Ethical AI: An Effort to Make AI Systems More Responsible

As artificial intelligence (AI) continues to play an increasingly prominent role in our lives, the need for ethical considerations becomes more pressing than ever. Imagine a city’s police department using AI-driven predictive policing led to increased social inequalities rather than enhancing public safety and law enforcement just because their AI system is biased.

To minimize such negative consequences of AI, it is important to address ethical considerations. From data used for training to designing and deploying, ethical AI makes sense at every stage of development. 

What is the Meaning of Ethical AI?

Ethical AI, also known as responsible AI, refers to the development and use of artificial intelligence systems in a manner that is consistent with ethical principles, moral values, and societal norms. It involves ensuring that AI technologies are designed, implemented, and utilized in ways that are fair, transparent, accountable, and beneficial to individuals and society as a whole.

Let us explain to you with another example. Lensa AI, an application that converts images into cartoon pictures raised the debate of AI being unfair to human artists in 2022. It is because Lensa AI takes images from the internet but does not give credit to the artists, thus creating the ground of unfairness.

Read More: The Rise of AI in Cybersecurity: Protecting Businesses from Emerging Cyber Threats

The Foundations of Ethical AI

Understanding What’s Bias and What’s Fair

AI models cannot think of their own to decide what is biased and what is fair. They only generate responses based on the data they are trained on. For example, if a company is biased towards a particular race of people and uses an AI recruiter trained on the same ideology, talented people might lose a good opportunity and the unemployment rate may go up. It will boost discrimination defeating the sole purpose of artificial intelligence.

So, it’s up to us to ensure that whatever information is fed to the AI systems does not discriminate against people based on race, gender, community, or other parameters that discriminate people.

Maintaining Transparency and Accountability

Transparency is a cornerstone of ethical AI development. It ensures that the inner workings and decision-making processes of these systems are understandable and open to scrutiny. Building trust, identifying, and rectifying biases or errors, and holding organizations accountable for their technology's impact are some of the benefits that come with a transparent AI system. Let us understand this with an example:

Imagine a bank employing an AI system to assess loan applications automatically. This AI system uses a complex set of criteria to determine who qualifies for a loan and who doesn't. 

Transparency in this context means the bank clearly explains how the AI makes loan approval decisions. Customers and loan applicants can understand why they were approved or denied, which fosters trust in the bank's lending practices. If the AI's decisions were opaque, customers might feel unfairly treated and lose trust in the bank.

With transparency, the bank can regularly audit the AI system to check for biases. They might discover that the AI is systematically denying loans to people from certain neighborhoods or ethnic backgrounds. This transparency allows them to correct these biases and ensure fair lending practices.

In this example, transparency in the loan approval algorithm not only builds trust but also enables the bank to detect and correct biases, remain accountable for its lending decisions, comply with regulations, and improve the system's fairness. All of these factors contribute to making the AI system more ethical in its operation, benefiting both the bank and its customers.

Keeping Privacy and Data Protection at the Top

Users’ privacy and data protection is one of the biggest concerns of ethical AI development. These systems require massive amounts of user data including their personal information to offer personalization and better results. However, the way this data is collected and handled can pose privacy risks for customers while bringing legal liabilities and reputational damage to the businesses as well. Therefore, AI developers must adhere to standard data protection laws and regulations like GDPRR, and HIPAA to safeguard user information.

Besides this, ethical AI also involves anonymizing or de-identifying data to remove personally identifiable information. With clear policies for data retention, the personal data of users can be stored for a specific duration for the intended purpose, reducing the risk of prolonged privacy exposure.

Conclusion

Irrespective of the area where AI is implemented, from healthcare to customer service, AI-driven systems must be made to respond ethically. Understanding the fundamentals of AI and the risks associated with it is the first step towards ethical AI. Next step is implementing regulatory frameworks, and working within the standard ethical guidelines, and policies. As we navigate the ever-evolving landscape of AI, our collective responsibility is multifold to ensure that technology benefits all of humanity while upholding ethical standards and values.