1. Privacy and Data Protection

Privacy is the most critical ethical issue of commercial AI applications. Normally, these systems require a vast amount of personal data to function properly. The ethical dilemma lies with striking a satisfactory balance between utilities and rights to privacy.

Everyone has the right to respect for his or her private and family life, their home, and their correspondence, according to international human rights law, and in particular according to Article 12 of the Universal Declaration of Human Rights. In light of this, commercial uses of artificial intelligence must deploy measures for data protection.

The same can be achieved through the use of strong encryption methods, anonymization techniques, and extraction of content that is explicit from users prior to the collection and processing of data. Businesses will also need to be transparent about what they do with the data and offer control to the users.

2. Prejudice and Discrimination

AI systems could be a source of existing biases and even worsen the situation if they are trained with them. This has raised significant ethical concern, especially in the business domain, such as hiring and loan approvals or other crucial areas of decisions.

Besides, the firm development of ethical AI should be matched with precise measures of detecting and curtailing bias. This boils down to training on heterogeneous and representative data and close observation of AI outcomes by including ethicists at the development stage.

Regulations like the General Data Protection Regulation (GDPR) and the AI Act proposed by the European Union underline the necessity of non-discriminatory AI systems and provide a legal precedent for the field of ethical AI.

3. Transparency and Accountability

Transparency in AI systems should guarantee that the decision made by AI is understandable to the user and stakeholders. The transparency of AI systems forms the bedrock of trust and accountability.

Further, ethical practices in AI warrant elements of opaqueness in how AI systems are developed and how they operate. This should be made possible through techniques such as explainable AI. There should also be mechanisms to address accountability with regard to the harms caused by AI systems. This can take the shape of legislated lines of responsibility, legal remedies which may accrue to a subject adversely affected by an AI decision, among others.