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Stakeholder-Centric AI Strategies:Prioritising Needs and Concerns (no one left behind)

Developing stakeholder-centric AI strategies involves creating AI solutions and policies that primarily focus on the needs, expectations, and concerns of all stakeholders affected by AI technology.

This approach ensures that AI deployment drives business objectives and aligns with the broader interests of employees, customers, partners, and society.

Approaches to Developing Strategies: Stakeholder Identification and Engagement

Developing a Stakeholder-Centric AI Strategies approach looks at all people and groups affected by an AI system.

This is different from other disciplines like Project Management and Business Analysis. as they only look at stakeholders involved in a specific project. Programmes Management examines stakeholders impacted by a broader AI program. But the goal is finishing the program, not serving all groups affected.

Methods like interviews, surveys and impact studies are used in many fields. But stakeholder-centric AI has a broader goal - engaging openly with all stakeholders, whether part of the project or external.

Stakeholder AI puts all affected groups first. That’s why a dedicated stakeholder plan is vital for ethical AI. It can’t just rely on typical business methods. This requires plain and balanced language for the public. It also takes an essential commitment to assess AI’s broad impacts.

Bringing People Together for AI Decisions

When companies and organisations build artificial intelligence (AI) systems, they can’t just decide independently.

People should have a chance to give their views. Groups like customers, employees, or anyone affected by AI plans must have a voice. Whatever the way you collect people’s opinions, the simple concept is getting input from different people.


Customising AI Tools with Stakeholders’ Opinions

The stakeholders’ feedback helps to spot where the AI frustrates or confuses them.

Remember that AI developers can’t predict all real-use cases, and bots can grow irrelevant for stakeholders over time. Thus, the algorithm must be retrained on that group’s diverse interests.

In this way, systems remain thoughtful by customising AI based on people’s guidance, and It keeps improving human experiences through their guidance. And continuous user input checks remind us that AI doesn’t just optimise efficiency. It keeps enhancing human experiences.

If more kinds of people give input, AI decisions tend to be fair. Companies may be blind to issues some groups face with new technology. When actively listening to the community, they catch these problems early, leading to responsible and thoughtful AI systems. We can also consider the military ethos “no one left behind.”….

This leads to responsible AI systems.

AI should ultimately serve its end users - not replace them.


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