The Necessity and Challenges of Establishing AI Laws

With artificial intelligence growing more common in daily life, current laws find it hard to handle emerging issues. Since older regulations focus on human actions, they rarely cover damage from automated systems powered by AI. When AI gains stronger functions and wider use, should rules change to secure responsibility and public safety?

If an AI causes real harm, determining liability gets complex - does it fall to creators, developers, companies behind it, or even users operating it? This fundamental question lies at the heart of modern AI governance debates.

The fast spread of artificial intelligence into key parts of life - like health care and legal judgment - opens a risky hole in safety that today's rules aren't built to close. When clear policies don't exist, firms building these strong technologies usually oversee their own actions; past examples show businesses tend to favor earnings more than careful use.

A major concern comes from how complicated current AI is - the so-called "black box" issue. While broken machines reveal flaws you can see, smart algorithms decide things using hidden math patterns even designers struggle to follow. Should authorities ignore oversight now, they might let unknown systems enter essential services without anyone being able to clarify or manage them.

This absence of clarity makes trust difficult without legal supervision. Yet researchers Brandon Garrett and Cynthia Rudin claim such obscurity poses serious concerns - especially within criminal justice, where algorithms forecast criminal behavior. Since AI often operates invisibly, professionals like attorneys or magistrates may miss critical flaws behind its conclusions; grasping the meaning of automated advice becomes even harder.

"Unless AI is interpretable, decisionmakers like lawyers and judges who must use it will not be able to detect those underlying errors, much less understand what the AI recommendation means."

— Garrett and Rudin, 293

When experts rely fully on machines believing them neutral, while unseen data biases steer incorrect outcomes, harm follows easily. Regulation should step in - to stop unchecked dependence on systems whose workings remain unclear.

Furthermore, lacking strong regulations, firms rarely feel pushed to pause and look for issues like unfairness. Since artificial intelligence draws knowledge from information, flawed records lead straight into repeated mistakes by machines. In the Boston University Law Review, Andrew Selbst points out these flaws aren't just chance occurrences. Instead, he notes that the technology introduces unique complications, such as:

"The inability to predict and account for AI errors."

— Selbst, 1326

When legal systems equate AI with regular programs, businesses may simply deny responsibility after automated tools harm protected individuals. Since machines work unlike human thinking, Selbst argues that "AI disrupts our typical understanding of responsibility for choices gone wrong," making standard negligence laws ill-suited to the technology. Clear rules must require firms to reveal inner workings - showing safety prior to public launch.

Once we agree rules are necessary, the justice system must tackle a tough issue: where does AI fit? Could it be seen as a tool, similar to a mower? Might it count as assistance, much like hired help? Some forward-looking minds even propose treating it as a legal entity. The label matters - it shapes what happens in court when things fail.

Some experts in law have looked into giving AIs a kind of official standing. Such status might let machines face lawsuits on their own, carry insurance, or cover harm using self-held resources - similar to how companies operate. Legal academic Lawrence Solum has examined if awareness or smart behavior must come before rights under law. He questions:

"Whether an AI could possess the sort of moral status that would entitle it to the full range of legal rights and duties."

— Solum, 1232

When systems reason and decide without human help, certain thinkers suggest they ought to function like separate parties legally - one able to manage property meant to compensate damage they create.

Yet calling AI a legal person brings serious ethical and real-world issues. Legal rules assume penalties stop wrongdoing - computers can't be punished this way. A machine doesn't experience guilt, dread confinement, or mind financial loss. Visa Kurki cautions about fitting AI into human-style legal roles. Instead of safeguarding society, giving AI rights may let developers avoid blame. He suggests that such a move could effectively:

"Protect the human owners from liability," shifting risk away from those who design it.

— Kurki, 17

If one AI were treated as its own legal being, the business behind it might assign fault to that system during a crisis - then drain the AI's funds to cover costs while shielding its main finances. Such setup lets firms push boundaries risk-free.

Yet considering AI purely as a 'product' poses challenges since software acts differently from tangible items. According to a RAND Corporation study:

"It is unclear whether courts will define AI as a product," making product liability rules harder to enforce.

— RAND Corporation, 4

Conventional regulations expect products to remain fixed post-manufacture. In contrast, AI evolves even after deployment. If a robot picks up harmful actions from its user, claiming the maker delivered a flawed item isn't straightforward. Because of this uncertainty, courts struggle to handle cases around smart machines - new terms are needed but missing.

If we don't hold the AI accountable, then responsibility spreads across multiple actors - the so-called 'many hands' issue arises. It becomes hard to identify one person at fault when numerous individuals contribute to building and operating tech systems. Take a self-driving vehicle striking a pedestrian - where does liability lie? Could it rest with the coder drafting algorithms, the firm supplying training inputs, or perhaps the operator distracted behind the wheel?

In today's legal setting, victims must show someone didn't use proper caution. Still, because AI behaves unpredictably, meeting that requirement becomes difficult. Developers may claim they followed common programming methods; meanwhile, users say they trusted built-in safeguards. Because of this back-and-forth, injured parties are left with no clear resolution. As Selbst points out, when people engage with advanced technology:

"The user's failure to prevent the harm is often a result of the system's design," yet courts could hold them responsible anyway.

— Selbst, 1345

In short, artificial intelligence creates serious challenges for current laws. Although granting AI 'personhood' seems like a solution to some, it may mainly let companies escape responsibility. Instead of accepting this, stronger rules should target real dangers while keeping human oversight central.

Rather than claiming AI is too complicated to control, we need clear information directly from developers. When risky AI systems cause harm, firms shouldn't escape blame by claiming secrecy. Holding them liable ensures progress doesn't come at the cost of fairness.

Machines act - yet people must answer for the damage they create.