Richard Foster-Fletcher
Founder of MKAI.org
MKAI Insights Network of Diverse Stakeholders (MINDS)
In our algorithm-driven world, companies that can demonstrate their trustworthiness will win.
In contrast to other high-risk industries, artificial intelligence presents special challenges for regulation and governance. Therefore, relying solely on experts, research, and conventional testing is insufficient to create artificial intelligence (AI) that is secure from harm.
True diversity is required for the development of artificial intelligence. People with varied life experiences and differing points of view need to be included in the conversation and their insights, concerns, and observations to be heard and factored into the development.
Multi-Stakeholder Feedback is essential to identify potential harms of an artificial intelligence application. MKAI has the largest and most diverse AI de-risking community in the world.
Learn how to gain vital perspectives about the potential risks, harms, exclusions, biases and prejudices from your AI.
Learn how to gain vital perspectives about the potential risks, harms, exclusions, biases and prejudices from your AI.
Start a conversation with us
Let's talk about reducing your AI risk through accessing diverse perspectives
It is impossible to understand what it is like to experience the world as a person of a different gender, race or age to your own, or that thinks differently. The only way to learn is to ask and listen.
Algorithmic bias and injustice are prominent topics in AI ethics and machine learning conversations and for good reason. From racist chatbots to facial recognition algorithms that fail women, many cases of bias and injustice have infiltrated artificial intelligence models. The risk implicit in the technologies we are supposed to trust has been exposed through high-profile failures in artificial intelligence (AI) and autonomous systems.
MKAI provides a Multi-Stakeholder Feedback service known as MKAI Insights Network of Diverse Stakeholders (MINDS). It's a large and inclusive collective of diverse individuals. We provide foundation education and peer-to-peer mentorship so that, together, the collective can help companies and organisations to discover what is out of sight as they develop and deploy artificial intelligence.
When external, open dialogue becomes routine between organisations and stakeholder communities like this, we will begin to mitigate the problems created by AI.
When external, open dialogue becomes routine between organisations and stakeholder communities like this, we will begin to mitigate the problems created by AI.
Why use MINDS?
- Access Diverse Thinking: We provide access to our 1,000+ AI ethics stakeholders that will engage with you to discover the 'blind spots' in your AI plans.
- Gain Greater Perspective: Many unique individuals will work together to review your AI processes. We help you to spot the mistakes before they materialise into embarrassing or expensive errors.
- Be challenged: MKAI stakeholders will ask the difficult questions that might not get raised otherwise. They will challenge you to think wider and deeper about the impact of your AI.
- Unlock unique lived experiences: We enable you to speak to people that don't think you like you do, and help you and 'your AI systems' to see the world through their eyes.
Who is this for?
Anyone deploying AI is open to risk, but
there is specific pressure to get it right on those companies involved with:
● HR Automation
● Social Media Algorithms
● Recommendation Engines
● Education Technologies
● Insurance Algorithms
● Health and Medicines
● Security and Surveillance
● Government Related Systems
● HR Automation
● Social Media Algorithms
● Recommendation Engines
● Education Technologies
● Insurance Algorithms
● Health and Medicines
● Security and Surveillance
● Government Related Systems
What MINDS offers:
- Data - The data used in AI models is either restricted, under-representative, or biased. Individuals in this collective can contribute information and data to make the models more diverse and useful.
- Discourse - Often, teams working on AI projects have a lack of diversity, for example, gender, age, region, neurodiversity, and impaired capacities. This restricts the scope of their ideas as well as the understanding of those who will use the systems. MKAI members will examine the issues that organisations are attempting to address in the nations they are targeting. We will identify the flaws, errors, and omissions.
- Testing - Testing solutions with or on a diverse group is challenging. With over 95 nations represented, and many other diverse attributes, the MKAI collective will be able to provide comments, feedback and suggestions.
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