The development of AI has signified a brand new age of tech. The advancement of these systems correlates with the increasing efficiency of other industries, including business, healthcare, and education, improving the lives of people around the world. While AI has created a whole new realm of possibilities within the tech world, it has also raised some red flags due to its unknown ethical implications. The exponential growth of these systems, such as Google’s Deepmind, are being closely monitored and worked on to ensure that they are being built with fairness, interpret-ability, privacy, and security for optimal user experience.

So, should we be scared of AI? It’s the first technological development created by humankind that functions in ways we cannot always understand. Older AI systems that were designed for specific purposes such as smartphone assistance or navigation will always perform specific tasks, however, newer, self-learning AI utilize digital neural networks and reinforced learning techniques, allowing them to solve complex problems, correct errors, and improve efficiency. This new form of self-learning could allow us to make breakthrough scientific discoveries at an alarming rate; however, we cannot always foresee what AI will learn, making this new wave of artificial intelligence unpredictable. This raises the question: What can we do to develop responsible AI?

The Development Process


Designing systems with humans in mind.


When it comes to the development of responsible AI, it is critical that users are able to interact with a system during its design stage. The individual experience a user has provides important feedback that can be incorporated later in the development process. As user experiences continue to inform research and development, the data provided by system interaction can be used to accurately asses the impact of a system’s decisions, predictions, and recommendations. And while general knowledge AI can technically serve an unlimited amount of uses, certain measures should be taken during the design process to improve a system’s clarity & control despite its specific function. These include augmenting system answers, live testing, and engaging with a diverse set of users within varied use-case scenarios.

Streamlining and Optimizing AI training/monitoring.


Multiple forms of training metrics allow developers to understand and pinpoint the source of potential system errors and experiences. By utilizing a variety of training metrics and monitoring tools – such as feedback from user surveys – system developers are provided with the necessary insight and data needed to optimize AI performance and observe long-term product health. However, these different metrics should all align with a system’s context and goals as this will ensure that it will be able to perform its given function without any uncertainty.

Examining the data.


When it comes to developing responsible AI, specific data input is crucial. ML (Machine Learning) models perform based on the data they are trained on, so it is vital that developers examine the raw data they’re using carefully. When examining the data that is going to be used, there are several steps that can be taken to ensure that is error-free.

  • Look for mistakes. Even simple mistakes such as missing values or incorrect labels can interfere with a system’s self-learning process, affecting overall system performance.
  • Does the data represent the target demographic? This will not only aid in a system’s ability to communicate with and understand its users but also allow it to perceive events and make predictions about future interactions.
  • Simplify the model. When it comes to AI, less is more. Streamlining a model to only perform its specific function will optimize its performance and precision.
  • Remove data biases. AI models being fed historical data are vulnerable to pre-existing biases. This can interfere with a system’s ability to perform in a way that is fair and consistent across all potential interactions.

Understanding system limitations.

Any system designed with a specific function in mind will have its limitations. For example, a system designed to predict possible basketball game outcomes based on player and team statistics will not be able to determine what players are going to be drafted in an upcoming season. The clarification and communication of a system’s functions and limitations are crucial to its development. With this specific information, users can provide improved feedback needed for system updates and corrections.

A.I. In The World


Eventually, after development, data corrections, and rigorous testing, new AI systems will be released into the world. Even then they will still be subject to the scrutiny and monitoring of their developers. While this may not alleviate the concerns and premonitions of critics, the current advisable process that has been established for the development of artificial intelligence eliminates any observable system errors. With these steps in place, the goal of AI in everyday life can remain unobstructed: to serve, to educate, and to discover.