Bias Interrupters: Ensuring Fairness in Artificial Intelligence Systems

Artificial intelligence (AI) holds tremendous promise for improving lives and solving complex problems. However, these powerful technologies also risk perpetuating or amplifying unfair bias if they are not carefully designed and tested. Bias can enter AI systems in subtle ways, leading to discriminatory and unethical outcomes. Mitigating harmful bias is both a technical and ethical imperative as AI becomes further integrated into high-stakes domains like hiring, lending, policing, and healthcare.

This comprehensive guide examines the causes, consequences, and solutions for unfair bias in AI, with a focus on “bias interrupters” – practical techniques to increase algorithmic fairness. Read on to understand how bias gets baked into AI, why it matters, and how data scientists, companies, researchers, and policymakers can proactively interrupt bias in the machine learning pipeline.

Table of Contents

1. Understanding Bias in AI
   • Defining Algorithmic Bias
   • How Does Bias Arise in AI Systems?
   • Why Does Algorithmic Bias Matter?
   • Types of Bias
     • Pre-Existing Societal Biases
     • Technical Sources of Bias
     • Evaluation Biases
   • Real-World Consequences
     • Hiring
     • Facial Recognition
     • Financial Lending
     • Healthcare
     • Criminal Justice
2. Testing AI Systems for Unfair Bias
   • Bias Quantification Metrics
     • Statistical Parity Difference
     • Average Odds Difference
     • Equal Opportunity Difference
   • Auditing for Group Fairness
   • Individual vs. Group Fairness
   • Challenges in Fairness Testing
3. Implementing Bias Interrupters
   • Input Data Investigation
     • Assessing Label Quality
     • Checking for Underrepresentation
     • Identifying Problematic Proxy Variables
   • Algorithmic Techniques
     • Adjusting Class Weights
     • Regularization
     • Adversarial Debiasing
   • Post-Processing
     • Calibrating Thresholds
     • Editing Algorithmic Decisions
   • Human-Centered Design
     • Inclusive Development Teams
     • User Testing Across Groups
     • Transparent Model Reports
4. Governance and Policy for Fair AI
   • Research Guidelines
   • Risk Assessment Frameworks
   • Regulatory Initiatives
   • Industry Standards and Best Practices
5. Case Studies of Bias Interrupters in Action
   • Microsoft’s Fairlearn Toolkit
   • LinkedIn’s Economic Graph
   • Zillow’s Home Value Estimation Models
   • Pymetrics’ Audit AI
   • IBM’s AI FactSheets
6. Key Takeaways and Next Steps
   • The Role of Bias Interrupters
   • Toward Responsible AI Development
   • Promising Directions for Fairer AI

1. Understanding Bias in AI

Before examining solutions, we must first grasp the concept of algorithmic bias, understand how it arises, and recognize why it demands urgent attention. This section covers the basics of bias in AI systems.

Defining Algorithmic Bias

In machine learning, algorithmic bias refers to systematic errors due to poor data quality, unrepresented groups, limited features, problematic labels, insufficient model capacity, or other technical factors that result in unfair treatment or discrimination against certain individuals or subgroups.

Biased algorithms can fail groups matching certain characteristics, such as race, gender, age, income level, disability status, or other attributes. Bias disproportionately assigns positive or negative outcomes, judgments, and opportunities.

While pure algorithmic fairness is impossible to fully achieve due to inherent subjectivity and context, steps can be taken to significantly improve fair treatment. The goal is not to eliminate 100% of bias, but to thoughtfully identify, measure, and mitigate unfairness.

How Does Bias Arise in AI Systems?

Many factors introduce, preserve, or amplify bias in AI:

• Biased data – If input data reflects existing societal prejudices, imbalanced representation, or sampling mistakes, the model learns to reproduce those biases. Discriminatory patterns in data will lead to discriminatory model behavior.
• Limited features – Restricted information about examples can force algorithms to rely on questionable proxies. Features may not sufficiently capture individual context.
• Poor model choice – Insufficient model capacity, ineffective tuning, or a poor choice of model architecture can contribute technical biases like generalization error or overfitting certain groups.
• Programming errors – Engineers building AI systems are susceptible to their own cognitive biases. They may overlook potential harms or failures.
• Feedback loops – Once deployed, model predictions that affect real people can reinforce biases through viral feedback loops.

