What is ethical artificial intelligence?

Ethical artificial intelligence is a system of ethical guidelines and techniques intended to ensure the responsible use and development of artificial intelligence technology.

Ethical challenges of AI

Today there has been an increased use of artificial intelligence. Following are some ethical challenges of AI.

Bias

The risk is that the system might unfairly discriminate against groups and individuals or even opinions. If the data collected by AI does not represent the population accurately, its results might be susceptible to bias.

Explainability

The system and the decisions it makes should be understandable to the users and developers. The AI algorithms and models should be transparent and their decision-making process should be easy to understand.

Robustness

The algorithm might fail under certain circumstances or in the case of an attack. AI systems should be able to perform in a reliable and consistent manner across a wide range of conditions and inputs.

Privacy

There is a major risk that the system does not protect personal data. With the current rise in the use of AI, where most people are sharing their personal information with AI tools, there is a risk of breach of information.

How to enforce ethical AI

The following three approaches are applied, to maintain an ethical AI system and mitigate risk.

• Principles: These include the guidelines and values that control the design, development, and implementation of AI. These are the standards an AI system should adhere to.

• Processes: This involves integrating principles into the design of the AI system to address the risks comprehensively. This includes both the technical as well as non-technical aspects of it.

• Ethical consciousness: This approach refers to the developer or creator of the AI system being motivated by moral awareness and committed to ethical behavior while deploying the system.

AI developers and creators can mitigate the risks of AI by following the above three approaches. Ethical AI requires a mix of expertise in AI policy and standards and computer science, to ensure that best practices are followed while creating an AI system.