OpenAI truly revolutionized the AI field in March 2022 by launching its GBT3 AI chat model. Although developments in the field of AI have been underway for a long time, humanity is truly on the verge of being able to replace humans in many fields such as design, automation, software development and even in the areas of decision-making. However, such advanced AI poses ethical issues that need to be addressed.
In this essay, I propose to examine the ethical issues related to the use of AI in software development, proposing solutions to potential risks.
Ethical implications must be considered during software development to ensure fairness, transparency and accountability in the deployment of AI-based systems, even though AI offers many benefits to software development. AI.
The first is data security and privacy. According to research (Boulemtafes, Derhab, & Challal, 2020), privacy concerns are particularly related to sensitive input data, whether during training or inference, and sharing the trained model with others . Typically, models used for AI are trained on huge amounts of data.
According to research (Arnott, 2023), ChatGPT collects both your account-level information as well as your conversation history. This includes records such as your email address, device, IP address, and location, as well as any public or private information you use in your ChatGPT prompts. This can only raise concerns since this data may be confidential.
Therefore, developers of AI systems must obtain consent to process personal data. Generally, when it comes to processing personal data, protection against unauthorized access is necessary, so it is necessary to implement a strong encryption method.
The second is bias and fairness. As mentioned earlier, large amounts of data are used to train AI. But are there guarantees that AI will not inherit biases from its information sources? This can lead to very unpredictable consequences, unfair outcomes, discrimination and the perpetuation of social inequalities.
For example, a person who turns to an AI language model expects to receive pure information that does not convey anyone’s opinion, but receives biased and inverted information. The dataset is biased towards specific demographics and the algorithm may exhibit bias, leading to unfair results. Therefore, according to a study (Li, 2023), collecting a wide variety of data representing different races, genders, ages, cultures and social backgrounds is essential to ensure algorithmic fairness.
The danger lies in the fact that it can control the opinion of all humanity on certain facts. These include diversity in data presentation and regular bias checks to identify and mitigate discriminatory patterns. Therefore, continued commitment and efforts are essential to ensure algorithmic fairness.
According to Li (2023), only through continuous learning, improvement, and adaptation can the AI field achieve true fairness. Data scientists, on the other hand, are responsible for analyzing data, ensuring that the algorithm’s performance is fair across various demographics.
Third, there is accountability and transparency. Nowadays, AI algorithms have a complex structure, which makes it quite difficult to determine the degree of responsibility for the errors they contain. Issues around transparency, debates over job cuts, and global disparities in AI development exacerbate these ethical dilemmas. According to Li (2023), to solve this problem it is necessary to introduce clear standards and, of course, reporting.
This same demand will differentiate the roles of developers, data scientists, decision-makers and end users. Additionally, the process of documenting key processes can enable tracking and reporting of AI results. The concept of transparency refers to the degree to which the decisions and actions of AI systems are understandable, interpretable and understandable not only by the specialists who developed the AI model, but also by all interested parties.
An example of using interpretable machine learning models could be decision trees or even linear models. This will allow us to evaluate the factors influencing AI predictions and decisions. The Association for Computing Machinery has defined seven principles that emphasize the importance of ethical considerations in the design, implementation and use of analytical computing systems.
These principles are provided and explained in more detail in the article by Garfinkel et al (2017). According to Matthews (2020), these principles encompass various facets of ethical considerations in the design, implementation, and use of analytical systems. Access and redress should encourage the adoption of mechanisms to interrogate and redress individuals and groups who are harmed by algorithmically informed decisions.
Accountability implies that institutions are held responsible for the decisions made by the algorithms they use, even if it is not possible to explain in detail how the algorithms produce their results. Explanations of systems and institutions that use algorithmic decision-making are encouraged to produce explanations regarding both the procedures followed by the algorithm and the specific decisions made.
Data provenance refers to a description of how the collected training data should be maintained by the algorithm builders, accompanied by an exploration of potential biases induced by the human or algorithmic data collection process. The audibility of models, algorithms, data and decisions should be recorded so that they can be audited in cases where harm is suspected.
Validation and testing institutions must use rigorous methods to validate their models and document these methods and results. In particular, they should regularly carry out tests to assess and determine whether the model generates discriminatory harm.
In conclusion, developments in the field of AI have been underway for a long time, humanity is truly on the verge of AI being able to replace humans in many fields like design, automation, development of software, and even in the areas of decision-making. .
However, such advanced AI poses ethical issues that need to be addressed. In this essay, I examined the ethical issues surrounding the use of AI in software development, proposing solutions to potential risks.
The references
Arnott, B. (September 13, 2023). Yes, ChatGPT saves your data. Here’s how to keep it safe. Retrieved May 11, 2024, from Forcepoint website: https://www.forcepoint.com/blog/insights/does-chatgpt-save-data#:~:text=ChatGPT%20collects%20both%20your%20account Boulemtafes, A. , Derhab, A. and Challal, Y. (2020). A review of privacy-preserving techniques for deep learning. Neuroinformatics,
384, 2–5. https://doi.org/10.1016/j.neucom.2019.11.041
Garfinkel, S.; Matthew, J.; Shapiro, S.; and Smith, J. 2017. Towards Algorithmic Transparency and Accountability. Communications of the ACM 60(9): 5. doi.org/10.1145/3125780.
Li, N. (2023). Ethical Considerations in Artificial Intelligence: An In-Depth Discussion from a Computer Vision Perspective. SHS Web Conferences,
179. https://doi.org/10.1051/shsconf/202317904024 Matthews, J. (2020). Models and anti-models, principles and pitfalls: accountability and transparency in artificial intelligence. AI Magazine, 41(1), 82-89. https://doi.org/10.1609/aimag.v41i1.5204
