Algorithmic
Bias Protection
As algorithms take on greater social responsibility, the risk of systemic bias grows exponentially. We provide bespoke coverage against employment, credit, and service discrimination claims arising from automated decisions.
The AI / Modern Angle
AI hiring tools, credit scoring algorithms, and automated service delivery systems are increasingly facing regulatory scrutiny and litigation. When an algorithm trained on biased data systematically disadvantages protected classes, the resulting legal exposure extends from the model developer to every organization in the deployment chain. The EU AI Act classifies employment and credit scoring as "high-risk" AI applications, mandating bias audits and transparency. Our policies are structured around these emerging regulatory frameworks, covering the full spectrum from pre-deployment audit failures to post-deployment discrimination claims.
Core Coverage Points
Employment Bias Defense
Protection against recruitment filter discrimination claims when AI hiring tools exhibit disparate impact on protected groups.
Credit Scoring Liability
Defense for automated lending and financial decisions that may produce discriminatory outcomes across demographic groups.
Legal Defense Costs
Comprehensive coverage for civil litigation fees, regulatory fines, and settlement costs arising from bias-related claims.
Remediation Funding
Funding for mandatory bias audits, dataset rebalancing, and algorithmic retraining required by regulators or courts.
Reputation Recovery
Crisis management and PR coverage for algorithmic bias incidents that threaten brand integrity and public trust.
We Don't Just Insure —
We Help You Audit
Our philosophy extends beyond reactive coverage. We partner with our clients to proactively audit algorithmic systems, identify bias vectors before they become liabilities, and build equity into the architecture from the ground up.
Our team of AI ethics specialists and actuarial scientists evaluate your models against fairness frameworks including demographic parity, equalized odds, and calibration metrics — ensuring your algorithms serve all populations equitably.
"We don't just insure the outcome; we help you audit the engine to ensure equity is built into the architecture."