What is data bias?

AI models are programs that have been trained on data sets to recognize certain patterns or make certain decisions. They apply different algorithms to relevant data inputs to achieve the tasks or output they’ve been programmed for.

Training an AI model on data with bias, such as historical or representational bias, could lead to biased or skewed outputs that might unfairly represent or otherwise discriminate against certain groups or individuals. These impacts erode trust in AI and organizations that use AI. They can also lead to legal and regulatory penalties for businesses.

Data bias is an important consideration for high-stakes industries—such as healthcare, human resources and finance—that increasingly use AI to help inform decision-making. Organizations can mitigate data bias by understanding the different types of data bias and how they occur and by identifying, reducing and managing these biases throughout the AI lifecycle.

Data bias can lead to unfair, inaccurate and unreliable AI systems resulting in serious consequences for individuals, businesses and society. Some risks of data bias include:

Data bias within AI systems can perpetuate existing societal biases, leading to unfair treatment based on characteristics such as gender, age, race or ethnicity. Marginalized groups might be underrepresented in or excluded from the data, resulting in decisions that fail to address the needs of the actual population.

For example, a hiring algorithm primarily trained on data from a homogeneous, male workforce might favor male candidates while disadvantaging qualified female applicants, perpetuating gender inequality in the workplace.

AI models trained on skewed data can produce incorrect outcomes, which can cause organizations to make poor decisions or propose ineffective solutions. For example, businesses using biased predictive analytics might misinterpret market trends, resulting in poor product launches or the misallocation of resources.

Data bias can put organizations at risk of regulatory scrutiny, legal non-compliance and substantial fines. For instance, under the EU AI Act, failing to comply with prohibited AI practices can mean fines of up to EUR 35,000,000 or 7% of worldwide annual turnover, whichever is higher.

Organizations in violation of local and regional laws might also see an erosion of reputation and customer trust. Consider a retail company found culpable of discrimination for using an AI-powered pricing model that charged higher prices to certain demographic groups. This situation could result in a public relations crisis that hurts the company’s brand image and customer loyalty.

Data bias can erode trust in AI systems. Severe or repeated instances of biased or inaccurate AI-driven decisions might spur individuals and communities to question the integrity of the organization deploying the AI. People might also become increasingly skeptical about the reliability and fairness of AI in general, leading to a broader reluctance to embrace the technology.

AI systems that use biased results as input data for decision-making create a feedback loop that can also reinforce bias over time. This cycle, where the algorithm continuously learns and perpetuates the same biased patterns, leads to increasingly skewed results.

For example, historic discrimination such as redlining—financial services being denied to people based on their race—can be reflected in training data for an AI model tasked with bank loan decision-making. As an AI system processes applications using this data, it could unfairly penalize individuals who share socioeconomic characteristics with victims of redlining in years past. Data from those more recent loan rejections could inform future AI decision-making, leading to a cycle where members of underrepresented groups continue to receive fewer credit opportunities.

Data bias, AI bias and algorithmic bias can all result in distorted outputs and potentially harmful outcomes, but there are subtle differences among these terms.

AI bias, also called machine learning bias, is an umbrella term for the different types of bias associated with artificial intelligence systems. It refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithm.

Algorithmic bias is a subset of AI bias that occurs when systemic errors in machine learning algorithms produce unfair or discriminatory outcomes. Algorithmic bias is not caused by the algorithm itself, but by how the developers collect and code training data.

Data bias also falls under the umbrella of AI bias and can be one of the causes of algorithmic bias. Data bias specifically refers to the skewed or unrepresentative nature of the data used to train an AI model.

Understanding and addressing the different types of bias can help create accurate and trustworthy AI systems. Some common types of data bias include:

• Cognitive bias
• Automation bias
• Confirmation bias
• Exclusion bias
• Historical (temporal) bias
• Implicit bias
• Measurement bias
• Reporting bias
• Selection bias
• Sampling bias

When people process information and make judgments, they are inevitably influenced by their experiences and preferences. As a result, people might build these biases into AI systems through the selection of data or how the data is weighted. Cognitive bias could lead to systematic errors, such as favoring data sets gathered from Americans rather than sampling from a range of populations around the globe.

Automation bias occurs when users overrely on automated technologies, leading to uncritical acceptance of their outputs, which can perpetuate and amplify existing data biases. For example, in healthcare, a doctor might rely heavily on an AI diagnostic tool to suggest treatment plans for patients. By not verifying the tool’s results against their own clinical experience, the doctor could potentially misdiagnose a patient should the tool’s decision stem from biased data.

Confirmation bias occurs when data is selectively included to confirm preexisting beliefs or hypotheses. For example, confirmation bias occurs in predictive policing when law enforcement focuses data collection on neighborhoods with historically high crime rates. This results in the over policing of these neighborhoods, due to the selective inclusion of data that supports the existing assumptions about the area.

