Introduction
Generative AI is rapidly transforming how attorneys research, draft, and deliver legal services. But alongside its potential comes a growing challenge — AI bias. In legal practice, a biased output isn’t just a technical issue; it can lead to compliance violations, reputational damage, and ethical risks.
Here’s what every attorney, in-house counsel, and law school needs to know about AI bias — and how to manage it responsibly.
1. Four Key Types of AI Bias
A. Training Data Bias
AI learns patterns from historical data, but if the data is biased, so is the output.
Example: A predictive sentencing AI trained on older case histories disproportionately recommends harsher penalties for minority defendants.
B. Confirmation Bias in Outputs
AI often amplifies assumptions in prompts or datasets.
Example: An attorney prompts AI to draft a brief supporting a specific argument, and the AI filters out contradictory case law — resulting in incomplete legal reasoning.
C. Selection Bias
When the data fed into the AI is incomplete or unrepresentative, outputs can skew.
Example: An AI reviewing employment agreements trained mostly on tech-sector contracts misses key clauses in healthcare-related agreements.
D. Automation Bias
Attorneys may over-trust AI outputs without verifying them.
Example: In the Mata v. Avianca incident, lawyers cited nonexistent AI-generated cases in court filings — damaging credibility and triggering sanctions.
2. Why Attorneys Can’t Ignore AI Bias
- Compliance Exposure: Misuse of biased AI outputs may violate anti-discrimination laws like Title VII and ADA.
- Ethical Obligations: ABA Model Rule 1.1 now implies a duty of technology competence.
- Malpractice Risk: Blind reliance on AI-generated legal content exposes firms to client disputes and reputational harm.
3. Guardrails for Responsible AI Use
Generative AI isn’t the problem — unchecked AI is. Attorneys must adopt responsible practices:
- Human-in-the-loop review → Always validate AI outputs before submission.
- Bias audits → Require vendors to demonstrate fairness testing where possible.
- Confidentiality protection → Avoid exposing sensitive client data in open AI models.
- Stay compliant → Understand frameworks like the Texas TRAIGA and emerging regulations shaping AI use in law.
Conclusion
AI bias isn’t just a technology challenge — it’s a legal imperative. Attorneys, law schools, and firms must build AI literacy and establish responsible practices to protect clients, uphold ethics, and stay compliant.