Ethical Considerations in AI and Machine Learning: A Framework for Responsible Development

Abstract

In this paper, we argue that the ethical considerations for the decisions and actions made by machine learning algorithms should align with ethical considerations for human behavior. It is desirable that these considerations are consistently and explicitly followed in machine learning designs. When they are not followed completely, clear documentation should be maintained to show that they were considered during the development of a particular machine learning model.

In recent years, the academic community has made significant developments in methods and tools to address some of these challenges, particularly in machine learning fairness, accountability, and transparency. However, when machine learning models are deployed, the impact of these models can be profound and affect a large number of people. Therefore, we argue that an ethical framework is needed to provide guidelines not only for the development and deployment of machine learning algorithms but also for their entire lifecycle.

The interest in using AI to augment human decision-making processes, optimize services, and solve complex problems, or even replace human work, has led to the development of increasingly sophisticated machine learning techniques. As these techniques, such as classifiers and recommender systems, are used in important real-life applications like criminal justice, recruitment, news feeds, and credit lending, it is crucial for the decisions made by machine learning algorithms to be ethical, fair, transparent, robust, and accountable.

Artificial intelligence (AI) and machine learning have the potential to greatly improve the world and are already impacting many aspects of our society. However, there is rising concern that the benefits of these technologies may be overshadowed by unintended harmful impacts due to their complexity and speed of development.