AI Ethics, Bias Auditing & Algorithmic Accountability
Fairness claims are becoming enforceable obligations. From EEOC guidance on algorithmic hiring to state and local bias audit laws and the EU AI Act's transparency duties, organizations must be able to show — not just assert — that their systems are tested and accountable. We design the legal frame for audits: scope, privilege, methodology, remediation, and disclosure.
Who this is for
Employers and HR tech vendors, lenders and insurers, healthcare AI, criminal-adjacent tools, public-sector deployers, and companies facing investor or customer demands for responsible AI evidence.
What you get
- Algorithmic impact assessments aligned to NIST AI RMF and sector rules
- Bias testing and fairness audit scoping with counsel-managed privilege where appropriate
- Bias audit vendor selection, contracts, and report handling (including NYC Local Law 144-style regimes)
- Red-teaming and evaluation protocols for safety and misuse
- Remediation plans and monitoring cadence
- Disclosure language for transparency reports and regulator responses
Frequently asked questions
What is an algorithmic impact assessment?
A structured review of an AI system's purpose, data, outputs, and affected populations — identifying discrimination, explainability, and rights risks, then documenting controls and residual risk. Increasingly, specific statutes require versions of this for high-impact decisions.
Are bias audits privileged?
Not automatically. Privilege depends on who directs the work, for what purpose, and how the report is used. We structure audits with litigation and regulatory risk in mind so findings can inform remediation without unnecessarily creating discoverable admissions.
How often should we re-audit a model?
At minimum on material model, data, or use-case change, plus a scheduled cadence (commonly annually for high-risk systems) and after incidents or drift alerts. Continuous monitoring with trigger-based deep audits is the emerging best practice.