3.1 Types and Sources of Algorithmic Bias
6 min read•july 30, 2024
Algorithmic bias is a crucial issue in AI ethics. It occurs when computer systems produce unfair outcomes, often due to flawed training data, human biases, or feedback loops. This can lead to in areas like facial recognition, hiring, and predictive policing.
There are several types of algorithmic bias, including , , and . These biases can stem from historical data, societal prejudices, or lack of diversity in AI teams. Recognizing and addressing these issues is essential for creating fair AI systems.
Types of algorithmic bias
Systematic errors creating unfair outcomes
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- Algorithmic bias refers to systematic errors in computer systems that create unfair outcomes, such as privileging one arbitrary group over others
- These biases can emerge from various sources, including problems with training data, human biases, and feedback loops
- Examples of unfair outcomes include facial recognition systems performing poorly on people with darker skin, or resume screening tools discriminating against women
Specific types of algorithmic bias
- Selection bias occurs when the data used to train an AI system does not accurately reflect the population of interest, leading to skewed outcomes
- For instance, if a medical diagnosis AI is trained mainly on data from white patients, it may perform poorly for patients of other races
- Measurement bias arises when the way data is collected or labeled causes certain data to be considered more important, skewing the outcomes
- As an example, if historical hiring data reflects discriminatory practices, an AI system trained on this data will learn to perpetuate these biases
- Confounding bias happens when an AI system picks up on and amplifies spurious correlations rather than true causal relationships
- A classic example is an AI system concluding that ice cream sales cause drowning, when in fact both are correlated with hot weather
- occurs when important features are left out of the data used to train an AI, leading it to rely on other less relevant features
- For instance, if data on loan repayment doesn't include employment status, an AI might incorrectly use race as a proxy, leading to bias
Bias in training data
Historical and societal biases in data
- AI systems learn patterns and correlations from the data they are trained on. If this training data contains biases, those biases will be learned and reproduced by the AI system
- Historical data used to train AI often contains societal biases around factors like race and gender. AI trained on this data will pick up and perpetuate these biases
- For example, Amazon had to scrap an AI recruiting tool that discriminated against women because it was trained on historical hiring data reflecting human bias
- Lack of diverse representation in training data means the AI will perform poorly for underrepresented groups. This is especially problematic in facial recognition systems
- Research has shown that leading facial recognition tools have significantly higher error rates for women and people of color due to skewed training data
Issues with data collection and labeling
- Imbalanced datasets, where some groups are over- or under-represented compared to their real-world frequencies, lead to skewed AI outcomes
- For instance, if medical data comes disproportionately from healthier and wealthier patients, AI diagnostic tools may not work well for other populations
- Mislabeled data, where humans incorrectly tag data during the training process, leads to AI learning the wrong associations and exhibiting biased performance
- An example would be a computer vision system learning to classify images of kitchens as "women" and offices as "men" based on gender biases in labeled training photos
- Careful curation of training datasets for diversity and accurate labeling is crucial for mitigating bias, but is often difficult and resource-intensive
- Facial recognition datasets aiming for diversity have run into issues with consent and privacy when scraping online images of underrepresented groups
Human biases in AI
Conscious and unconscious biases of developers
- The humans who design AI systems and choose what data to train them on have conscious and unconscious biases that can become built into the technology
- Biases around age, gender, race and other characteristics can skew how developers frame problems for AI systems to solve
- For example, developers of predictive policing tools may focus on optimizing for high arrest numbers vs. community wellbeing due to biases about crime
- Confirmation bias, anchoring bias, in-group bias and other cognitive biases can influence the decisions of AI developers throughout the design process
- An example of confirmation bias would be a developer who believes AI is objective testing their systems in ways that confirm this belief and overlooking evidence of bias
Lack of diversity in AI teams
- Lack of diversity in AI teams means that developers' blind spots around bias are more likely to go unnoticed and unaddressed
- The AI field is currently dominated by white and Asian men, especially in leadership roles at top companies
- A 2019 study found that only 18% of authors at major AI conferences are women, and more than 80% of AI professors are men
- Homogeneous teams are less likely to recognize and question biased assumptions, leading to biased AI products
- With more diverse voices involved in the development process, issues of bias are more likely to be anticipated and avoided
- Increasing diversity in the AI field is a crucial step for identifying and mitigating algorithmic bias, but significant barriers remain
