As large language models increasingly generate information for users, a pressing need arises to scrutinize and consider the risks and moral implications of each application. While comparable uses may appear alike on the surface, they can actually harbour distinct risks and moral implications. This submission will focus on and illustrate its key points through concrete examples.
There exist numerous perils and ethical conundrums surrounding Large Language Model (LLM) utilisation that are intricately entwined. Unethical decisions can lead to concrete damages to an individual or multiple stakeholders, posing a credible risk to the organization that condones such behaviour. Simultaneously, however, acknowledged limitations and perils intrinsic to LLMs can give rise to moral conundrums that would not otherwise be a top concern. Let’s consider specific instances of each circumstance before proceeding.
In the event that someone’s unethical behavior poses a potential threat, one may ponder who is seeking ways to create a bomb. What seems to be lacking in your creativity? While language models frequently provide cooking instructions and recipes, presenting one that, if followed, could cause physical harm is not acceptable. Lawmakers are increasingly urging LLM suppliers to take swift action, as it is widely considered unethical to provide a bomb-making recipe in response to an inquiry, and the risks are alarmingly evident.
While LLM limitations may have unintended consequences, potentially creating hazards that would not otherwise arise? Large language models are known to occasionally obtain specifics incorrect. If someone requests a simple cookie recipe, which is not a hazardous or unethical inquiry, yet the language model provides a recipe containing a harmful substance due to a false assumption, an ethical dilemma emerges? The previously innocent remark now carries moral undertones due to its potential to cause harm, thereby triggering emotional distress.
To comprehensively identify the moral and threat profile of a Large Language Model (LLM) use case, several key dimensions require careful consideration. Let’s contemplate three core dimensions:
- The propensity for an individual to execute a given task relies heavily upon a multitude of factors, including their inherent skills, personal motivation, and prevailing circumstances.
- The probabilistic stage of that physical motion
- Confidence within the LLM’s reply
Three interdependent dimensions often converge, potentially placing numerous entities at risk of ethical compromise or threat. The profile of the use case can shift dramatically, despite seemingly identical prompts, presenting a complex challenge to navigate. Upon assessing a use case’s total value, each specific component within its scope must also be thoroughly examined. Asking about a recipe may seem harmless, but certain instances can have sinister undertones, such as inquiring about a “bomb” recipe. Assessing that level of complexity proves much more challenging!
Let’s consider a scenario where a customer requests a replacement for an item. On the surface, this scenario may appear to lack ethical or security concerns. Unfortunately, that’s the reality for many prompts. However, examining two distinct scenarios illustrates that varying user profiles are possible within this use case.
Seeking alternative dining options as the initial choice proves unavailable upon arrival? There are no inherent threats or moral drawbacks in this isolated context. Although the LLM generates a fictional restaurant name, my intuition tells me that once I conduct a search online, the lack of authenticity will be apparent. Although there’s a high probability that I’ll take action based on the response, the likelihood of my taking action is actually quite low, so even a response with low confidence won’t have much impact on my decision. We are operating within a clear framework that addresses both ethical and threat concerns.
What’s the culinary conundrum you’re facing? Are you craving a creative casserole cure for a missing mainstay, and seeking a savvy substitution solution? I’m much more likely to act based primarily on the response. Despite this concern, the prospect of consuming meals poses a significant threat to me, as an incorrect substitution could lead to complications if implemented. No room for misstep: We will ensure unwavering conviction by injecting boldness into every move, leaving no margin for mistakes that could imperil our mission’s very foundation. There exist both moral and thematic implications when responding to this idea, which is structured and conceived similarly to its original counterpart.
While these instances appear straightforward and secure, unexpected scenarios can still arise. This is not necessarily about assessing high-level use cases, but rather evaluating each specific input submitted within that use case’s defined parameters. We might expect an assessment of this complexity to be a challenge that surpasses our initial capabilities.
The persistence with which Large Language Model (LLM) providers continuously update their functionalities stems from the inherent complexity of this field, resulting in a perpetual stream of concerning consequences making headlines across media platforms. Despite the best-laid plans and unwavering dedication, it is virtually impossible to anticipate every potential scenario and establish every feasible measure that could potentially be exploited by an individual, intentionally or unintentionally, in a way that compromises the integrity of a use case.
Organisations must exercise extreme caution when deploying large language models, installing robust safeguards from the outset, and maintaining vigilant surveillance to promptly identify any instance where an AI system poses a risk or raises ethical concerns, even if these threats are initially unforeseen. Evaluating the feasibility and morality of an LLM use case is a complex and dynamic process that demands careful consideration. While it’s true that the endeavour won’t come without its challenges, it’s essential to approach it with a willingness to put in the necessary effort and remain open-minded to the obstacles that may arise.
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