Pop culture has affected the general understanding of artificial intelligence. The idea has shifted from a modified villain such as Agent Smith to good robots. This is an exaggeration by Wall-E. Hollywood adds a dramatic effect, though there is truth tied up around these visionary characters presently. The explorations into AI are fundamentally perfect. However, several industries are applying the little knowledge to enjoy AI adoption thereby doing away with manual and redundant tasks.
The legal sector should expect more promising potential to boost efficiency by programming high-volume tasks like legal research, diligence, electronic discovery, document drafting, and automating routines. A study conducted some time demonstrated that approximately 90% of the respondents gave a negative response when questioned about the law firms integrating artificial intelligence.
The results were surprising, but another assessment asking why showed that approximately 80% of the law agencies fear that the new technology might replace human resources. Also, they feared that the technology tools would eliminate the need for paralegals and attorneys. William Aron, a Santa Barbara criminal attorney, said at first he was hesitant to implement AI in his law practice for this very reason. He then went on to say that he soon realized it didn’t replace human jobs, only made them easier and allowed them to get more done. Already, the law sector is known to be among the slower-moving in implementing innovative and creative technologies. Indeed, many law firms do not prefer to lead, instead, they like following others.
An intensive assessment of a situation helps to determine the depth of the matter. Therefore, not all legal positions will be eliminated by artificial intelligence. Again, the corporate law departments and legal firms will prepare in advance for the interference.
A Glance at AI History
Officially, artificial intelligence was founded in 1956 at Dartmouth College. This was six years after the Turing Test, developed by Alan Turing. 60 years later, Eugene Goostman, a chatbot managed to trick the legal judges to believe that he was a human. This was in 2014 when he passed the Turing Test.
Therefore, progress in the AI sector does not come overnight and extra guidance on how to solve the minor issues using AI can be demonstrated by professional developers. Implementing artificial intelligence is a more complicated issue unlike how one can perceive it.
Top-Down Problem Solving
A while ago, only the routine tasks with given rules can be automated. However, the top-down technique starts with the business question or problem. There is a primary belief in a corporate law department and has been swindled by several law firms or vendors on some unconfirmed fees or expenses.
The approach will select the suspected vendors and review the invoice line, contrary to the contract. Alternatively, billing guidelines are available to check the correctness of the invoicing or whether they are unconfirmed or excessive charges. After mastering all the rules, programs can be developed to authenticate the respective data.
For many years, computer programmers developed several codes to substantiate the business rules and program workflows for institutions. However, with time, the rules grew, and the programs became more complex, and therefore expensive to maintain.
Bottom-Up Problem Management
Also, one can create data and proceed to build valuable rules and insights. This technique has some machine-learning algorithms meant for a bigger quantity of data as well as computing power to discover and establish to guide the business rules. This did away with deliberations of how the difficult problems were or how the solutions should be modified for automation.
This approach facilitates starting with algorithms and invoice data to determine whether there are impressive anomalies or trends. After screening through the insights, the system will spot spikes like travel expenses for certain cases that do not need traveling. Invoice reviewers can consider it useful to audit the system accordingly.
AI algorithms theory might deviate from real life, and so implementing it might be difficult. Labeling data is costly and difficult because the data is limited and dear to collect. Also, a lot of data is needed to train since the machine’s responses increase proportionally. The explanation challenge is also a concern because AI products are crucial in the decision-making process. Therefore, the business will take clear steps to solve any prevailing problem.
The contemporary artificial intelligence systems cannot solve the regularly witnessed intelligence problems. Machines cannot easily emulate the brain to apply the bottom-up and the top-down approaches because it involves presenting a persuasive and cohesive case and connecting dots to a divergent jury. Legal services may entail a combination of automated tasks, and others remain traditional especially the ones that need special experiential knowledge.
Artificial intelligence is expected to progress in the next few years, and 2020 has been a lesson that it is easy to fall. Therefore, legal firms must always have this in mind to embrace the technology and remain updated. The lawyers who acquire technology and change their culture to a hybrid legal tech will experience greater rewards. AI’s scalability can enhance the easier transition to the future to continue offering independent judgment, paying attention to the critical work for the customers.