- Hostile workplace cultures, unequal access to resources and mentoring, and biased hiring and promotion practices all hinder diversity efforts
Feedback loops and bias amplification
How AI outputs become future inputs
- Feedback loops occur when the outputs of an AI system are used as inputs, directly or indirectly, in the future. This causes the system's predictions to influence its later behavior
- Bias in an AI system can be amplified over time through feedback loops, as the model's skewed outputs are fed back into it and used to re-train the system
- For example, if an AI resume screening tool favors male applicants, more men will be hired, and their performance data will be fed back into the AI, amplifying its bias over time
- Feedback loops can be subtle and hard to identify, as there are often several steps between an AI system's outputs and the way those outputs make their way back into the system as future inputs
- In a social media newsfeed, biased click and share data influences what content is boosted, which influences future user behavior in hard-to-trace ways
Examples of bias-amplifying feedback loops
- Predictive policing is an example of a feedback loop that can amplify bias. Crime predictions lead to more policing in certain areas, which leads to more crime detected there, which feeds back into the crime prediction algorithms
- This can create a "runaway feedback loop" where policing is increasingly concentrated in overpoliced communities, regardless of true crime rates
- Recommendation engines, as used by online platforms, can create filter bubbles that trap users in feedback loops of being shown only content that matches their existing preferences
- This can reinforce and amplify biases, as with YouTube's algorithm being more likely to suggest progressively more extreme political content to users who start out watching partisan videos
- Feedback loops in AI systems that make real-world decisions (e.g. loan approval, medical diagnosis) are especially dangerous as outputs directly influence people's lives
- If a medical AI incorporates biased assumptions that certain patients are less likely to comply with treatment, those patients may be undertreated, leading to worse outcomes that feed back into and amplify the AI's bias
Key Terms to Review (22)
AI Act: The AI Act is a proposed regulatory framework by the European Union aimed at ensuring the safe and ethical deployment of artificial intelligence technologies across member states. This act categorizes AI systems based on their risk levels, implementing varying degrees of regulation and oversight to address ethical concerns and promote accountability.
Algorithmic accountability: Algorithmic accountability refers to the responsibility of organizations and individuals to ensure that algorithms operate fairly, transparently, and ethically. This concept emphasizes the need for mechanisms that allow stakeholders to understand and challenge algorithmic decisions, ensuring that biases are identified and mitigated, and that algorithms serve the public good.
Confounding Bias: Confounding bias occurs when an external factor, known as a confounder, is associated with both the independent and dependent variables in a study, leading to a distorted view of the relationship between those variables. This bias can lead to incorrect conclusions, especially in algorithmic contexts where the data used for training models may inadvertently incorporate these confounders. Understanding confounding bias is crucial in identifying the true impact of an algorithm on outcomes and ensuring fairness in decision-making processes.
Data Bias: Data bias refers to systematic errors or prejudices present in data that can lead to unfair, inaccurate, or misleading outcomes when analyzed or used in algorithms. This can occur due to how data is collected, the representation of groups within the data, or the assumptions made by those analyzing it. Understanding data bias is crucial for ensuring fairness and accuracy in AI applications, especially as these systems are integrated into various aspects of life.
Deontological Ethics: Deontological ethics is a moral theory that emphasizes the importance of following rules and duties when making ethical decisions, rather than focusing solely on the consequences of those actions. This approach often prioritizes the adherence to obligations and rights, making it a key framework in discussions about morality in both general contexts and specific applications like business and artificial intelligence.
Discrimination: Discrimination refers to the unfair treatment of individuals or groups based on characteristics such as race, gender, age, or other attributes. In the context of artificial intelligence, discrimination often arises from algorithmic bias, where AI systems may perpetuate existing social inequalities through their decision-making processes.
Equity in AI: Equity in AI refers to the principle of fairness and justice in the design, development, and deployment of artificial intelligence systems. It emphasizes that AI should operate without bias and should treat all individuals and groups fairly, ensuring equal access to benefits and opportunities generated by AI technologies. This concept is critical in understanding how algorithmic bias can arise and the importance of implementing techniques to mitigate such biases in AI systems.
Exclusionary Practices: Exclusionary practices refer to methods or strategies that deliberately or inadvertently restrict access to resources, opportunities, or participation based on specific characteristics such as race, gender, socioeconomic status, or other attributes. These practices can result in systemic biases, particularly within algorithmic systems, where certain groups may be marginalized or left out of the decision-making processes.
