Access to Justice, AI, and the Myths That Hold Us Back

I never planned on creating an AI employment agreement analyzer and I certainly did not expect it to change my perspective on access to justice. Access to justice is an issue I care deeply about, but making significant contributions felt out of reach for a law student like me. In fact, on the morning of The Justice Hack, an event where multidisciplinary teams compete to build the tools aimed at justice challenges, I was so overcome with anxiety and imposter syndrome that I nearly didn’t show up. I assumed meaningful access to justice work required professional seniority and technical expertise that I did not yet have.

To my great surprise, my partner and I won the competition.

Participating directly in justice innovation transformed how I see my future in law. I realised that:

• Impactful solutions don’t need to be perfect,
• Technical knowledge is not the prerequisite that I imagined it was, and
• Technology alone is not a solution without human judgment and engagement.

Those realizations shaped the myths I want to challenge here.

Myth 1: Only Perfect Solutions are Meaningful

One of the hardest parts of innovating in access to justice is choosing where to begin. Even in the narrowed context of employment law, it took my partner and me hours to settle on a tool to close the knowledge gap between employers and employees in employment agreements. We had to overcome the persistent worry that our contribution was too small to matter in the face of a systemic problem as large as access to justice. The way we overcame this was not to focus solely on what I’d learned in law school, but instead to draw on our shared experience as new job seekers signing employment agreements we barely understood and had little power to challenge.

We made something that solved a problem we had firsthand experience with. We built the tool we wished we had. My legal education came in not at the idea stage, but in setting its boundaries. For example, ensuring the tool only provided legal information and adhered to privacy principles like limiting data collection and use. Our tool doesn’t solve every employment injustice, particularly for people without a written contract. However, it does represent one small step towards something better, even if it isn’t a complete solution.

Myth 2: Innovation Requires Technical Expertise

Technical expertise improves outcomes, but it shouldn’t deter experimentation. To be candid, I didn’t do any coding myself during The Justice Hack. There were numerous, equally important tasks that better aligned with my skill set, including sourcing employment agreements for testing, providing user-experience feedback, and developing our pitch and presentation. However, I have since learned the process my partner used to build our prototype: vibe coding.

Vibe coding is a development method where users prompt generative AI in plain language for what they want built. Because vibe coding uses plain language, you don’t need to be a software expert to make a working prototype. Lowering the technical barrier to experimentation matters because it allows more legal professionals to participate in shaping the tools that increasingly affect justice. To get started, you do need to learn the foundational principles of coding because that informs your vision and approach. While the quality, complexity, and scalability of your prototype will increase with your level of knowledge, at its core, vibe coding relies on transferable skills many legal professionals already have like file organization, the ability to break down a complex goal into discrete steps, and the ability to communicate those steps clearly and precisely.

With some trial and error, an adventurous beginner can make a simple working prototype and see an idea move from concept to prototype.

Myth 3: AI Is Only Good or Evil

In access to justice contexts, AI is neither wholly good nor wholly bad: its impact depends on how legal professionals, community partners, and users engage with it. The risks of AI are serious, from environmental damage to hallucinations. At the same time, AI lowers the barriers to tasks that used to require a lot of expertise to begin. People turn to AI in legal contexts because it is easier and cheaper than consulting a lawyer directly. Yet the challenges created by the accessibility of AI are that users often lack the necessary background knowledge to assess the quality of the output.

This tension directly informed our vision of flagging clauses for further review by the user and facilitating optional lawyer consultation. This approach operates on a similar principle to unbundled legal services where users do what they feel comfortable with, and lawyers, aided by the flagged contract can provide quick, simple, and targeted assistance that reduces the cost for the service. Any generative AI can perform a similar task, however, the emerging challenge for legal professionals is dealing with clients and self-represented parties using AI without understanding its limitations. We could continue to be reactive or have a say in building easy-to-use tools informed by the realities of the legal system.

Final Reflections and Next Steps

Anyone interested in access to justice innovation should not let preconceptions about their ability to make a difference, technical expertise, or views on AI hold them back from experimenting. Gather snacks, a few brave friends from different walks of life, and pick one day to come up with a solution to a time you haven’t been able to access justice. Spend an hour or two learning the basics of coding, then experiment with AI prototyping. In this type of low-stakes environment, the worst outcome is that the only thing you accomplish is spending a fun day with friends. If you successfully make something, it could change people’s lives.

Access to Justice BC supported the Justice Hack BC that Emily was a part of and took place on October 25 and 26. To learn more about it, check out The Justice Hack – Improving Access to Justice, One Hack at a Time.