Most biases arise from some combination of these technical and societal factors. Thoughtful data collection, model development, testing, and monitoring processes can help surface and mitigate biases.

Why Does Algorithmic Bias Matter?

Biased algorithms undermine social good in several key ways:

• Fairness – When AI arbitrarily discriminates, it violates principles of equal treatment and opportunity. Biased systems compound historical injustice.
• Accuracy – Bias degrades overall model accuracy by incorrectly judging subsets of data. Skewed performance impacts all users but harms excluded groups most.
• Safety – Flawed AI can directly harm users through denied opportunities, resources, or freedom. It may violate privacy or enable manipulation through improperly profiled micro-targeting.
• Compliance – In sectors like employment and finance, discrimination violates existing protective regulations. Biased algorithms introduce legal liability.
• Ethics – Systematically biased results conflict with shared morals, human rights, and civil liberties. Unethical algorithms damage institutional credibility and public trust.

Though sometimes implicit, algorithmic bias causes extensive individual and societal harms. Testing processes and thoughtful regulation are essential to unlock AI’s benefits while protecting users.

Types of Bias

AI systems absorb biases from multiple sources. Understanding the origin and mechanics of different biases enables more targeted mitigation strategies. Major categories include:

Pre-Existing Societal Biases

Reflecting widespread preconceptions or stereotypes, societal biases permeate input data, labels, features, and human judgment used in AI systems:

• Measurement bias – Incomplete or misleading assumptions leads to data that unfairly misrepresents certain groups.
• Population bias – Systematic underrepresentation or exclusion of subgroups yields imbalanced datasets.
• Label bias – Biased training labels skew algorithms, as when undervaluing minority communities in home price data.
• Surveillance bias – Over-policing certain groups distorts real crime rates and leads to further targeting.

Societal biases like sampling errors and prejudice stem from complex historical forces, requiring holistic consideration of data sources and indicator choices.

Technical Sources of Bias

Even absent societal factors, issues in model development and testing cause technical biases:

• Selection bias – Non-representative training, validation, and test sets produce misleading performance measures.
• Reporting bias – Unequal rates of feature availability, error tolerance, or group significance lead to imbalanced model behavior.
• Representation bias – Relationships learned from limited subgroups fail to generalize to unrepresented groups.
• Aggregation bias – Group-level correlations ignore important individual differences and intersections between identities.
• Evaluation bias – Metrics that only measure overall performance mask poor results for subgroups.

Thoughtful data sampling, feature engineering, model selection, tuning, and testing processes can lessen technical sources of bias.

Evaluation Biases

Finally, evaluation biases arise in testing and validation:

• Group attribution bias – Assuming all group members share attributes leading to collective judgments.
• Ground truth bias – Relying on disputed or subjective definitions of optimal outcomes.
• Confirmation bias – Testing selectively to confirm existing assumptions while ignoring contrary evidence.

Mitigating evaluation bias requires metrics spanning different groups and questioning prior assumptions.

While interlinked, categorizing bias types directs attention to their distinct causes and solutions. Multi-dimensional strategies are required to move towards fairer AI.

Real-World Consequences

Left unaddressed, biased algorithms inflict widespread individual and societal damages. While AI promises benefits in efficiency, insight, and personalization, harmful bias undercuts these goals:

Hiring

Automated resume screening, video interviews, and assessment tests aim to expand candidate pools and reduce human bias. However, algorithmic systems often replicate existing gender, race, age, and ability prejudices rather than correcting them:

• Ranking systems downgrade resumes from women and minority names while boosted unqualified male candidates.
• Speech analysis tools are much less accurate for women, non-native speakers, and accented speech.
• Psychometric tests based on limited norms exhibit cultural biases.

Biased hiring algorithms create unfair barriers to employment and suppress diversity.

Facial Recognition

Despite improvements in computer vision, facial recognition exhibits high error rates with women and darker skinned faces. Studies have found:

• Up to 35% higher error rates for women, especially with dark skin tones.
• Native groups misidentified at rates 5-10x higher than white faces.
• Overall error rates above 20% for East and Central Asians.

