Understanding Algorithmic Bias: Types, Causes and Case Studies

Nitika Sharma 08 Sep, 2023 • 9 min read

Introduction

Have you ever wondered why your social media feed seems to predict your interests with uncanny accuracy, or why certain individuals face discrimination when interacting with AI systems? The answer often lies in algorithmic bias, a complex and pervasive issue within artificial intelligence. This article will disclose what is algorithmic bias, its various dimensions, causes, and consequences. Moreover, it underscores the pressing need to establish trust in AI systems, a fundamental prerequisite for responsible AI development and equitable utilization.

What is Algorithmic Bias?

Algorithmic bias is like when a computer program makes unfair decisions because it learned from data that wasn’t completely fair. Imagine a robot that helps decide who gets a job. If it was trained mostly on resumes from men and didn’t know much about women’s qualifications, it might unfairly favor men when choosing candidates. This isn’t because the robot wants to be unfair, but because it learned from biased data. Algorithmic bias is when computers unintentionally make unfair choices like this because of the information they were taught.

Algorithmic Bias
Source: LinkedIN

Types of Algorithmic Bias

Data Bias 

It occurs when the data used to train an AI model is not representative of the real-world population, resulting in skewed or unbalanced datasets. For example, if a facial recognition system is trained predominantly on images of light-skinned individuals, it may perform poorly when trying to recognize people with darker skin tones, leading to a data bias that disproportionately affects certain racial groups.

Model Bias 

It refers to biases that occur during the design and architecture of the AI model itself. For instance, if an AI algorithm is designed to optimize for profit at all costs, it may make decisions that prioritize financial gain over ethical considerations, resulting in model bias that favors profit maximization over fairness or safety.

Evaluation Bias

It occurs when the criteria used to assess the performance of an AI system are themselves biased. An example could be an educational assessment AI that uses standardized tests that favor a particular cultural or socioeconomic group, leading to evaluation bias that perpetuates inequalities in education.

Causes of Algorithmic Bias

Several factors can cause algorithmic bias, and it’s essential to understand these causes to mitigate and address discrimination effectively. Here are some key causes:

Biased Training Data

One of the primary sources of bias is biased training data. If the data used to teach an AI system reflects historical prejudices or inequalities, the AI may learn and perpetuate those biases. For example, if historical hiring data is biased against women or minority groups, an AI used for hiring may also favor certain demographics.

Sampling Bias

Sampling bias occurs when the data used for training is not representative of the entire population. If, for instance, data is collected primarily from urban areas and not rural ones, the AI may not perform well for rural scenarios, leading to bias against rural populations.

Data Preprocessing

The way data is cleaned and processed can introduce bias. If the data preprocessing methods are not carefully designed to address bias, it can persist or even be amplified in the final model.

Feature Selection

Features or attributes chosen to train the model can introduce bias. If features are selected without considering their impact on fairness, the model may inadvertently favor certain groups.

Model Selection and Architecture

The choice of machine learning algorithms and model architectures can contribute to bias. Some algorithms may be more susceptible to bias than others, and the way a model is designed can affect its fairness.

Human Biases

The biases of the people involved in designing and implementing AI systems can influence the outcomes. If the development team is not diverse or lacks awareness of bias issues, it can inadvertently introduce or overlook bias.

Historical and Cultural Bias

AI systems trained on historical data may inherit biases from past societal norms and prejudices. These biases may not be relevant or fair in today’s context but can still affect AI outcomes.

Implicit Biases in Data Labels

The labels or annotations provided for training data can contain implicit biases. For instance, if crowdworkers labeling images exhibit biases, these biases may propagate into the AI system.

Feedback Loop

AI systems that interact with users and adapt based on their behavior can reinforce existing biases. If users’ biases are incorporated into the system’s recommendations, it can create a feedback loop of bias.

Data Drift

Over time, data used to train AI models can become outdated or unrepresentative due to changes in society or technology. This can lead to performance degradation and bias.

Detecting Algorithmic Bias

Detecting algorithmic bias is critical in ensuring fairness and equity in AI systems. Here are steps and methods to detect algorithmic bias:

Define Fairness Metrics

Start by defining what fairness means in the context of your AI system. Consider factors like race, gender, age, and other protected attributes. Identify which metrics to measure fairness, such as disparate impact, equal opportunity, or predictive parity.