Exclusion bias happens when important data is left out of data sets. In economic predictions, the systematic exclusion of data from low-income areas results in data sets that are accurately representative of the population, leading to economic forecasts that skew in favor of wealthier areas.

Historical bias, also known as temporal bias, occurs when data reflects historical inequalities or biases that existed during data collection, as opposed to the current context. Examples of data bias in this category include AI hiring systems trained on historical employment data. In these data sets, people of color might be underrepresented in high-level jobs, and the model might perpetuate the inequality.

Implicit bias occurs when people’s assumptions based on personal experiences, rather than more general data, are introduced into ML building or testing. For example, an AI system trained to evaluate job applicants might prioritize résumés with masculine-coded language, reflecting the unconscious bias of the developer, even though gender is not an explicit factor in the model.

Measurement bias can occur when the accuracy or quality of the data differs across groups or when key study variables are inaccurately measured or classified. For instance, a college admissions model that uses high GPAs as its main factor for acceptance does not consider that higher grades might be easier to achieve at certain schools than at others. A student with a lower GPA but a more challenging course load at one school might be a more capable candidate than a student with a higher GPA but a less challenging course load elsewhere. Given its emphasis on GPAs, the model might not factor this possibility into its decision-making processes.

Reporting bias occurs when the frequency of events or outcomes in the data set are not representative of the actual frequency. This bias often occurs when humans are involved in data selection, as people are more likely to document evidence that seems important or memorable.

For example, a sentiment analysis model is trained to predict whether products on a large e-commerce website are rated positively or negatively. Most of the reviews of similar products in the training data set reflect extreme opinions because people are less likely to leave a review if they did not respond to it strongly, making the model’s predictions less accurate.

Selection bias happens when the data set used for training is not representative enough, not large enough or too incomplete to sufficiently train the system. For example, training an autonomous car on daytime driving data is not representative of the full range of driving scenarios the vehicle might encounter in the real world.

Sampling bias is a type of selection bias that occurs when sample data is collected in a way in which some information is more likely to be included than other information, without proper randomization. For instance, if a medical AI system designed to predict the risk of heart disease was trained solely on data from middle-aged male patients, it might provide inaccurate predictions. This system would especially affect women and people of other age groups.

Mitigating bias within AI starts with AI governance. AI governance refers to the guidelines that work to help ensure AI tools and systems are and remain safe and ethical. Responsible AI practices, which emphasize transparency, accountability and ethical considerations, can guide organizations in navigating the complexities of bias mitigation.

To mitigate data bias, organizations should implement robust strategies and practices aimed at identifying, reducing and managing bias throughout data collection and analysis, such as:

• Representative data collection
  
• Audits and assessments
• Transparency
• Bias detection tools
• Inclusive teams
• Synthetic data

Broad representation in data sources helps reduce bias. The data collection process should encompass a wide range of demographics, contexts and conditions that are all adequately represented. For example, if data collected for facial recognition tools predominately includes images of White individuals, the model might not accurately recognize or differentiate Black faces.

Bias audits enable organizations to regularly assess their data and algorithms for potential biases, reviewing outcomes and examining data sources for indicators of unfair treatment among different demographic groups. Continuous performance monitoring across various demographic groups helps detect and address discrepancies in outcomes, helping to ensure that any bias present is identified and removed in a timely manner.

Documenting data collection methods and how algorithms make decisions enhances transparency, particularly regarding how potential biases are identified and addressed. Open data policies can facilitate external review and critique, promoting accountability in collection and data analysis, which is essential for fostering trust in AI systems.

Using algorithmic fairness tools and frameworks can aid in detecting and mitigating bias in machine learning models. AI Fairness 360, an open source toolkit developed by IBM, provides various metrics to detect bias in data sets and machine learning models, along with algorithms to mitigate bias and promote fairness. Implementing statistical methods to evaluate the fairness of predictions across different demographic groups can further improve objectivity.

Fostering diversity on data science and analytics teams introduces various perspectives and can reduce the risk of bias. Diverse teams are more likely to recognize and address potential biases in data sets and algorithms because they draw on a wider range of experiences and viewpoints. For instance, a team that includes members from different racial, gender and socioeconomic backgrounds can better identify areas where the data might misrepresent or overlook certain groups of people.

Synthetic data is artificially generated data created through computer simulation or algorithms to take the place of data points collected from real-world events. Data scientists often find synthetic data a beneficial alternative when data is not readily available and because it offers more data privacy protection. Synthetic data mitigates bias by allowing the intentional creation of balanced data sets that include underrepresented groups and scenarios to help ensure more equitable model outcomes.