Ethical Issues in the Artificial Intelligence Field Research Paper Introduction

When the term Artificial Intelligence (AI) is mentioned, people think of the many negative and morally wrong things. Most AI debates revolve around the morally problematic issues and outcomes that must be solved. However, it is essential to note that AI still has various positive features, which include economic benefits due to increased efficiency and productivity. This leads to the ethical and moral benefits of having more wealth and wellbeing, which enable people to have better lives. AI saves humans from tedious, repetitive tasks by having the ability to analyze quantities of data. Most of the negative ethical issues of AI arise from policy perspectives. This study will analyze ethical bias and accountability issues arising from freedom of expression, copyright, and right to privacy and use the ethical frameworks of utilitarianism and deontology to propose a policy for addressing the issues.

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Freedom of Expression

AI has faced distinct challenges whereby the application of automation in the online media environment has negatively impacted freedom of expression. AI is an essential aspect of the media used by social media, search engines, and other internet services as information processing technologies (Llansó et al., 2020). However, various issues that affect its accountability have been raised, such as false positives, whereby some wrong information is classified as objectional and false negatives, where the AI misses some things which should be privileged as offensive. Therefore, when the AI calls for a false positive, it can flag or remove content that is not wrong, leading to a burden and affecting the freedom of expression (Llansó et al., 2020). On the other hand, if the AI calls for false negatives, it may fail to address issues such as harassment or hate speech which may affect an individual’s willingness to participate. This indicates that using automation has bias and accountability issues because it can negatively affect the freedom of speech through false positives or negatives.

Automation has demonstrated potential bias and algorithms for underrepresented groups leading to suppression of freedom of expression. Algorithms perform poorly on marginalized groups based on ethnicities, political leanings, or non-dominant languages caused by the possibility of biased training datasets and a lack of data (Llansó et al., 2020). Just like data is affected by real-world inequalities and biases, the automation systems trained on handling the data may amplify this. This can lead to significant repression of freedom of expression for marginalized individuals and communities.

Copyright

Most AI systems are designed to work by viewing, reading, and listening to the works of humans, which has created the issue of copyright. AI systems must use books, films, photographs, recordings, articles, and videos protected by copyright. According to copyright laws, the owners of these works have the exclusive right to reproduce their work in many copies, and people who may violate these rights are infringers of copyright (Levendowski, 2018). Innovative technologies such as reverse engineering software have received much skepticism. However, there is still no clear rule on whether the materials used to train AIs are a copy of the Copyright Act of 1976 (Levendowski, 2018). This implies there is a much-debated topic on whether the materials made for AI training purposes can be regarded as “copies” and then be subjected to copyright claims.

The other issue is the use of copyrighted works in training the AIs. Judge Pierre Leval asked whether taking an interesting cartoon picture from The New Yorker magazine and photocopying it to stick on a fridge would be considered an infringement (Levendowski, 2018). Although the material belongs to the category of copyright, the de minimis doctrine would not consider that copyright infringement. This shows that in the future, the court is expected to handle loads of cases concerning this matter. The copyright law thus leads to three challenges; access, accountability, and competition. Concerning access, the copyright law may favor specific works more than others, encouraging AI trainers to rely on legally low-risk and easily available training data (Levendowski, 2018). From the competition perspective, the law can choose to restrain the implementation of bias mitigation strategies on the already available AI systems to limit competition. This shows that copyright law can increase access or competition regarding copyright.

Right to Privacy

Another common ethical issue regarding AI is its privacy and data protection. The two words do not mean the same