Ethical AI, its Challenges and Solutions

Introduction to AI and it’s 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 in some of the AI systems, it 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 risk. Therefore it is necessary to find the 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?

AI system has the following ethical concerns:

  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 community.
  2. Liability: Whether it is a human or 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 that 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?

It is necessary to implement data and AI Ethics. AI must be developed and deployed ethically. The following step will be followed to build customized, scalable, operationalized, and sustainable AI Ethics and allow the customers to adopt the AI system that they are looking for:

Ethics Council

There should be a committee such as a governance board that can take care of fairness, privacy, cyber and other data related to risk and issues. It should be with ethics adjacent to cyber, risk and compliance, privacy, and analytics. External subject matter experts, including ethicists, should also be included in the committee. They can

  1. Monitor the task of the employees 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

Creating data and an AI an ethical risk framework is an excellent approach to reducing ethical issues. It consists of a governance structure that needs to be maintained. It identifies the ethical standards that need to be adhered to or followed. The Framework must suggest how their systems express and incorporate these core Ethical AI principles. It is a quality assurance program to measure its effectiveness in designing and developing ethical AI systems.

Optimize guidance and tools

Undoubtedly, the Ethical AI framework provides high-level guidance, but it is still required that the guidance at the product level is granular. Some AI systems require an explanation of how it reaches a decision, especially when the AI system’s decision has a strong enough effect of changing someone’s life. But transparency of the model decreases as the accuracy of their prediction increases. So in such a situation, Product managers need to know how to make that tradeoff. Customized tools should be developed to help product managers to make those decisions. Tools can evaluate the importance of explainability or accuracy for a particular system and suggest to the product manager what to be implemented in that specific system.


  • Organisation’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?

Ethical AI should follow principles such as fairness, reliability, safety, privacy, security, and inclusiveness. It should provide transparency and accountability.

  1. Social Well Being: AI systems should be beneficial for humans, society, and the environment.
  2. Fairness: AI systems should be inclusive, 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 are making 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. It serves humans by respecting human values. The system is not performing any unfair and unjustified actions.

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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.

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