The case for transparency and consumer information in the age of generative AI is becoming stronger by the day. The market is increasingly concentrated, with a few corporations controlling most data and models. Looking ahead, we expect more competition issues, and we'll keep a careful eye on consumer rights as key markets continue their antitrust efforts.

But one thing is sure: generative AI will transform many aspects of our existence, including our laws, conventions, and values, making transparency crucial. Traditional consumer protection must be rethought to keep up with the latest advancements.

For example, the United Nations Guidelines for Consumer Protection highlight transparency in providing individuals with the information they need to make educated decisions and allowing authorities to formulate and enforce standards.

They have already begun to increase transparency in artificial intelligence. Three crucial stages need to be considered for consumer protection to guarantee that people fully benefit from this new technology: 

Construction 

According to an assessment by the US Federal Trade Commission, consumers have genuine concerns regarding AI development and data integration. Numerous generative AI models necessitate extensive datasets for training and acquiring knowledge. The process of constructing and managing AI models must be examined to determine whether it has been carried out equitably for consumers.

Is the data utilized to train an AI model obtained legally and with the explicit agreement of individuals? Is it ethically acceptable for humans to label and categorize data? Are the environmental resources involved in this process being managed responsibly? Developers should practice transparency about creating a tool that consumers utilize, similar to how product labelling assists individuals in comprehending the components of their food, fabrics, or medicine.

Distribution

After constructing an AI model, it is necessary to implement it in a manner that prioritizes the consumer's needs and preferences. The conflict between open-source and closed-source development has become a crucial issue. An open model refers to an application's source code being accessible to the general public for utilization. In contrast, a closed model denotes that the source code is kept confidential and exclusive.

Hence, those working on AI systems must recognize and report what they know about the potential for damage.

Responsibility

We also need to investigate whether robust procedures are in place to address emerging issues and whether the appropriate levels of accountability and recourse have been established across industry, government, and civil society. It covers customers' right to seek recourse, the disclosure of government access demands, and intellectual property infringement.

For example, if an AI system causes a problem for a human, who is to blame and who should remedy it? Clear lines of accountability must be drawn.

Hence, serious debate should be about how to appeal or oppose AI algorithm decisions in credit lending, healthcare, insurance, and hiring.

Consumer choices

Furthermore, consumer autonomy is jeopardized when AI systems impact decision-making processes. AI can impact customer choices through targeted advertising or algorithmic pricing. Protecting customers' ability to make informed decisions free of excessive influence from AI systems is critical. Regulations should require transparency in advertising practices and prohibit fraudulent or manipulative tactics.

Quality and safety

Additionally, there are concerns about the quality and safety of AI-powered products and services. Biased algorithms can exacerbate societal disparities, whilst poorly constructed AI systems can pose safety issues. Regulatory organizations must set AI development and implementation guidelines to reduce these dangers. Furthermore, processes for monitoring and certifying AI systems can help guarantee that they adhere to established norms and best practices.

Handling complaints

Finally, procedures are needed to handle complaints about AI-powered products and services. If consumers are harmed or discriminated against by AI algorithms, they should have access to legal remedies. It could include establishing ombudsperson services, streamlining dispute resolution processes, or strengthening consumer rights enforcement authorities' ability to address AI-related issues.

Conclusion

Defending consumer rights in the age of AI necessitates a multidimensional approach that includes openness, privacy protection, autonomy, safety, and redress mechanisms. Regulatory frameworks must be developed to keep up with technical advancements, ensuring that AI serves customers' interests while adhering to ethical standards and social values. By maintaining a balance between innovation and responsibility, we can maximize the benefits of AI while minimizing its potential harm to customers.