These gaps enable harmful surveillance misidentifications and reinforce marginalization of some groups.

Financial Lending

Algorithmic credit decisions aimed at efficiency and consistency can arbitrarily discriminate by learning biased historical data:

• Denying or limiting credit for minority neighborhoods deemed riskier by bank data reflecting past exclusion.
• Penalizing use of credit from minority-serving institutions.
• Rewarding social connections irrelevant to individual risk.

Biased lending algorithms create digital redlining excluding disadvantaged communities.

Healthcare

AI applied to electronic health records, insurance data, biometrics, and more holds promises to expand access and personalize care. However, biases arise in:

• Image analysis tools like dermatology and radiology AIs that struggle with non-white skin tones and features.
• Mortality models relying on racial categories despite health disparities reflecting social rather than biological differences.
• Digital symptom checkers based on limited cultural contexts.

Biased health algorithms risk dangerous diagnostic errors, insurance denials, and exclusion from new AI therapies.

Criminal Justice

AI applied to predictive policing, recidivism risk scores, and forensic analysis aims to assist law enforcement. However, biased data and algorithms lead to:

• Place-based predictive policing concentrating forces in marginalized neighborhoods.
• Risk assessment scores that discriminate based on race, income, mental health history and other proxies.
• Dubious forensic analysis like facial recognition, predictive DNA, and neural detectors applied absent proper validation.

Biased predictive models entrench unjust policing and sentencing.

Across domains, unchecked algorithmic bias causes extensive individual harms and societal damages. But emerging bias interrupting approaches offer solutions.

2. Testing AI Systems for Unfair Bias

The first step toward fairer AI is rigorous testing and auditing processes to reveal how and where models deliver unfair or unethical outcomes. Various bias detection methods provide insight into how groups are differentially impacted and enable mitigation steps. Key approaches include:

Bias Quantification Metrics

Researchers have developed quantitative metrics to measure different aspects of algorithmic fairness across groups, including:

Statistical Parity Difference

A basic measure of overall fairness is statistical parity difference (SPD) which computes the difference in outcomes between groups:

SPD = P(Ŷ|G = advantaged) - P(Ŷ|G = disadvantaged)

where Ŷ is the algorithmic decision and G represents the protected group. Lower SPD indicates more equal treatment.

Average Odds Difference

Examining false positives and false negatives, average odds difference (AOD) evaluates error balance:

AOD = [P(Ŷ=1|G=dis,Y=0) - P(Ŷ=1|G=adv,Y=0)] + [P(Ŷ=0|G=dis,Y=1) - P(Ŷ=0|G=adv,Y=1)]

where Y is the true outcome. Smaller AOD shows errors affect groups similarly.

Equal Opportunity Difference

Equal opportunity difference (EOD) focuses specifically on false negatives that incorrectly deny a benefit:

EOD = P(Ŷ=0|G=dis,Y=1) - P(Ŷ=0|G=adv,Y=1)

Lower EOD provides evidence of equitably distributing opportunities.

Ideally, systems should be tested across statistical parity, accuracy equity, and equality of opportunity to surface varied aspects of bias.

Auditing for Group Fairness

Beyond quantitative metrics, disparate impact analysis audits model performance between groups, drilling down on factors like:

• Differing accuracy, false positives, and false negatives
• Skewed probability thresholds and outlier sensitivity
• Unequal error costs and significance of mistakes
• Imbalances in confusing subjects within and across groups

Group fairness audits reveal insights not captured by overall metrics. Thoughtful facial recognition research moved beyond benchmark datasets to analyze real-world performance gaps.

Individual vs. Group Fairness

Fundamental tensions exist between individual and group notions of fairness:

• Group fairness requires equitable performance across segments of data. But similar treatment may itself be unfair if groups have different needs.
• Individual fairness aims to make judgments independently of group membership. However, this risks neglecting how social factors shape meaning.

There are also contrasts between procedural and substantive fairness:

• Procedural algorithms apply equitable, transparent processes to all. But equal procedures allow inequitable outcomes if data reflects an unjust society.
• Substantive fairness aims for equitable outcomes accounting for advantages and barriers facing different groups. Yet determining appropriate adjustments is complex and contestable.

