Comprehending Ethical AI Challenges and it's Solutions

Introduction to AI and its Ethics

Artificial intelligence (AI) is the digital technology that has a major impact on humanity's development. All tech giants are trying to develop cutting-edge AI technology. It means that the Ethical Problems in AI also need to be discussed. What are the dangers associated with developing Artificial Intelligence? It has raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these.

What are the Challenges of Ethical Artificial Intelligence?

Companies are using AI (Artificial Intelligence) to create scalable solutions. But ethical considerations are becoming critical in AI systems. Because it is noticed that some AI systems can be prone to errors for particular communities. Apple Cards AI algorithm was discriminating against women while granting credit limits. It is giving large credit limits to men as compared to women. Such problems are found in other AI systems also. Failure in data and AI ethics' operationalization is a significant threat because it can expose the companies' reputational, regulatory, and legal risks. Therefore it is necessary to find a solution to develop AI systems without failing in the ethical pitfalls. It is required to identify ethical risks throughout the systems.

Researches and experiences are showing that it’s inevitable that AI will replace entire categories of work, especially in transportation, retail, and customer service. Source: Ethical Concerns of AI

What are the Ethical Concerns around Artificial Intelligence?

1. Bias: AI systems use data and look at data patterns to learn. The result that it generates also favors those patterns. That data can be biased for some specific communities.
2. Liability: Whether it is a human or a machine, they become intelligent after learning. AI systems also need to learn from the data to defect the right patterns. But it can be possible that it cannot cover all possible cases during the learning phase. Then it can be possible that the system came with the wrong decision when it encountered the new case. Then it is a big concern who will be responsible if the AI system makes a mistake.
3. Security: AI systems that can cause damage if used maliciously. Therefore cybersecurity is critical. Because by improving AI, systems are becoming faster and more capable than us by orders of magnitude.
4. Privacy: Most of the data is stored digitally on a single internet. In the digital world, it is hard to control access to data. Therefore the security of data is always at risk while using AI.
5. Manipulation of Behaviour: In AI, in surveillance use of information to manipulate behavior can reduce autonomous rational choice. It directly attacks the autonomy of individuals.
6. Opacity: Lack of accountability, auditing, and engagement reduce opportunities for human perception. Both users and developers are not aware of the system's process to reach the output. This opacity increases the bias in datasets and decision systems.

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How to Operationalize Ethical AI?

Ethics Council

1. Monitor the task of the employees and how they take care of these issues.
2. Take care of legal and regulatory risks.
3. To fit AI ethics strategy to systems.

Ethical AI Framework

Optimize guidance and tools

Awareness

• The organization's culture will help to implement ethics in AI successfully. A culture can be created so that everyone in the organization is aware of the ethical framework and hence allow them to raise the question for ethics in the system at every stage or level of the AI system.

 

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What are the Principles of Ethical AI?

1. Social Well-Being: AI systems should be beneficial for humans, society, and the environment.
2. Fairness: AI systems should be inclusive, and accessible and should not have unfair discrimination against individuals, communities, or groups. It should provide equitable access and treatment to everyone. The primary reason behind the bias is that algorithms are developed and trained only on a certain portion of the population, but there is diversity in the world in actuality. Thus when the same system is implemented globally, it shows bias.
3. Privacy Protection and Security: AI systems should respect privacy rights and data protection. It ensures the security of data. Ethical AI designed system provides proper Data Governance and model management systems. While designing systems, the system's privacy and security are primary concerns.
4. Reliability and Safety: AI systems should reliably work in accordance with their intended purpose.
5. Transparency and Explainability: AI systems should provide complete transparency of their system and explain how it makes decisions.
6. Accountability: AI systems are under the control of the appropriate human. The system provides opportunities for feedback and appeal.
7. Value of Alignment: Humans make decisions by considering universal values. Our main motive is to provide AI that also considers those universal values.
8. Governable: A system that works on intended tasks. It detects and avoids unintended consequences.
9. Human-Centered: Ethical AI system values human diversity, freedom, autonomy, and rights. Artificial Intelligence serves humans by respecting human values. The system is not performing any unfair or unjustified actions.

Conclusion

We make sure that it will provide core values to their use by implementing ethics in our AI systems using:

Intention: This examines the system's intention. Whether the system gives positive intention or not?
Future: Examining the future of the system. How does it affect if everyone adopts it, and how long can it go?
Humanity: It checks whether the system is obeying the law of humanity or not.