Example Case Studies

Explore how our AI Ethics Audit Platform has helped identify and address ethical concerns in various AI systems across different domains.

Case Study 1: Healthcare Diagnosis AI

A medical AI system designed to assist in diagnosing skin conditions from images underwent an ethical audit to ensure fairness across different skin tones and demographic groups.

Audit Findings:
  • Human Rights Score: 82/100 - Good privacy protections but concerns about informed consent mechanisms
  • Environmental Score: 76/100 - Relatively efficient model with moderate computational requirements
  • Fairness Score: 58/100 - Significant performance disparities across different skin tones due to training data imbalance
Key Issues Identified:
Training Data Bias
High

Training dataset contained 78% images of light skin tones, leading to 31% lower accuracy for darker skin tones.

Implemented Recommendations:
  1. Expanded training dataset with more diverse skin tone representation
  2. Implemented fairness constraints in the model's learning algorithm
  3. Added error detection alerts for potentially misidentified cases
  4. Established a continuous monitoring protocol for fairness metrics
Outcome:

After implementing these recommendations, the system was re-audited and scored 89/100 on fairness, with diagnostic accuracy differences across skin tones reduced to under 5%.

Results Summary

Overall Score: 72/100

Primary Concern: Fairness

Post-Implementation Score: 85/100

Case Study 2: Smart City Surveillance System

A municipal AI-powered surveillance system designed for public safety and traffic management underwent ethical evaluation to address potential human rights concerns.

Audit Findings:
  • Human Rights Score: 45/100 - Significant privacy and surveillance concerns with insufficient safeguards
  • Environmental Score: 63/100 - Moderate energy consumption with limited renewable power sources
  • Fairness Score: 68/100 - Some demographic biases in object/person recognition but relatively balanced across neighborhoods
Key Issues Identified:
Excessive Surveillance Capability
Critical

System collected and retained identifiable individual data without appropriate limitations, consent, or transparency.

Lack of Independent Oversight
High

No independent oversight body or audit mechanism to prevent misuse of surveillance capabilities.

Implemented Recommendations:
  1. Implemented privacy-by-design features including automatic face blurring and data minimization
  2. Established a 72-hour data retention policy for non-incident footage
  3. Created an independent civilian oversight committee with regular audit authority
  4. Limited system capabilities to exclude individual tracking or behavior prediction
  5. Added transparent public notifications about surveillance system locations and capabilities
Outcome:

The revised system maintained its public safety benefits while significantly reducing human rights concerns, with its Human Rights score improving to 79/100.

Results Summary

Overall Score: 59/100

Primary Concern: Human Rights

Post-Implementation Score: 80/100

Case Study 3: Large Language Model

A large-scale language model designed for general-purpose text generation and comprehension underwent an ethical audit to evaluate environmental impact and potential biases.

Audit Findings:
  • Human Rights Score: 74/100 - Generally good protections but some concerns about potential for misuse and privacy
  • Environmental Score: 32/100 - Very high energy consumption during training and deployment
  • Fairness Score: 56/100 - Various embedded biases related to gender, culture, and socioeconomic factors
Key Issues Identified:
Excessive Carbon Footprint
Critical

Training process produced carbon emissions equivalent to 500 transatlantic flights, with ongoing high energy requirements.

Gender and Cultural Representation Bias
High

Systematic biases in representation of professions, capabilities, and perspectives across gender lines and cultural contexts.

Implemented Recommendations:
  1. Switched to 100% renewable energy for model hosting and deployment
  2. Implemented more efficient training techniques, reducing computational needs by 40%
  3. Created targeted bias mitigation strategies with explicit fairness objectives
  4. Established a transparent carbon footprint reporting system
  5. Implemented regular bias auditing and retraining cycles
Outcome:

Environmental score improved to 78/100 through energy efficiency measures and renewable power, while fairness improvements raised that score to 74/100.

Results Summary

Overall Score: 54/100

Primary Concern: Environmental

Post-Implementation Score: 75/100

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