Fairness-aware algorithms: Fairness-aware algorithms are designed to reduce bias and promote equitable treatment across different demographic groups when making decisions based on data. These algorithms incorporate fairness criteria directly into their design or operation, striving to ensure that the outcomes produced do not disproportionately disadvantage any particular group, thereby addressing concerns related to algorithmic bias and discrimination.
GDPR: The General Data Protection Regulation (GDPR) is a comprehensive data protection law in the European Union that came into effect on May 25, 2018. It sets guidelines for the collection and processing of personal information, aiming to enhance individuals' control over their personal data while establishing strict obligations for organizations handling that data.
Historical Bias: Historical bias refers to the systematic favoring or prejudice that arises from the perspectives, values, and experiences of individuals or groups throughout history. This bias can significantly influence how data is collected, interpreted, and used in algorithmic models, resulting in outcomes that may not accurately represent or reflect reality. Understanding historical bias is crucial as it helps identify the roots of data discrepancies and highlights the need for fairness in algorithmic decision-making.
Human-Centered Design: Human-centered design is a creative approach that prioritizes the needs, preferences, and experiences of the end-users throughout the design process. This method emphasizes empathy and understanding users' perspectives to create solutions that genuinely meet their needs and improve their overall experience. By integrating feedback from users at every stage, human-centered design helps to minimize the risk of bias and ensure that algorithms and systems are fair and accessible to diverse populations.
Kate Crawford: Kate Crawford is a prominent researcher and thought leader in the field of artificial intelligence (AI) and its intersection with ethics, society, and policy. Her work critically examines the implications of AI technologies on human rights, equity, and governance, making significant contributions to the understanding of ethical frameworks in AI applications.
Measurement Bias: Measurement bias refers to systematic errors that occur when the tools or methods used to collect data consistently produce inaccurate results. This type of bias can lead to misleading conclusions, particularly in algorithmic contexts where data is used to train machine learning models, affecting the overall fairness and effectiveness of AI systems.
Model bias: Model bias refers to the systematic error in a machine learning model that leads to inaccurate predictions or conclusions, often stemming from incorrect assumptions in the learning algorithm or training data. This type of bias can affect the fairness and accuracy of decision-making processes, ultimately influencing real-world outcomes. Understanding model bias is crucial as it can perpetuate inequalities and impact various domains, such as hiring practices, law enforcement, and healthcare.
Omitted variable bias: Omitted variable bias occurs when a model leaves out one or more relevant variables that influence both the dependent and independent variables, leading to incorrect conclusions about the relationship between them. This can significantly distort the results of algorithmic analysis and decision-making, as it can create a misleading representation of how certain factors are related, ultimately affecting fairness and accuracy in AI systems.
Participatory Design: Participatory design is an approach that actively involves stakeholders, especially users, in the design process to ensure that the end product meets their needs and expectations. This method emphasizes collaboration and feedback from those who will use the system, which can lead to more effective solutions and reduce the risk of bias in design. By incorporating diverse perspectives, participatory design helps to address potential algorithmic biases that may arise from a lack of representation.
Ruha Benjamin: Ruha Benjamin is a prominent scholar and sociologist known for her work on the intersections of race, technology, and justice. She explores how technology can reinforce existing social inequalities and emphasizes the importance of a critical approach to understanding algorithmic bias and its implications for fairness and equity in society.
Sampling Bias: Sampling bias occurs when the individuals selected for a study or analysis do not represent the larger population from which they are drawn, leading to skewed results and conclusions. This type of bias can significantly affect the fairness and accuracy of algorithmic decisions, especially when algorithms rely on biased data to make predictions or assessments.
Selection Bias: Selection bias occurs when the sample used in a study or analysis is not representative of the population being studied, leading to inaccurate or skewed results. This can happen in various contexts, especially in data collection and algorithm training, where certain groups may be overrepresented or underrepresented. The implications of selection bias are significant as they can impact the fairness and accuracy of algorithms, often perpetuating existing inequalities.
Transparency in algorithms: Transparency in algorithms refers to the clarity and openness regarding how algorithms function, including their decision-making processes and the data they use. This concept is crucial for understanding algorithmic bias, as it helps stakeholders identify how biases may emerge from the algorithm's design or data inputs, enabling accountability and trust in AI systems.
Utilitarianism: Utilitarianism is an ethical theory that advocates for actions that promote the greatest happiness or utility for the largest number of people. This principle of maximizing overall well-being is crucial when evaluating the moral implications of actions and decisions, especially in fields like artificial intelligence and business ethics.