What Mitigation Meaning, Applications & Example
Strategies and techniques for addressing risks and potential negative impacts associated with AI systems.
What is Mitigation?
Mitigation in AI refers to the strategies and techniques used to reduce or eliminate potential risks, biases, or unintended consequences associated with AI systems. It involves addressing challenges that arise during the development, deployment, and use of AI, ensuring that its operation remains ethical, fair, and aligned with desired outcomes.
Types of AI Mitigation
- Bias Mitigation: Techniques used to identify and reduce bias in AI models, ensuring fairness across different demographic groups.
- Risk Mitigation: Strategies to minimize the risks associated with AI deployment, such as ensuring robustness against adversarial attacks.
- Ethical Mitigation: Addressing ethical concerns by ensuring transparency , accountability, and alignment with societal values.
- Environmental Mitigation: Reducing the environmental footprint of AI systems by optimizing energy use and resource consumption.
Applications of Mitigation
- Fairness and Inclusion: Using mitigation techniques to ensure AI systems are fair and inclusive, especially in sensitive areas like hiring, lending, and law enforcement.
- Security : Implementing strategies to safeguard AI systems from attacks or manipulations that could compromise their integrity.
- Sustainability: Reducing the environmental impact of AI technologies, particularly in terms of energy consumption and carbon footprint.
Example of Mitigation
An example of bias mitigation is applying algorithms like re-weighting or adversarial debiasing to ensure an AI recruitment tool does not favor one gender or ethnicity over others in the hiring process.