How does an inclusive AI engineering approach have an upper hand?
While inclusive AI engineering allows different verticals and people to achieve a common goal, an exclusive approach lets only specialized teamwork.
Businesses today have been focusing on developing best practices. Though we cannot say everything in black and white and judge the right or wrong way of doing things, we can surely explore what works best for an industry. Considering the future of the data industry with various data teams, data roles, and artificial intelligence, one of the most important questions is to determine whether AI should be selective or diverse.
This is an important question to address and decide its role in the future. A diverse or inclusive AI encompasses people from different verticals to work together to achieve a common objective. An exclusive or selective AI engineering field would be where a special team will work towards efficiently getting the job done.
So, in which direction should artificial intelligence engineering move? Which one will align better with the organizational structure and brighten a professional’s career path? Let us discuss the same.
Embracing/ Inclusive AI Engineering
Let us discuss what it means to have an inclusive AI structure. Inclusive Artificial Intelligence Engineering allows the collaboration of people with diverse backgrounds, roles, profiles, strengths, and departments to work together and achieve a common company goal. However, this type of AI is not restricted to just having teamwork for a particular project; it also means collaboration of different AI processes throughout a company. This will lead to a massive transformation in the working environment.
As mentioned earlier, an inclusive AI engineer caters to a much larger realm of things, more than we know! Inclusive also means handling key issues like employee fairness and biases, job responsibility, interpretability, etc. Inclusive AI promotes the idea that more people must be included in the AI processes. This approach believes this will generate better results internally and externally due to the addition of diverse skills, ideas, and opinions. So, under the inclusive AI model, AI data will not be used by a specific team or role. AI will equip and allow all the company employees to make routine decisions, change processes, and use data.
How to promote inclusion?
It is essential to discuss and implement the ethical application of AI. As we know, AI has a major impact today on everyday decisions and industries like finance, healthcare, etc., so developing a reliable method of AI engineering has become necessary. Most businesses want to develop a responsible and inclusive AI to match customers’ requirements and ensure brand positivity.
- To ensure that AI has a broad appeal, here is how to develop an AI process:
- Implement an all-inclusive approach to collect, mine, and validate data to build machine learning models.
- Use “data diversity” and cover the maximum possible use cases, the maximum number of users and their interaction with the system, etc.
- One of the approaches is data governance, where data management norms must ensure the use of the highest quality and accurate data. Only representative data can build an inclusive product.
- To ensure all the models are utilized ethically, implementing model governance is critical.
- Perform testing on diverse users to determine weaknesses, new scenarios, etc.
Selective/Exclusive AI Engineering
The non-inclusive or specific or exclusive AI works just the opposite of what is mentioned above. This approach is an alternative to the above inclusive AI scenario. This approach also has negative and positive features. For example, the exclusive use of AI is advocated for being a premier and completely dedicated source. Similarly, it has a negative connotation as it does not include diversity.
Many experts believe that the future of exclusive AI engineering is not inspiring. It will keep the AI in the hands of just a few data engineers. This will hamper scalability. However, the benefits that it would be more localized, permitting only brilliant AI systems to work and make progress, the objective not getting compromised due to the less experience in the space, etc., cannot be ignored. This may also take Enterprise AI much ahead of the curve. But exclusive AI systems might have a questionable ability to get tailor made suiting the business needs. It may also result in biases and ambiguity.
Wrapping up
An exclusive AI may work well for companies who want to build, design, and deliver a new product quickly and establish a technological advantage. However, an organization needing scalable AI must become more inclusive. Businesses must carefully think and choose between inclusive and exclusive AI engineering based on their business goals, products, and services. The company leaders need to remain careful and fulfil this responsibility impeccably because determining the right approach to AI engineering will shape their company’s future and professional artificial intelligence career.
