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In a world where Artificial Intelligence (AI) is gaining ground in the legal sector, in-house legal teams must take an active role in ensuring the ethical and responsible use of these tools. The misuse of AI systems can already result in serious penalties for companies: from 2025, they can be penalised both for engaging in practices that violate competition law and for failing to comply with existing AI regulations. In this context, this article analyses a practical guide to identifying biases, auditing the use of legal AI and maintaining a solid ethical standard in your organisation.
This article is also available in Spanish.
In this article, you will find:
- Why is ethics in legal AI critical for in-house teams
- Checklist for assessing bias and transparency in AI providers
- How to implement an internal governance framework for legal AI
- Ethical failures in AI and how to avoid them
- Recommendations for integrating continuous human auditing
1. Why is ethics in legal AI critical for in-house teams
AI tools are not infallible; they can reproduce or amplify biases if the training data is biased or their design lacks adequate oversight. This has already led to serious situations: courts in the US and Australia have sanctioned lawyers who presented cases with false case law generated by AI, without having verified its validity. Can your team afford that reputational risk?
Legislation such as the European AI Act requires transparency, impact assessment and human oversight for high-risk applications, including legal systems. Ignoring this regulatory environment is not only irresponsible: it is dangerous for companies with a global reach.
You may be interested in: How to choose the best legal AI to optimise your legal work
2. Checklist for assessing bias and transparency in AI providers
When selecting an AI solution, whether for contract management, assisted drafting, or predictive analytics, don't just test its usability. Ensure that it meets ethical criteria from the design stage. Here is a useful checklist to keep handy when hiring new AI providers:
|
Ethical assessment |
Verification |
|
Data sources |
✅ Does the provider clarify the origin and diversity of training data? |
|
Bias audit |
✅ Does it present internal studies or tests (e.g., racial bias testing, IEC)? |
|
Explainability |
✅ Does it explain how it reaches decisions (interpretability)? |
|
Human oversight |
✅ Does it allow for intervention or validation of alerts before automated decisions? |
|
AI Act compliance |
✅ Is it classified as high risk, and does it comply with impact and control obligations? |
Demand these answers before signing. Your legal team must be the ethical guardian of the business, and with the rise of legal tech solutions for the department, vigilance must be exquisite.
3. How to implement an internal governance framework for legal AI
Having responsible suppliers is only part of the equation. The second step is to build a robust internal system.
Key steps:
Appoint an AI ethics officer
This person could be part of the legal department: someone who reviews models, data and bias alerts. This is not a technical role; it is a guardian of ethical processes.Design an internal AI policy
Define what use is acceptable and what is prohibited. Include human validation flows. This document must be accessible and reviewable.Train the legal and business teams
Everyone must understand the risks: errors, bias, and legal non-compliance. Brief training prevents serious consequences.
Conduct regular audits
Review internal metrics: number of automated alerts, cases rejected by supervision, and errors detected. Demand transparency from the supplier.
You may be interested in: Boosting your compliance from the heart of legal operations
4. Ethical failures in AI and how to avoid them
A. Fabricated court case
An Italian solicitor submitted documents containing unverified AI-generated legal citations. Although, in this particular case, the court did not penalise the irresponsible use of the AI tool because it was not done in bad faith and did not harm the other party, there are penalties for procedural actions carried out in bad faith or with gross negligence.
⚠️ Lesson: never use AI results without human verification; be especially cautious about plausibility, and do not even assume that what seems correct is correct.
B. Accidental leakage of confidential information
An in-house legal team used a generative AI model to draft a complex contract and, as part of the prompt, included sensitive data about ongoing litigation, including employee names, internal figures, and defence strategies. Although the system did not store this information, the mere fact of entering it into a platform not controlled by the company constituted a serious violation of internal confidentiality policies and a possible breach of the data protection framework.
⚠️ Lesson: never enter sensitive or confidential data into AI tools without clear contractual guarantees of privacy and security. It is advisable to work with internal models or environments controlled by IT/Compliance. If you cannot avoid them, always anonymise the information.
You may be interested in: Libra: Generative Legal AI
Unintentional discrimination in review processes
An in-house lawyer at an international company implemented an AI tool to analyse contracts with suppliers and classify risks. Unbeknownst to them, the model was trained with biased historical data, which resulted in higher risk scores being assigned to suppliers from certain regions or sectors, without a sufficient legal basis. The legal team detected the pattern late, after receiving complaints from several strategic partners.
⚠️ Lesson: All AI implementations must include active bias review, especially if the technology makes decisions that affect people, suppliers or customers. In-house lawyers must take on a watchdog role and ensure that results are evaluated from an ethical and compliance perspective.
5. Recommendations for integrating continuous human auditing
To close the cycle, the legal team must continuously monitor how AI impacts decisions and risks. Here are some specific recommendations:
Define ethical KPIs (Key Performance Indicators)
Examples: percentage of alerts rejected based on human judgement, average manual review time, cases with errors vs. total generated.Create a feedback channel between legal and suppliers
When you find erroneous outputs or risks, document them and request adjustments. This is part of the service contract.
Annual review of the ethical framework
Adjust clauses, flows, and roles each year as regulations or technologies evolve.
Communicate risks and successes internally
Share success stories (such as early detection of biases) and errors avoided thanks to legal involvement. This reinforces a culture of accountability.
AI offers enormous opportunities for legal efficiency and accuracy. But without a strong, strategic ethical approach, it can also become a serious threat. The consequences are clear: automatic failures, biases, and severe regulatory penalties.
It's time to leap into AI: as an in-house lawyer, you are in a privileged position to ensure that your company leverages AI responsibly and competitively.
Request a demo with our experts and learn about Bigle's legal AI, Libra
