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datasets that may contain historical biases, reflecting
societal and systemic inequities (https://doi.org/10
.1080/08839514.2021.2013652). In legal applications,
biased outputs can reinforce discriminatory practices,
affecting sentencing recommendations, contract
negotiations, and risk assessments. For example, if
an LLM is trained on case law reflecting historically
harsher sentencing for specific demographics, its
predictive analytics may perpetuate those disparities.
Similarly, biases in legal language, such as gendered
terms in employment contracts, can continue through
automated drafting tools, LexisNexis Canada reports.
These biases pose legal and reputational risks for firms
implementing AI without safeguards.
According to LexisNexis Canada, mitigating bias
requires diverse and representative training data,
ongoing model audits, and human oversight.
Transparency in AI-driven methodologies is key, with
organizations needing to disclose sources of training
data, reports Computer.org. Bias detection algorithms
can help identify and correct discrimination patterns
before they influence legal decision-making. The
European Commission has called for industry-wide
standards requiring fairness testing and accountability
measures for AI tools in legal practice. Thomson
Reuters emphasizes that continuous evaluation
and collaboration between legal professionals,
technologists, and policymakers are essential to ensure
ethical and equitable outcomes.
TRANSPARENCY AND EXPLAINABILITY IN
LEGAL TECH
According to Bender and colleagues, legal professionals
must understand how GenAI models generate outputs
to assess their reliability and fairness. However, the
European Commission indicates that these models
often function as black-box systems, making it
difficult to trace specific reasoning behind generated
responses. This lack of transparency complicates legal
accountability, Malik reports (https://www.computer.
org/publications/tech-news/trends/ethics-of-large-
language-models-in-ai). To address these concerns,
firms should implement model auditing frameworks
that assess output consistency and flag potential
biases, according to Xu. LexisNexis Canada reports that
attention visualization, prompt engineering, and fine-
tuning on curated datasets improve interpretability.
Additionally, integrating human oversight where
attorneys validate AI-generated content before use
helps ensure accuracy and reduces ethical risks,
according to Korum Forum.
Thomson Reuters writes that beyond model auditing,
organizations should establish clear guidelines on AI
explainability tailored to legal standards. According
