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UNBIASE.ME (updated)
Artificial Intelligence service to fact-check a team’s decisions against their unconscious biases.
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Solution

Unconscious biases (the thoughts and beliefs that are so deep ingrained on our brain, that we don’t realise they exist) affect our decision making every day which, in turn, affects teamwork and the development of solutions (8).

UNBIAS.ME is an AI service that allows Hal (the Jovoto AI Steward System) or any other AI facilitator to fact-check a team’s decisions against their unconscious biases. This allows for a more diverse recruiting as well as committing to a bias-free design process, and, in consequence, to a better product.

With the help of UNBIAS.ME, Hal is prepared to make a personal assessment of each individual (if given the permission) that analyzes all these variables by having a one to one test to identify personal biases based on variables such as previous experiences, cultural background and gender. It does this by noticing the physical symptoms of biases in the team member's behaviors such as language, eye contact, blinking and smiles amongst others (6). It also gathers data with the help of algorithms that identify biased information and points of view (7).

Describe the challenge that the team faces:

It’s the year 2025, and a company has the challenge to recruit a team and develop a prototype for a next-generation home assistant. A product that can easily be affected by biases during its design process.

How can this team overcome their biases in search for a better product?

Briefly describe your solution:

UNBIAS.ME is an AI service that allows Hal (the Jovoto AI Steward System) or any other AI facilitator to fact-check a team’s decisions against their unconscious biases. This allows for a more diverse recruiting as well as committing to a bias-free design process, and, in consequence, to a better product. To do this, UNBIAS.ME uses deep learning processes, real-time machine learning, algorithmic personality detection, voice, image & video recognition & analysis, and natural language generation.

Explain how it improves teamwork and solves the problems we encounter today:

Today, companies have access to a global workforce and user base. This will widely increase in the future. Collaborating with & designing for people from very different backgrounds, can be challenging. Our idea can help make communication and decisions be less biased.

Additionally, the increasing awareness for problems such as sexism, racism, and xenophobia put pressure on companies (9, 10) to deliver biases free products. Our idea can help them respond to that market demand.

Explain how it works in 5 steps:

1: A plug-in is installed on Hal to give him/her the ability to fact-check the team members’ decisions against their unconscious biases. 2: A team member has to make a decision that involves other team members (e.g.: who to recruit next) or a project (e.g.: choosing the name of the product). 3: Hal collects information to analyze individual and group decisions. 4: Hal gives suggestions for improvement. 5: The team decides if they wants to implement the changes.

DID YOU USE ANY STOCK OR THIRD PARTY MATERIAL?

Yes.

IF YES, PLEASE LINK ALL STOCK, FONTS AND CREATIVE COMMONS MATERIAL HERE:

https://pixabay.com/en/connection-fractal-neural-pathways-647206/ https://thenounproject.com/search/?q=team&i=543768 https://thenounproject.com/search/?q=Sergey+Patutin+brain&i=291205

For the other research sources, please see attachment.