Audit the Data

Data Analysis: Conduct a thorough analysis of your training data. Look for imbalances in the representation of different groups. This involves examining the distribution of attributes and checking if it reflects real-world demographics.

Data Visualizations

Create visualizations to highlight any disparities. Histograms, scatter plots, and heatmaps can reveal patterns that are not apparent through statistical analysis alone.

Evaluate Model Performance

Assess your AI model’s performance for different demographic groups. Use your chosen fairness metrics to measure disparities in outcomes. You may need to split the data into subgroups (e.g., by gender, race) and evaluate the model’s performance within each subgroup.

Fairness-Aware Algorithms

Consider using fairness-aware algorithms that explicitly address bias during model training. These algorithms aim to mitigate bias and ensure that predictions are equitable across different groups.

Regular machine learning models may not guarantee fairness, so exploring specialized fairness-focused libraries and tools can be valuable.

Bias Detection Tools

Utilize specialized bias detection tools and software. Many AI fairness tools can help identify and quantify bias in your models. Some popular ones include IBM Fairness 360, AI Fairness 360, and Aequitas.

These tools often provide visualizations, fairness metrics, and statistical tests to assess and present bias in a more accessible manner.

External Auditing

Consider involving external auditors or experts to assess your AI system for bias. Independent reviews can provide valuable insights and ensure objectivity.

User Feedback

Encourage users to provide feedback if they believe they have experienced bias or unfair treatment from your AI system. User feedback can help identify issues that may not be apparent through automated methods.

Ethical Review

Conduct an ethical review of your AI system’s decision-making process. Analyze the logic, rules, and criteria the model uses to make decisions. Ensure that ethical guidelines are followed.

Continuous Monitoring

Algorithmic bias can evolve due to changes in data and usage patterns. Implement continuous monitoring to detect and address bias as it arises in real-world scenarios.

Legal and Regulatory Compliance

Ensure that your AI system complies with relevant laws and regulations governing fairness and discrimination, such as the General Data Protection Regulation (GDPR) in Europe or the Equal Credit Opportunity Act in the United States.

Documentation

Document your efforts to detect and address bias thoroughly. This documentation can be crucial for transparency, accountability, and compliance with regulatory requirements.

Iterative Process

Detecting and mitigating bias is an iterative process. Continuously refine your models and data collection processes to reduce bias and improve fairness over time.

Case Studies 

Amazon’s Algorithm Discriminated Against Women

Amazon’s automated recruitment system, designed to evaluate job applicants based on their qualifications, unintentionally exhibited gender bias. The system learned from resumes submitted by previous candidates and, unfortunately, perpetuated the underrepresentation of women in technical roles. This bias stemmed from the historical lack of female representation in such positions, causing the AI to unfairly favor male applicants. Consequently, female applicants received lower ratings. Despite efforts to rectify the issue, Amazon ultimately discontinued the system in 2017.

COMPAS Race Bias with Reoffending Rates

The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) aimed to predict the likelihood of criminal reoffending in the United States. However, an investigation by ProPublica in 2016 revealed that COMPAS displayed racial bias. While it correctly predicted reoffending at roughly 60% for both black and white defendants, it exhibited the following biases:

  • Misclassified a significantly higher percentage of black defendants as higher risk compared to white defendants.
  • Incorrectly labeled more white defendants as low risk, who later reoffended, compared to black defendants.
  • Classified black defendants as higher risk even when other factors like prior crimes, age, and gender were controlled for, making them 77% more likely to be labeled as higher risk than white defendants.

US Healthcare Algorithm Underestimated Black Patients’ Needs

An algorithm used by US hospitals to predict which patients needed additional medical care unintentionally reflected racial biases. It assessed patients’ healthcare needs based on their healthcare cost history, assuming that cost correlated with healthcare requirements. However, this approach did not consider differences in how black and white patients paid for healthcare. Black patients were more likely to pay for active interventions like emergency hospital visits, despite having uncontrolled illnesses. Consequently, black patients received lower risk scores, were categorized with healthier white patients in terms of costs, and did not qualify for extra care to the same extent as white patients with similar needs.

