be universally valid. If these assumptions are incorrect or influenced by bias, the model’s results will accurately represent these flaws or biases.
In addition, biased predictions can sometimes lead to a feedback loop, where the biased output of an ML model affects future decisions, thus strengthening the bias. For instance, a prejudiced predictive policing algorithm could cause a rise in police activity in specific regions, leading to a higher number of arrests and further distorting the data utilized to train the model.
Mitigating Bias in Machine Learning
Addressing bias in ML requires a comprehensive approach that incorporates both data-centric and algorithmic solutions.
Ensuring the diversity and representativeness of training datasets is essential. This may entail the acquisition of supplementary data, the utilization of synthetic data to bridge any gaps, or the adjustment of existing data weights to more accurately represent underrepresented demographics.
Regularly doing bias detection and auditing of ML models is crucial. Methods such as fairness testing, sensitivity analysis, and differential impact analysis can be utilized to detect and measure bias in models. These audits should be conducted continuously, particularly when models are revised or used in different settings.
Algorithmic fairness is integrating fairness criteria into algorithm design to mitigate prejudice. Algorithms that are aware of their levels (or lack) of fairness aim to mitigate uneven impact on various demographic groups by assuring equitable performance of the model across these categories.
Enhancing the openness and explainability of ML models enables stakeholders to comprehend the decision-making process and detect potential biases. Explainable AI (XAI) methods have the ability to enhance the interpretability of intricate models, allowing users to closely examine and question biased results.
Human Oversight: The inclusion of human oversight in the decision-making process can often reduce bias. While it is not easy, ML models can be reviewed and adjusted by human specialists, especially in critical situations like healthcare, criminal justice, and finance.
The Influence of Bias in Machine Learning on Future Users of Artificial Intelligence
As mentioned above, bias in ML models has caused several negative ramifications for AI users. Unfortunately, the mere identification of this bias has not meant that it has or can necessarily be eradicated. The existence of bias in ML models is likely to have substantial ramifications for the future of AI and its users. With the increasing integration of AI systems into decision-making processes in different areas, the potential impact of biased decisions could be significant.
If people view AI systems as exhibiting bias or unfairness, it could result in a substantial decline in trust towards these technologies. The absence of trust may impede the widespread implementation of AI tools, hence constraining the potential advantages that these technologies could offer.
Moreover, partial AI models have the potential to strengthen and worsen current disparities in society and the economy. Biased credit scoring methods have the potential to reject loan applications from specific demographic groups, hence perpetuating cycles of poverty and exclusion.
From a legal perspective, the utilization of AI systems that exhibit bias may give rise to legal and ethical issues, especially in fields that are subject to regulations, such as healthcare, banking, and criminal justice. Organizations implementing these technologies may encounter legal action, regulatory fines, and harm to their reputation.
More broadly, the continued presence of prejudice in ML has the potential to impede progress by constraining the ability of AI to benefit the entirety of the human population. On the other hand, unbiased AI systems are more prone to producing inventive solutions that are inclusive and advantageous to various populations.
The presence of bias in ML is a crucial issue that requires attention in order to guarantee the fairness, equality, and reliability of AI technology. By recognising the many forms of prejudice, their origins, and the methods for alleviation, anyone involved might adopt proactive measures to diminish bias and its effects on users. As AI progresses, it is crucial to prioritize the development of impartial ML models. This is necessary to fully use the immense capabilities of this revolutionary technology and ensure that AI benefits all individuals equally in the future.
