Ethics in the age of AI: Strategies for mitigation and their historical context

AI systems and technologies are not ethically neutral - they can perpetuate and amplify existing societal biases despite benevolent intentions

Historical biases in data collection and racial profiling are reflected in AI systems, as demonstrated by predictive policing tools like PredPol showing less than 1% accuracy and disproportionately targeting communities of color

Self-reinforcing feedback loops in AI systems can amplify sampling bias and create runaway effects, particularly when systems operate in closed loops without external validation

Key mitigation strategies include:

• Using smaller, purpose-specific language models instead of general LLMs
• Implementing safety prompts and meta prompts
• Grounding content in verifiable sources
• Conducting red team testing for adversarial scenarios
• Fine-tuning models with balanced, representative data
• Building multiple layers of safety systems

Underrepresentation in training data leads to poor performance for marginalized groups, as seen in medical AI, facial recognition, and recruitment tools

Responsible AI requires:

• Understanding model limitations and biases
• Regular testing and monitoring
• Balanced feedback loops with human oversight
• Transparent sourcing and documentation
• Proactive risk assessment and mitigation

Leaders in technology have a responsibility to advocate for proper representation in datasets and implement ethical AI practices that protect vulnerable populations