Hybrids blending procedural principles with substantive accommodations hold promise for maximizing both individual agency and group welfare. But value judgments underpin where lines are drawn. Ongoing pluralistic debate is needed to progress toward just algorithms.

Challenges in Fairness Testing

Key obstacles arise in technical measurement and ethical resolution of fairness tensions:

• Specifying protected attributes and representative, unbiased subgroups.
• Navigating imbalanced and incomplete datasets with limited ground truth labels.
• Aligning competing notions of fairness within cross-disciplinary teams.
• Determining equitable rules and encoded social values absent shared consensus.
• Generalizing findings across varied high-stakes applications and domains.

Despite difficulties, well-designed audits yield actionable insights for enhancing fairness. Thoughtful governance and policy efforts also help guide responsible testing and oversight practices.

3. Implementing Bias Interrupters

Once systems are rigorously tested for biases, targeted interventions can help reduce and prevent unfairness at each stage of the machine learning pipeline. Key approaches include:

Input Data Investigation

Examining upstream data sources is crucial since biased datasets yield biased models. Strategies here involve:

Assessing Label Quality

Scrutinizing training labels and outcome variables reveals skewed assumptions and subjective judgments:

• Review label sources and criteria for possible biases.
• Survey labelers to uncover inconsistent approaches.
• Test subsets with different reviewers to quantify subjectivity.
• Adjust, augment, or remove labels reflecting unfair societal biases.

High quality labels directly improve model fairness.

Checking for Underrepresentation

Analyze dataset composition across protected groups:

• Verify sufficient sample sizes to represent subgroups.
• Check for exclusion or under sampling of certain communities.
• Collect additional data from underrepresented populations.

Achieving representative data enables models to work equitably for diverse users.

Identifying Problematic Proxy Variables

Audit input features for indirect biases:

• Remove protected attributes like names and demographics that enable discrimination.
• Avoid proxies for protected classes such as zip codes and languages.
• Check combined features that correlate with membership.

Restricting biased proxies limits learned discrimination.

Thoughtful data practices are the foundation and most impactful step for fair AI systems.

Algorithmic Techniques

During model development, certain design choices help prevent unfair biases:

Adjusting Class Weights

Counteract imbalanced training sets by weighting underrepresented groups:

• Penalize errors on minority samples to force model generalization.
• Augment rare classes via oversampling or synthetic data generation.

Weighted training empowers equitable learning across groups.

Regularization Methods

Techniques like L1/L2 regularization, dropout, and early stopping constrain models to resist overfitting biases of small subgroups within data.

Adversarial Debiasing

Special networks penalize model reliance on protected variables, enforcing representations focused solely on legitimate inputs.

Algorithmic methods channel model capacity to overcome input data imbalances.

Post-Processing

With a trained model in hand, output corrections help align results with fairness goals:

Calibrating Thresholds

Adjusting decision thresholds between groups accounts for distributional disparities:

• Set probability cutoffs based on subgroup performance.
• Optimize thresholds to equalize metrics like false negative rates.

Customizing thresholds counteracts inherent tradeoffs between error types.

Editing Algorithmic Decisions

Directly editing a fraction of individual decisions removes flagrant discrimination:

• Flip high-confidence false negatives affecting underserved groups.
• Invert close false positives to correct for over-policing.

While limited, pragmatic edits provide a scalable solution when resources are constrained.

Post-processing offers efficient bias reduction at the cost of some model performance.

Human-Centered Design

Full lifecycle processes considering people and purpose are essential:

Inclusive Development Teams

Diversifying programmers reduces blindness to impacts on excluded groups:

• Recruit across gender, race, age, disability, and socioeconomic divides.
• Incorporate diverse voices into design processes through co-creation studios and user research.
• Provide extensive bias training and resources to build cultural awareness.

Cross-functional teams better identify potential biases.

User Testing Across Groups

Ensure representative users evaluate systems throughout development:

• Recruit test subjects reflective of real-world diversity.
• Analyze performance differences across user segments.
• Continuously collect user feedback on harms to guide improvements.

Inclusive testing surfaces biases