AI and Law: Addressing Bias and Accountability in the Digital Age

Imagine being wrongly accused of a crime, not due to human error or malice, but because an algorithm created to assist in legal processes has mistakenly flagged you as a suspect. This scenario is no longer a distant possibility but is becoming a very undeniable and substantial concern as Artificial Intelligence increasingly becomes more of an integral tool within the legal system. Whilst AI promises efficiency and accessibility, it also raises profound ethical concerns; from biased algorithms that disproportionately impact minority groups, to questions about accountability, the ethical implications of AI being used in law are immense.  

Bias In Algorithms

AI algorithms typically rely on large datasets to learn and make predictions; however, these datasets can contain biases that reflect historical inequalities or systemic discrimination. For instance, within the criminal justice system, if the data used to train an AI system disproportionately represents a certain demographic as offenders, the AI system may produce predictions that unfairly target those groups. This doesn't just lead to biased legal outcomes where some are more likely to be flagged or penalised than others, but it also reinforces existing inequalities leading to these marginalised groups being subjected to harsher treatment and damaging consequences based on demographic characteristics rather than genuine behaviour. 

An example of this AI bias in action is the case of Robert Williams, a black man from Detroit who was wrongly identified by facial recognition in 2020. This entailed an AI-powered facial recognition system mistakenly identifying Williams as a suspect in a shoplifting case despite significant differences between him and the guilty suspect. This error in the system's algorithm led to his arrest and detention, illustrating the exceedingly real dangers of relying on flawed technology within the justice system.

Whilst it is reasonable to expect that facial recognition matches should be treated as investigative leads; it should certainly not be used as definitive evidence to be blindly followed. In Williams' case, no additional evidence was collected before he was charged, which is particularly concerning as it contradicts the legal standard that every individual has the right to a fair investigation. Relying on flawed evidence can have devastating effects, not only for the accused but also for public trust in the justice system, weakening the ties between the community and law enforcement and leading to a possible increase in crime as well as undermining cooperation with members of the public to provide essential information to aid in solving crimes. 

This wrongful arrest highlights a fundamental flaw in the legal system, which can lead to numerous injustices, as reflected in a study conducted by the National Institute of Standards and Technology (NIST). The study found that facial recognition systems tend to misidentify people of colour at much higher rates than white individuals due to underrepresented minority groups in the training datasets. As a result, these systems produce disproportionately high false-positive rates for people of colour, demonstrating how reliance on flawed, biased technology can perpetuate injustices if not carefully scrutinised.

As these technologies become more ingrained in our legal systems, we must critically ask: Are they truly advancing justice, or are they deepening existing inequalities?

Accountability in AI Decision-Making   

As Robert William aptly states, "It's dangerous when it works and even more dangerous when it doesn’t work," referring not only to the potential errors of AI and technology but also to the uncritical trust we often place in them. One of the most significant ethical challenges of using AI in law is determining accountability when these systems make decisions that negatively impact people's lives. If an AI system delivers a flawed or biased legal decision, who should bear responsibility? Is it the developers, the users, or the legal professionals who rely upon the AI system? 

During 2020 when the COVID-19 pandemic prevented students from taking their exams, an algorithm was used to standardise grades based on teacher predictions and school performance. This led to the infamous A-level grading fiasco, as the system disproportionately downgraded students from less affluent schools, unfairly favouring those from higher-performing institutions. Public outrage quickly followed, with many protesting the reliance on a defective system. The UK government and Ofqual (the body responsible for overseeing the algorithm) were criticised for trusting an AI system that entrenched social inequality without ensuring proper safeguarding was in place. 

While the government eventually scrapped the algorithm-based results and reverted to teacher-assessed grades, the incident greatly emphasises the importance of human oversight in AI systems—especially when those systems are used in decision-making processes that can leave a lasting mark on people's lives. Without clear guidelines for accountability, AI can create situations where nobody is held responsible, allowing potentially harmful systems to go unchecked, to which without any consequences on individuals there will be no incentive for system improvement. 

Consequently, the A-level grading scandal provides a clear illustration of the ethical challenges posed by AI, particularly the difficulty in assigning accountability when technology goes wrong. As AI becomes more prevalent in legal decision-making, similar concerns will arise. In the legal context, where decisions can affect livelihoods, rights, and freedoms, the stakes are even higher. Who is responsible when an AI system renders an unjust verdict, misidentifies a suspect, or overlooks key legal nuances? 

To conclude, with current public bodies such as the legal system holding the potential to impose detrimental outcomes for any person caught in the justice system, it is paramount that we protect the rights of the individual through fair and reliable evidence. Implementing frameworks to regularly test and evaluate AI systems can enhance their reliability and strengthen the legal system as opposed to undermining it. The future of AI in law must strike a careful balance—embracing technological innovation whilst ensuring the ethical principles that assure justice and equality for all are thoroughly upheld.