Ethical Analysis and Harm Mitigation in Generative AI : What Strategies are Generative AI Tool Providers Adopting as a Response to the Ethical Complexities in their Models?

Sammanfattning

Generative AI is artificial intelligence that produces generative content with examples such as images, videos or audio. Most known generative AI models read the instructions given by a prompt and generate the output content based on it. However, the rapid development of generative AI carries the problem of ethical risk emergence. Examples are violent content, misinformation or the copyright of output content. Legislations are too slow to combat areas of ethical concern. This raises the question what efforts against ethical harms are generative AI companies pursuing and what implications do these strategies have. This research project is going to answer this question. Qualitative data analysis is conducted on three companies for each of three generative AI model types (text-to-image, video and audio generation types) on five areas of ethical concern (data transparency, training data, copyright, harm mitigation efforts and artist opt-out). The results show that only one generative AI company is combatting all five areas of ethical concern sufficiently and companies can do more. All companies have restricted very similar topics in their terms of service. Other ethical issues showed heterogeneous approaches between the companies. No differences or patterns in terms of approaches between companies, company values, generative AI types or other factors have been found. A notable observation is, that the less financial resources a company has, the fewer efforts against ethical complexities there are. Some companies are more transparent about their approaches and/or combat ethical issues tougher and some other companies are less transparent and/or less tough which may be attributed to various motives such as implementation costs, PR or other reasons. From the project, a conclusion is that there are no or no sufficient rules from legislations regarding these issues which means that to a high degree is up to the companies to combat ethical issues. Due to implementation costs, companies prefer fewer approaches against ethical issues in generative AI.