Ethical AI: Why It’s a Must-Have Skill for Future Data Leaders

The field of science and technology is experiencing a huge transformation, along with the participation of AI (Artificial Intelligence) and ML (Machine Learning). Besides being a mere technical tool, AI has a significant role in the everyday operations of numerous companies. When these AI strategies are implemented with ethical policies, they bring enormous benefits in all aspects of a business. Continue reading to understand the importance of Ethical AI in leadership roles and the key ethical skills you need to develop.

What is Ethical AI?

When we apply ethics in our day-to-day lives, we create a safe and responsible life in light of the impacts of our actions on others. In the same way, following ethics when developing AI technologies for the melioration of society is termed as ethical AI or AI ethics. It is a holistic approach to consider fairness, responsibility, adaptability, privacy, and transparency. 

In our daily lives, we come across various AI systems unwittingly. For instance, retrieving cash from an ATM involves an AI program that validates the transaction, and face recognition in smartphones is also AI. 

Imagine an AI system in a healthcare facility designed to identify patients eligible for specific healthcare benefits; however, a misappropriated algorithm causes the system to target patients of a certain race to exploit healthcare benefits. This highlights the need for ethical governance over AI development to prevent bias or inequality and to encourage innovation.

Benefits of Ethical AI

A recent survey conducted by the Pew Research Center in 2020 revealed that 53% of people worldwide believe the evolution of AI is beneficial for society. However, most people also think that AI implemented in organizations in the coming years might be unethical.

Studies show that there are three main benefits of Ethical AI. Avoiding partiality towards a specific category, protecting the privacy of customers and information, and reducing ecological risks. Some of the other benefits are as follows,

• Transparency:

Being transparent about what, how, and why customers’ data is being used with AI makes the customers trust the organization. When a company is transparent, it helps with identifying and reducing bias in the system. Additionally, it creates a positive perception and an increase in ROI. 

• Consistency:

The AI systems of an organization, when they ensure consistency, reliability, and accuracy, favor customers with safe and reliable information. It reduces errors and encourages customers to return.

• Strong Data Privacy:

Organizations following AI ethics ensure that they meet the data privacy laws. It ensures companies are unbiased towards their employees and lowers legal issues related to privacy.

• Promote Decision Making:

AI designed ethically, collects dependable data from available resources, and helps decision-makers resolve critical situations and make effective decisions favorably. Applying vigorous risk assessment reduces errors and retrieves reliable data.

• Loyalty:

When companies apply AI with ethical principles, it promotes brand reputation and creates strong and loyal customers.

• Public Good:

Rather than just being a technological advancement, AI implemented ethically will serve society and benefit its people. A lot of big organizations like Microsoft use Ethical AI for the welfare of society rather than making money

Understanding Ethical AI, Responsible AI, and Neural Networks 

It is quite possible to get confused between responsible AI and ethical AI, as they are relatively the same in some ways. In some companies, they use both responsible AI and ethical AI interchangeably to benefit from their enormous benefits and to minimize operational errors. The main differences between responsible and ethical AI lie in their scope, goals, implementation, and user experience. 

Ethical AI in an organization is applying moral principles and policies that AI has to adhere to human values, such as privacy, transparency, and equality. Responsible AI, on the other hand, pertains to how AI is implemented within an organization for the good of society. Ethical AI focuses on ethical resolutions and is accessible to all users without any bias. On the other hand, responsible AI focuses on straightforward interaction with individuals.

For an easy understanding of these AI principles, imagine a car with safety features. The car comes with such safety features to serve society without harm is Ethical AI, and how the car is driven safely is Responsible AI.

Like Responsible AI and ethical AI, neural networks are also a part of the trending AI. Neural networks are the multiple-layered computational simulations of the human brain. Neural network studies a lot of patterns from online data and makes decisions.

Since this system does a pattern study on available data, it is possible to make biased decisions and errors while making decisions. Unlike ethical AI, which protects the privacy of the user, neural networks can exploit sensitive information and are not transparent to the users.

In Tesla, neural networks are applied in self-driving cars to analyze sensors and avoid collisions and accidents. Some of the other real-time applications of AI neural networks are friend suggestions recommended on social media platforms, suggestions of products on e-commerce sites with reference to history, and personal assistants that understand voice instructions and act appropriately.

While the rooting of AI in every business is unwavering, getting to know the complications and consequences of AI in the real world is obligatory for data leaders. Exploring the fundamentals of AI is essential to prepare yourself for future leadership.

Why is Ethical AI Increasingly Important In Leadership?

According to a recent survey conducted in 2024 among 71 companies, 38 companies responded that they employ ethical AI in their system. As the evolution of AI is prominent in every field, the need for decision-makers who are proficient in handling AI ethics is growing.

The AI systems in numerous organizations were earlier handled by IT team members, which did not work as it was intended. Data leaders who are proficient in data analysis and other technical skills can guide a team to the future of ethically responsible AI. Not only that, they make sure that AI technologies within their organizations are morally sound. Only with strategic thinking and clear decision-making skills, setting comprehensible policies and governance is painless. Organizations that implement beneficial AI programs ethically can attract plenty of customers and earn their trust.

Vital Ethical AI Skills 

The transformation across the working environment after the arrival of AI is apparent. This has prompted professionals and students to educate themselves on essential AI skills. Here are some basic AI skills that are needed for students who are interested in the AI field.

Solid Understanding Of Algorithms

Learning Machine Learning and Deep Learning are the most important parts of AI. Having a good grasp of these algorithms and techniques is essential to master AI. 

High-Level Languages:

Programming languages like Java, Python, and R are used to develop AI programs. Skilling up with these languages helps when building intelligent systems, and data visualization.

Data Analysis: 

Analyzing a large set of data, recognizing patterns, and measuring data accuracy facilitate shaping an AI system that is unbiased and dependable.

Ethical Frameworks:

Understanding various frameworks, such as the NIST AI Risk Management Framework and other responsible toolkits published by several companies, is valuable for creating morally good AI systems.

Understanding Regulatory Policies:

As AI systems have spread over every industry, many organizations and government bodies have come up with regulatory policies. Knowledge in regulatory frameworks, such as GDPR, EU AI Act, and OECD, will complement when creating AI systems that are ethically right.

Communication: 

Communication skills are essential to interact effectively with internal teams, data scientists, and engineers. Collaborating with both internal and external teams and explaining complex scenarios clearly requires professionals with excellent communication and adaptability skills.

Real-Life Examples of Ethical AI: 

AI implemented with ethical policies facilitates human life with privacy, reliability, trustworthiness, and equality.  AI ethics are applied in the healthcare industry to provide equality in the diagnosis algorithm. The diagnosis algorithms used in many healthcare facilities are implemented with ethical AI to provide fair treatment without any partiality. 

Some other real-life examples of ethical AI are as follows: In the banking system, it helps provide unbiased credit scores for its customers. In HR departments, it helps with fair recruiting. Smart cities help to protect privacy and assure the public that no harm will be caused by surveillance. 

Conclusion: 

Developing AI with ethical values makes data leaders reliable and trustworthy. Organizations that adopt ethical AI for the privacy and confidence of their customers are adaptable and strong. To skill up your AI ethics, start by engaging in AI communities and ethical discussions in data teams, and take relevant courses from an accredited institution. If you are on a chase to learn deep learning, machine learning, or AI fundamentals, do a Master’s in data science and artificial intelligence (AI), from Westford. Westford provides affordable postgraduate programs that equip graduates with the relevant skills required for data-driven leadership. Contact us today for a bright future!