Section 1
Introduction
The use of generative AI tools like ChatGPT in academic settings has sparked much ethical debate recently. As Bahroun et al. (2023) discussed, these tools can help students significantly in researching, writing papers, answering questions, and more. However, they also open the door to potential misuse for cheating and plagiarism. This raises critical ethical questions for developers of such tools – do they have an obligation to incorporate preventative features (Farrelly & Baker, 2023)? This essay will analyze this question using ethical theories of consequentialism, utilitarianism, and more from sources like Shafer-Landau (2023). It will assess the competing answers and provide a supported conclusion on whether developers ethically should prevent generative AI misuse. The argument that developers have no ethical obligation relies on the stance that technology is ethically neutral. Just as a pencil company is not responsible if someone uses a pencil to write a threat, AI developers did not create the tools explicitly for cheating. However, strong arguments exist that developers should incorporate preventatives. Consequentialist and utilitarian theories state that ethical choices should maximize benefits while minimizing harm for the largest group (Shafer-Landau, 2023). Widespread misuse waters down academic merit and enables cheating.
Section 2
Permissibility of the Question
The argument that developers have no ethical obligation to prevent misuse of their generative AI tools relies on the stance that technology is ethically neutral. Just as a pencil company is not responsible if someone uses a pencil to write a threat, AI developers did not create the tools explicitly for cheating and hold no accountability (Shafer-Landau, 2023). Preventing misuse would also go against principles like academic freedom and individual responsibility. From a software design perspective, the use of monitoring tools also raises privacy issues. Overall, there are reasonable ethical arguments on both sides. The ACM code of ethics does not clarify AI developer obligations, only stating a general duty to “take action not to knowingly do harm” (ACM, 2023). However, ethical solid arguments do exist that developers should incorporate preventative features. Consequentialist and utilitarian theories state that ethical choices should maximize benefits while minimizing harm for the largest group affected (Shafer-Landau, 2023). Widespread misuse of generative AI significantly waters down academic achievements, advantages cheaters over diligent students, and diminishes learning integrity institutions. This leads to more aggregate harm than good.
Section 3
Competing Answers
Critics highlight how unconstrained AI could automate soft student skills meant to be gained through writing research projects manually (Laakso, 2023). From this view, developers allowing misuse carry ethical culpability for directly enabling outcomes shown to undermine educational and societal goals. They possess the capability and thus bear at least some obligation to mitigate detrimental impacts. On balance, software developers have an ethical responsibility to implement reasonable misuse prevention measures in academic generative AI systems. Allowing these advanced tools for learning assistance to enable widespread negative impacts conflicts with the consequentialist goal of producing the most utility through actions (Shafer-Landau, 2023). Educational cheating made easy via AI poses significant harm to learning outcomes and assessment validity that challenge the very function of academic institutions. These consequences extend societally if AI graduates lack expected knowledge and skills – undermining public trust in academic merit crucial to careers.
Section 4
Answer and Ethical Support
From an act utilitarian view focused on specific acts versus laws, taking active steps as developers to avert foreseeable misuse leading to dishonest advantages maximizes utility for most students not cheating (Shafer-Landau, 2023). Simply put, if developers can foresee how their AI systems may enable cheating, then according to act utilitarian ethics, developers should take reasonable steps within their power to prevent such misuse and the extensive harm likely to result.
Utilitarianism seeks the greatest happiness for the greatest number via promoting actions that produce more utility and well-being than harm and suffering (Shafer-Landau, 2023). Enabling widespread cheating predominantly benefits a small minority of students willing to cheat while seriously disadvantaging the majority not cheating. While perfect prevention is unrealistic, even basic friction measures such as monitoring questionable use patterns and requiring user verification substantially raise cheating difficulty. By increasing barriers and risks of misuse, fewer students are likely to pursue cheating even if the possibility remains. In this case, the benefits of deterring large-scale cheating and preserving academic assessment integrity outweigh any inconveniences resulting from responsible use monitoring.
Allowing advanced AI tools meant to aid honest learning and development to be coopted for dishonest academic cheating conflicts directly with the consequentialist utilitarian goal of producing the most overall utility through ethical actions (Shafer-Landau, 2023). A big problem associated with AI that makes cheating in education easier is the negative consequences it brings. This undermines the quality of learning and validity of assessment, thereby hitting at the heart of what academic institutions stand for in society. When pupils get more academic knowledge without education courtesy of AI, this becomes a serious threat to numerous careers where capability is mandatory because people lose faith in meritocracy and the credibility of certificates.