ChatBot Tay Shared Discriminatory Tweets

In 2016, Microsoft launched a chatbot named Tay on Twitter, intending it to learn from casual conversations with other users. Despite Microsoft’s intent to model, clean, and filter “relevant public data,” within 24 hours, Tay began sharing tweets that were racist, transphobic, and antisemitic. Tay learned discriminatory behavior from interactions with users who fed it inflammatory messages. This case underscores how AI can quickly adopt negative biases when exposed to harmful content and interactions in online environments.

How to Build Trust in AI?

Trust is a cornerstone of successful AI adoption. When users and stakeholders trust AI systems, they are more likely to embrace and benefit from their capabilities. Building trust in AI begins with addressing algorithmic bias and ensuring fairness throughout the system’s development and deployment. In this section, we will explore key strategies for building trust in AI by mitigating algorithmic bias:

Step 1: Transparency and Explainability

Openly communicate how your AI system works, including its objectives, data sources, algorithms, and decision-making processes. Transparency fosters understanding and trust.

Provide explanations for AI-generated decisions or recommendations. Users should be able to grasp why the AI made a particular choice.

Step 2: Accountability and Governance

Establish clear lines of accountability for AI systems. Designate responsible individuals or teams to oversee the development, deployment, and maintenance of AI.

Develop governance frameworks and protocols for addressing errors, biases, and ethical concerns. Make sure there are mechanisms in place to take corrective action when needed.

Step 3: Fairness-Aware AI

Employ fairness-aware algorithms during model development to reduce bias. These algorithms aim to ensure equitable outcomes for different demographic groups.

Regularly audit AI systems for fairness, especially in high-stakes applications like lending, hiring, and healthcare. Implement corrective measures when bias is detected.

Step 4: Diversity and Inclusion

Promote diversity and inclusivity in AI development teams. A diverse team can better identify and address bias, considering a wide range of perspectives.

Encourage diversity not only in terms of demographics but also in expertise and experiences to enhance AI system fairness.

Step 5: User Education and Awareness

Educate users and stakeholders about the capabilities and limitations of AI systems. Provide training and resources to help them use AI effectively and responsibly.

Raise awareness about the potential biases in AI and the measures in place to mitigate them. Informed users are more likely to trust AI recommendations.

Step 6: Ethical Guidelines

Develop and adhere to a set of ethical guidelines or principles in AI development. Ensure that AI systems respect fundamental human rights, privacy, and fairness.

Communicate your organization’s commitment to ethical AI practices and principles to build trust with users and stakeholders.

Step 7: Continuous Improvement

Implement mechanisms for collecting user feedback on AI system performance and fairness. Actively listen to user concerns and suggestions for improvement.

Use feedback to iteratively enhance the AI system, demonstrating a commitment to responsiveness and continuous improvement.

Step 8: Regulatory Compliance

Stay up-to-date with and adhere to relevant AI-related regulations and data protection laws. Compliance with legal requirements is fundamental to building trust.

Step 9: Independent Audits and Third-Party Validation

Consider independent audits or third-party assessments of your AI systems. External validation can provide an additional layer of trust and credibility.

Conclusion 

In artificial intelligence, addressing algorithmic bias is paramount to ensuring trust and fairness. Bias, left unattended, perpetuates inequalities and undermines faith in AI systems. This article has unveiled its sources, real-world implications, and far-reaching consequences.

Building trust in AI requires transparency, accountability, diversity, and continuous improvement. It’s a perpetual journey towards equitable AI. As we strive for this shared vision, consider taking the next step with the Analytics Vidhya BB+ program. You can deepen your AI and data science skills here while embracing ethical AI development. 

Frequently Asked Questions 

Q1. What is algorithmic bias?

A. Algorithmic bias refers to the presence of unfair or discriminatory outcomes in artificial intelligence (AI) and machine learning (ML) systems, often resulting from biased data or design choices, leading to unequal treatment of different groups.

Q2. What is an example of algorithmic bias?

A. An example is when an AI hiring system favors male candidates over equally qualified female candidates because it was trained on historical data that reflects gender bias in previous hiring decisions.

Q3. What is algorithmic bias in ML?

A. Algorithmic bias in ML occurs when machine learning models produce biased or unfair predictions, often due to biased training data, skewed feature selection, or modeling choices that result in discriminatory outcomes.

Q5. What are the five different types of algorithmic bias?

A. The five types of algorithmic bias are:
– Data bias
– Model bias
– Evaluation bias
– Measurement bias
– Aggregation bias

Nitika Sharma 08 Sep 2023

Frequently Asked Questions

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