Artificial Intelligence (AI) can potentially revolutionize almost every aspect of our lives. We can use AI to develop self-driving cars, diagnose medical disorders, and better predict natural disasters. But these advances are not without risks. We need to consider the ethics of the systems we create. This becomes more true as these systems become more advanced and more powerful. The concept of "Responsible AI" seeks to address our concerns.

Responsible AI refers to designing, developing, and deploying artificial intelligence systems that prioritize ethical considerations and the well-being of users and society. It involves an ongoing commitment to address potential risks and ensure we use AI technologies in ways that align with societal values.

Principles of Responsible AI

Microsoft emphasizes the following principles of Responsible AI:

• Fairness
• Reliability and Safety
• Privacy and Security
• Inclusiveness
• Transparency
• Accountability

Fairness

The system should strive to treat everyone fairly. Consider your system's impact on people of different ages, genders, and ethnicities. Are we discriminating against some groups? For example, are we more likely to return a higher credit risk simply because someone is a woman? We can address this by removing the bias from our input data. Biased input will create a biased model.

Reliability and Safety

An AI system should be reasonably accurate and should not present undue risks. Consider the possible errors in your output, the likelihood of those errors, and the impact if those errors should occur. Validate your data and your results. Some errors are inconvenient; others can be catastrophic. For example, if a self-driving car turns at the wrong time, it could result in the death of a pedestrian.

Privacy and Security

All Machine Learning Models use data as input, and some of that data may be private. Private data could be corporate information, such as financial reports, or personal information, such as names and addresses. In both cases, ensuring that you properly secure confidential data is essential. As you collect data, communicate how you will use it, and allow users to opt out of providing some information. Recognize what parts of your data are private and keep them secure using encryption, access restrictions, and auditing tools.

Inclusiveness

When building applications on top of AI, consider accessibility.

Transparency

Reveal the data and algorithms on which you base your model. People have a right to know how your system came to the conclusions it did.

Accountability

When designing an AI system, it is tempting to defer decisions to the system, deflecting responsibility away from ourselves. But those who develop an AI system are responsible for the decisions produced by that system.

Consider who is responsible for decisions based on your AI system.

Explainable AI

IBM has its take on this topic, which they refer to as Explainable AI. Explainable AI consists of the following principles:

• Fairness and debiasing: Check for potential biases in your data
• Model drift mitigation: Adjust models when results begin to drift from logical outcomes
• Model risk management: Understand the risks of an incorrect outcome of your system
• Lifecycle automation: Understand the dependencies of your models and automate their generation
• Multicloud-ready: Promote trust.

Conclusion

I confess that I do not understand many aspects of AI. But we have a duty to think responsibly when we deploy any system built on AI models. This article summarizes some of those responsibilities.