The potential societal harms are far beyond mere isolated cheating actions that destroy the value of education. This would develop a negative image in the public about educational institutions as they will perceive them as promoters of cheating instead of developing students’ skills. Instead, it does nothing else but encourage a “the end justifies means” attitude where a student cheats with the intention of passing the course rather than genuinely improving his or her knowledge and skills. Cheating also prevents self-development at a personal level and globally; through AI systems that can scale dishonest shortcuts over hard work and merit, there is a danger of affecting society’s schools, economy, innovation, or security, for instance, through normalizing laziness for academic success pushed by technology.
Therefore, AI developers are morally obliged to consider secondary implications in the pursuit of the primary objectives of their AI systems. This will be achieved by implementing the means of detecting cheating into the core functionality that enhances societal welfare and sustainable business at large. Although they cannot fully guarantee the prevention of cheating, engineered frictions and increases in risks can really diminish the level of abuse that can be done. Provided there are some controls on misuse that developers offer, any remaining incidents of cheating are less morally culpable than total disregard of obvious cheating opportunities through advanced algorithms. Similarly, there is a need for increased efforts in good faith to meet reasonable expectations basis even as the system gains more users. In addition, this evidences responsiveness to unintended ill effects when rectifying unavoidable problems in the future. Therefore, developers should thoughtfully design both strategic AI functional capacities and oversight mechanisms in light of foreseeable misuses and potential scales.
Section 5
Implications
Meaningful ethical implications can be drawn from this conclusion. Consequently, enabling academic cheating by virtue of developed technology in character evaluation as opposed to acting reflects poorly on developers’ integrity (Shafer-Landau, 2023). Nonetheless, there are some implications when such restrictions become too stringent. The absence of the prevention of some constructive applications might, however, lead to a decrease in the stock of knowledge –due to violations of utility principles. However, AI model transparency will focus on deterring likely misuse patterns in writing style or output so that it will minimize legitimate functionality limitations. Ongoing review is crucial, but not overreach and avoiding a gap between prevention and the spirit of the thing. Nevertheless, there are no fixed rules covering changes in technologies themselves. Ultimately, it is going to be up to developers’ continuous moral deliberations whether AI benefits or damages academia. Their duties are growing with more advanced generative models.
Conclusion
In an age of exponential progress in generative AI with unmatched potential for knowledge generation, there is also a corresponding power to destroy academic excellence. Therefore, learning institutions experiencing this revolution must ensure that their measures of integrity are updated, too (Farrelly & Baker, 2023). In this paper, I have employed ethical frameworks to argue that developers do have an ethical responsibility to include sensible provisions limiting foreseeable cheating abuse and avoiding undue restrictions on legitimate operations. However, more is needed in terms of static regulations because achieving a balance between enabling and discouraging harm must be regularly reviewed depending on the context and capacity changes. If designed and deployed conscientiously with student welfare and preserving public academic trust as guiding priorities, advanced generative models can uphold their vast potential to progress understanding rather than erode the foundations underpinning education. Whether this ideal is achieved depends substantially on the ethical judgments enacted in practice by AI creators. Their obligations are only increasing, and how they choose to fulfill them will shape the emerging landscape of AI in academics.
References
Association for Computing Machinery (ACM). ACM Code of Ethics and Professional Conduct. https://www.acm.org/code-of-ethics.
Bahroun, Zied, et al. “Transforming education: A comprehensive review of generative artificial intelligence in educational settings through bibliometric and content analysis.” Sustainability 15.17 (2023): 12983. https://doi.org/10.3390/su151712983
Farrelly, Tom, and Nick Baker. “Generative artificial intelligence: Implications and considerations for higher education practice.” Education Sciences 13.11 (2023): 1109. https://doi.org/10.3390/educsci13111109
Laakso, Atte. “Ethical challenges of large language models-a systematic literature review.” (2023). https://helda.helsinki.fi/server/api/core/bitstreams/e507d025-8c84-4789-a043-f185fa51eb0a/content
Shafer-Landau, Russ, ed. Oxford Studies in Metaethics Volume 18. Oxford University Press, 2023.https://books.google.co.ke/books?id=7mfKEAAAQBAJ&lpg=PP1&ots=YdfYP9x5CJ&dq=ShaferLandau%2C%20Russ%20(2023).%20The%20Fundamentals%20of%20Ethics%2C%20Sixth%20dition.%20Oxford%3A%20Oxford%20University%2 0Press. &lr&pg=PP1#v=onepage&q&f=false
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