How Law Firms Can Use AI Responsibly and Avoid Bias in Legal Practice

The legal industry stands at a crossroads: AI promises efficiency and insight, but misuse or embedded bias can lead to reputational risk, regulatory exposure, or worse. As AI becomes more deeply embedded in legal operations, law firms must adopt frameworks and guardrails to ensure fair, transparent, and responsible AI use.

Australia has adopted eight Artificial Intelligence Ethics Principles, developed by the government to ensure AI is safe, secure, and reliable.(Australia’s AI Ethics Principles) These principles serve as guardrails for deploying AI in sensitive domains—including legal practice.

Why Responsible AI Use Matters

AI systems can inadvertently mirror or amplify existing biases. For instance, in contract review, an AI model may over-flag clauses from jurisdictions with historically more litigation, unfairly penalising contracts from other regions. In predictive models, training data biased toward large firms or high-value clients can skew outcomes.

In the Australian legal environment, firms must be especially cautious. The Legal Services Board of Victoria has publicly warned that lawyers remain accountable for all legal work—even that produced or aided by AI tools. If AI outputs are faulty or biased, liability rests with the practitioner.

Moreover, there is increasing regulatory appetite for oversight. In 2024, the Australian government signalled moves toward formal AI rules requiring human oversight and transparency, especially for high-risk systems. (Australia plans AI rules on human oversight, transparency)

Identifying & Mitigating Bias in AI Models

1. Data Audits & Diversity‍

Ensure your training or reference datasets are representative across jurisdictions, firm sizes, industries, and client types. If your AI is largely trained on data from international mega-firms, that creates blind spots for mid-sized domestic practices.

2. Explainability & Transparency‍

Select models that can explain why they flagged a clause, rather than relying on opaque “risk scores.” This allows lawyers to understand and challenge decisions.

Continuous Feedback Loops

Enable attorneys to flag false positives or false negatives. Feeding those corrections back into training helps the model evolve and reduce “drift” over time.

3. Structured Ethics Audits‍

In Australia, CSIRO and Gradient Institute produced a comprehensive report titled Implementing Australia’s AI Ethics Principles, offering 26 actionable practices mapped to the ethical principles.(CSIRO / Gradient Institute Report) Regular internal or third-party audits aligned to that framework can expose gaps and tensions across principles (e.g., between accuracy and explainability).

Failures & Lessons Learned

AI-Generated False Citations (Australia, 2025)

In a high-profile Victorian case, a lawyer submitted court documents containing non-existent case citations generated by AI. The oversight was traced back to overreliance on AI without human verification. The court penalised the practitioner, and disciplinary attention followed. This underscores that human review is imperative when deploying AI in legal briefs.

Clifford Chance – Contract Automation

Internationally, Clifford Chance adopted AI systems that handled routine documents like NDAs, cutting review time by ~80%. However, flagged or high-risk clauses always required human review—a dual model of scale + oversight.

These real-world examples reinforce that the failure isn’t using AI—it’s using it without guardrails.

Best Practices for Ethical Deployment

1. AI as Assistant, Not Arbiter - Always position AI as support, not final decision-maker. Lawyers must review and approve outputs.
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2. ‍Comprehensive Documentation - Maintain logs of model versions, flagged changes, and user corrections to support auditability.
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3. Scope by Risk Level - Use AI for low-to-moderate risk tasks (clause tagging, prioritization), and reserve manual review for high-risk or client-facing deliverables.
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4. Client Transparency - Disclose to clients when AI assists work, its limitations, and that responsibility remains with the legal practitioner.
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5. Bias Testing Before Launch - Validate outputs across test sets (different jurisdictions, firm sizes, client categories) to detect systemic bias before live use.

Implementing Oversight & Governance

Firms should formalize AI governance as they would with any critical technology:

• Establish an AI governance committee including senior lawyers, technologists, and compliance experts.
• Use red teaming (simulated attacks or misuse) to test system vulnerabilities.
• Conduct periodic internal or third-party audits aligned with Australia’s ethical principles.
• Incorporate feedback loops where users rate and correct AI suggestions, feeding these back into model refinement.
• Keep abreast of evolving regulation; the government’s movement toward mandatory guardrails is already underway.

Over time, this builds a culture of accountable innovation: lawyers use AI confidently, clients benefit from efficiency, and regulators see defensible systems.

AI is reshaping legal operations, but the firms that succeed will not be those that rush deployment. They will be those that couple innovation with ethical discipline. By embedding bias mitigation, oversight, and transparency into your AI strategy, you unlock AI’s potential without violating client trust.

Want help structuring AI governance or deploying oversight protocols for your firm? Contact Teams Squared.