Since employment laws and taxation laws both comprise rules as opposed to standards, they offer the possibility to have ‘a clear feature engineering’ and, ‘train an accurate model’². For instance, the likelihood of success before the courts for a prospective claim for unfair dismissal could be predicted based on precedents with similar features. The same applies to tax appeals before the courts to challenge the computation of tax levied by the revenue authority.
A product which achieves this result will enhance efficiency by enabling the law firm to spend less time on routine paperwork, provide more accurate data and simultaneously enable more data to be handled and, result in less stress for lawyers especially in the current unprecedented times in which services are being rendered and ultimately, yield a higher revenue³.
Nevertheless, despite the many benefits which AI has to offer, it is suggested that AI may not be the most appropriate tool to help lawyers enhance their efficiency in view of the high level of skills and expertise which are required of them.
Instead, it is an integrated approach to machine learning which involves both the human input and technology which is the preferred approach to yield increased efficiency and help minimise risks e.g. improve the quality of research and acquire a competitive edge over competitors. In other words, supervised machine learning is the way forward. Atrium is a law firm which uses technology to perform automated routine tasks to help start-ups identify employees and draft their legal contracts4.
The ability to gear a culture by which lawyers see themselves as ‘transaction engineers’ as we have witnessed in the Silicon Valley law firms remains a wide leap into the practice of the law5 in Mauritius. Yet, it is highly desirable as lawyers operate within a highly competitive environment and the ability to facilitate transactions has the merit of reducing costs and fostering a role in the ‘co-creation’ of the infrastructure to deploy new technologies.
Data required to make the predictions
The first fundamental data which would be required to make predictions would be the clients’ profile (e.g. age, status, social background, gender), factual circumstances of their cases which could be categorized.
The second fundamental data would be the approach taken by our courts i.e. how the courts have interpreted a specific set of facts in the light of the prevailing legislation and whether there is a change of attitude by the courts or they have indicated to what extent they are prepared to draw certain exceptions.
What then are the opportunities and challenges ahead?
The main challenge concerns overcoming the lawyer legal privilege by which advice tendered to the client is confidential and it is the client’s privilege whether to waive it.
Therefore, the collection of data in order to translate it into information to predict an outcome raises issues of confidentiality and the accompanying issue whether this data collection breaches the Code of Ethics on lawyers’ professional conduct. From a Mauritian data law perspective, lawyers become data collectors who have mandatory reporting obligations to the Data Commissioner.
Conclusion
The rapid emergence of emerging technologies since the outbreak of the COVID-19 pandemic and the way it has revolutionised our entire modus operandi ie whether in the private sphere of our daily habits and way of living or our professional parameters and way of doing business and delivering our services and the accompanying mindset which they require must not be underestimated. The challenges which their implementation trigger are beyond an information technology perspective as they raise fundamental ethical issues for the legal profession.
