4 Considerations: Ethics in Artificial Intelligence and Machine Learning

Ethics in Artificial Intelligence and Machine Learning is growing in importance.

The growing use of Artificial Intelligence and Machine Learning in products and services in the 21st century has also increased the need for responsible use of these technologies. The guiding principles and techniques are important to combat the issues regarding biases in AI/ML applications.

For instance, the healthcare sector is specifically prone to such biases where the health practitioners are one of the earliest users of AI and ML. The reliance of these machines on multiple and heavy historical data that is not corrected with time can cause serious consequences.

Thus, the need for developing ethical principles and techniques is important before completely relying on AI/ML inventions to minimize the bias in society. Google Health, primarily focused on introducing AI to revolutionize breast cancer screening, is investing to ensure and validate the algorithm’s performance across various racial groups.

What Does it Mean? Ethics in Artificial Intelligence and Machine Learning

When discussing ethics in AI and ML, we refer to an ethical approach or framework for designing, implementing, deploying, and evaluating algorithms. It’s also important to note that different applications require different methods or frameworks for ethical design. For example, medical applications require additional considerations than autonomous vehicles or financial services applications.

The term “ethics” refers to how people should behave, not necessarily what outcomes they want. This means developing systems that don’t harm humans or society. It also means ensuring the technology doesn’t suffer unintended consequences after it’s released into the wild.

Principles of Ethics in Artificial Intelligence and Machine Learning

The development of the experiments and algorithms is entirely based on building a framework for design. The key concepts of the framework help introduce a better and more effective design to implement.

AI and ML Ethical Considerations

Ethical considerations include Fairness, Accountability, Transparency, and Explainability (FATE).

1. Fairness: We want to build systems that are fair for everyone interacting with them. This includes avoiding bias toward or against any particular group of people — including race, gender, or class — and preventing negative outcomes like discrimination or harassment because of those biases.

2. Accountability: People using AI and ML should understand that it is important to figure out to which extent they can be in charge and make corrections to the algorithms. Accountability is about taking responsibility in case of problems and who is to take the blame for whatever goes wrong.

3. Transparency: People have a right to know what decisions will be made by machines that affect them personally, so we should build transparency into our systems at every stage, from design through deployment.

4. Explainability: Most scenarios with serious life-and-death situations need a clear understanding of what a machine can do, for instance, in health care, credit lending, law, etc. It is important to check the parameter the machine model considers when coming to conclusions.

For instance, if a medical AI/ML model predicts that a patient has a particular disease, the medical practitioner should know the base parameters of the results. This leads to developing a useful AI/ML technology by detecting any fault/bias and debugging the model in the development phase.

Future of Ethics in Artificial Intelligence and Machine Learning

The impact of AI and machine learning are undeniable. The acceleration of these technologies has been rapid and transformative, creating vast opportunities for businesses and society alike. Three factors will shape the future of AI/ML ethics:

The Need for More Reliable Data

The most obvious way forward is to make use of better data. This could mean using more diverse data sets or simply cleaning up existing datasets to remove biases (e.g., race, gender). It’s also important to note that since AI/ML are still rather new technologies, we don’t yet know what kind of biases might arise from them — so it will be important to monitor their usage closely in the coming years.

The Need to Understand How Humans Think And Act

For AI/ML systems to make good decisions, they must understand why humans make certain choices. For example, if an algorithm is designed to determine the best route between two points on Google Maps by looking at historical traffic patterns, it needs to know what people consider when choosing between routes — otherwise, it won’t be able to come up with an optimal solution; it will just pick one randomly (or worse: based on experience).

The Need to Develop Robust Machine Learning Systems

There is a growing awareness that AI and ML can have unintended consequences. In particular, there is a potential for bias in the training data that can lead to bias in the resulting algorithm. This has led to calls for increased transparency and accountability from companies developing these systems.

Conclusion

In light of the advancements in AI and ML, ethics must be a priority. Considering the early stages of design can greatly reduce the chance that an AI or ML system will cause harm to users. By creating systems with transparency, explainability, and understandable constraints, creators can make it easier for users to predict and understand how the system will behave.