Part of:

The Ethical AI Dilemma: Striking the Balance Between Progress and Responsibility

KEY TAKEAWAYS

AI has made remarkable progress in industries, but ethical concerns need to be addressed for responsible use. Finding a balance is crucial for using AI ethically and effectively. By addressing all of the modern AI dilemmas, we can advance AI responsibly and ensure ethical use.

Artificial Intelligence (AI) is revolutionizing industries such as healthcare, automotive, finance, retail, and manufacturing, bringing improvements and boosting productivity. However, like any technology, it has its dark side.

Advertisements

AI can be used unethically, spreading misinformation, launching cyber-attacks, and even developing autonomous weapons. Moreover, when it is used without proper care, it can lead to problems like biased predictions, discrimination, and privacy violations.

As such, it’s crucial to find a balance between advancing AI and ensuring responsible use.

Advertisements

What Is Ethical AI?

Ethical AI refers to AI that follows clear ethical guidelines. These guidelines are based on important values like individual rights, privacy, fairness, and avoiding manipulation. When organizations use ethical AI, they have well-defined policies and review processes to make sure they are following these guidelines.

Ethical AI goes beyond just what is allowed by law. While laws set the minimum acceptable standards for AI use, it sets even higher standards to respect fundamental human values.

It is legal, but is it ethical?
AI algorithms designed to maximize user engagement and keep them involved can be legal. However, the algorithm may lead the users to addictive behavior, which could negatively affect their mental health. In this case, the algorithm is unethical as it prioritizes platform growth and profitability over user welfare.

In the 1940s, a famous writer named Isaac Asimov developed three principles known as the “Three Laws of Robotics” for the ethical use of AI. This could be considered an initial attempt to develop the principles:

Advertisements
  • The first rule emphasizes that robots must never cause harm to humans or allow harm to come to them by no action;
  • The second rule directs robots to obey and follow human commands unless those commands violate the first law;
  • The third rule states that robots should prioritize their own well-being as long as it does not conflict with the first two rules.

In 2017, a conference was held at Asilomar Conference Grounds in California to discuss the negative impact of AI on society and find ways to address the challenges. As a result, experts have devised a code book containing 23 principles, known as Asilomar AI Principles, providing guidelines on the ethical use of AI.

You can learn more about the 23 principles on the official website.

Dilemmas of Ethical AI

Ensuring ethical AI, however, involves facing and addressing numerous challenges that arise along the way.

In this section, we highlight some of the key dilemmas and discuss the progress being made toward ethical AI.

Performance vs. Interpretability

The AI is facing a tradeoff between performance and interpretability. Performance means how well the AI system performs tasks, and interpretability refers to understanding how an AI system makes decisions, like peeking inside its “brain.”

Now the dilemma is that the most powerful AI models are often complex and hard to understand. They work like magic, but we cannot grasp the “trick.” On the other hand, simpler AI models are easier to understand but may not be as accurate. It’s like having a clear view but with less accuracy.

As we increase the size and complexity of AI models to enhance performance, AI is becoming more opaque or harder to understand. The lack of interpretability makes it challenging to uphold ethical practices, as it results in a loss of trust in the findings of the model. Finding the right balance between AI performance and interpretability means improving AI systems without losing our ability to understand how they work.

Explainable AI is an emerging approach that aims to make AI more understandable, so we can have accurate results while still knowing how those results are generated.

In this regard, postdoc explainable AI techniques are being developed to explain the trained models without compromising their accuracy.

Privacy vs. Data Utilization

The dilemma between privacy and data utilization is like finding a balance between keeping personal information private and making use of data to improve AI systems.

On one hand, protecting privacy means safeguarding sensitive data and ensuring it is not misused or accessed without permission. On the other hand, data utilization involves using the information to train AI models and make accurate predictions or recommendations. Striking a balance means finding ways to utilize data while respecting privacy rights, obtaining consent, and implementing measures to protect personal information.

Ethical AI demands harnessing the benefits of data without compromising individual privacy. Researchers are working on different ways to maintain a balance between privacy and data use. In this regard, some of the key developments include the following AI techniques:

  • Federated learning
  • Differential privacy
  • Anonymization and aggregation
  • Privacy-preserving AI techniques

Innovation vs. Ethical Considerations

Finding a balance between innovation and ethical considerations is crucial when developing new ideas and technologies responsibly. Innovation involves exploring and testing novel concepts to achieve groundbreaking inventions, while ethical considerations require dealing with the consequences of these advancements on individuals, communities, and the environment.

This is a manifold challenge that has various aspects and dimensions. Some of the key aspects are mentioned below.

Innovation vs. Environmental Responsibility Many studies have reported the adversarial impact of training AI models on the environment, equating it to the emissions of a car over its lifespan. This emphasizes the need to strike a balance between innovation and the environmental consequences of AI development.

Sustainable AI has emerged as a field focused on reducing the environmental footprint of AI innovations and deployments. This involves prioritizing high-quality data over quantity, creating smaller yet efficient AI models, establishing energy-efficient AI infrastructure, implementing sustainable policies, and promoting awareness through education.

Innovation vs. Job Displacement On one side, AI can bring exciting advancements and boost productivity. On the other side, it can also lead to certain jobs being taken over by machines, causing people to lose employment opportunities. While AI can create new jobs, it’s important to find a balance and address the potential impact on workers.

Solutions include offering training programs to learn new skills, rethinking job roles in collaboration with AI, and ensuring support for those affected by automation.

Innovation vs. Misinformation The dilemma between innovation and misinformation in ethical AI is a significant concern. Two examples that highlight this challenge are deep fakes and chatbots. Deep fakes are realistic but manipulated videos that can spread false information, while chatbots powered by AI can also be used to spread misleading or harmful content.

Striking a balance between promoting innovations and preventing the spread of misinformation requires improved detection methods, educating users, and implementing regulations. It is essential to ensure responsible AI use while minimizing potential harm.

The Bottom Line

AI has brought remarkable progress to industries, but it also raises ethical concerns. It can be used unethically, spreading misinformation and violating privacy. Finding a balance is crucial. Key dilemmas include:

Performance vs. Interpretability: AI models can be complex, making it hard to understand how they work. Explainable AI aims to maintain accuracy while making AI more understandable.

Privacy vs. Data Utilization: Protecting privacy while using data to improve AI is important. Techniques like federated learning and differential privacy help strike a balance.

Innovation vs. Ethical Considerations: Balancing innovation and ethics is vital. Sustainable AI addresses the environmental impact, and support is needed for those affected by job displacement. Further, detection tools are required to address the misinformation.

By addressing these dilemmas, we can advance AI while ensuring ethical and responsible use.

Advertisements

Related Terms

Advertisements
Dr. Tehseen Zia

Dr. Tehseen Zia has Doctorate and more than 10 years of post-Doctorate research experience in Artificial Intelligence (AI). He is Tenured Associate Professor and leads AI research at Comsats University Islamabad, and co-principle investigator in National Center of Artificial Intelligence Pakistan. In the past, he has worked as research consultant on European Union funded AI project Dream4cars.