Responsible Intelligence

 It should be possible to understand how AI systems make decisions; and how to adjust “weight & bias” factors accordingly.

“Machine Learning" is the preferred term. It accurately describes what's going on (learning is essential to the function of the machine), and it doesn't carry the emotional baggage associated with terms like "Intelligence" and "Artificial." Perhaps the digital journey is not just about converging human intelligence and computer learning, but also harmonizing them to create positive synergy in advancing human society.

However, there are lots of risk-related concerns about machine intelligence such as security, accountability, safety, etc. Responsible AI is all about developing and using AI in a way that's safe, trustworthy, and ethical. As AI becomes more powerful and integrated into our lives, it's crucial to ensure it's used for good. Here are some key principles of responsible AI:

Fairness: AI systems shouldn't discriminate against any particular group of people. This means being aware of potential biases in the data used to train AI models and taking steps to mitigate them. Objectivity is the science people set standards to evaluate, leverage quality information to analyze, and dispassionately examine the facts to overcome bias. Some machine intelligence can do information-based reasoning and leverage “weight and bias” factors properly, becoming more fair in making sound judgments.

Reliability, Safety, Privacy, and Security: AI systems should be reliable and perform as expected. In safety-critical applications, like self-driving cars or medical diagnosis, this is especially important. Products or service reliability is one of the critical non-functional 'abilities' such as availability, reliability, scalability, reconfigurability, interoperability, elasticity, security, etc. AI systems should protect user privacy and be secure from hacking or other attacks. To improve system reliability, there needs to be a better appreciation to examine a situation and understand the problem from different angles to fix real issues and improve overall quality.

Transparency, Inclusiveness: The development and deployment of AI should consider the needs of all stakeholders, not just a select few. It should be possible to understand how AI systems make decisions; and how to adjust “weight & bias” factors accordingly. This is important for building trust and ensuring accountability.

There should be clear lines of responsibility for the development, deployment, and use of AI systems. Developing responsible AI is an ongoing process. As AI technology continues to evolve, so too will the need to adapt and improve our approach to developing and using it.

Posted in: Big Data,Business Intelligence