Inspiration

Many of us find it difficult to convey our thoughts during discussions for fear of being rejected. Some people gain conversational dominance by interrupting the speaker continuously or even confronting them, not allowing them to complete what they intend to share. Conveying thoughts and ideas during meetings/discussions, is a challenge due to Fear of Rejection. This idea of FINclusive stemmed from such struggles many of us faced in our workplace.
When we researched in-depth, we realized that many who try to gain conversational dominance do so because of biases. While some do it consciously, for many, it is their unconscious biases that come to play.
Making us self aware is the best way to beat unconscious biases. If there is some way to detect and alert us of our biases as and when we encounter them, we will become more watchful of the before-mentioned situations and, over some time, we will overcome such biases due to reinforcement learning. That’s where the discussions on FINclusive started.

What it does

FINclusive is an app that detects unconscious biases in human-to-human conversations and alerts the people involved using a machine learning model. One technique of gaining conversational dominance, which is interrupting the speaker is addressed in FINclusive currently. Finclusive’s current implementation addresses interruptions leading to conversational dominance. Integrated with Finastra’s solutions, FINclusive can alert customer service representatives of such biases during conversations with customers, thus offering an enhanced inclusive customer experience.

How we built it

NLP is used for voice to speech conversion and AI model built on convolutional neural networks to identify biases. For this hack, Twitter sentiment analysis is used to identify interruptions. HTML5 and java script is used for UI.
Consumer API of Fusion Fabric.cloud retrieves the customer information.

Challenges we ran into

  • Unavailability of training data for the CNN model. Thus we used Twitter sentimental analysis library for this hack
  • We intend to collect training data from movie dialogues and create a CNN model
  • If organizations can share customer support recordings of call centers, that would be a great source of data for building the training model
  • Also, we wanted to do this real-time in branch; however without consent of the customer we cannot record the data; hence we fine-tuned the use case to call center based customer service. However, we plan to solutionize FINclusive to support real-time branch based customer service in the future

Accomplishments that we're proud of

Excited that FINclusive can help several of us who have faced repercussions of unconscious biases in multiple ways. The potential end users of FINclusive will gain confidence over time, and will be devoid of their own unconscious biases, and well as others.
Statistical reports and predictions on unconscious biases will help employees create an inclusive workplace. We believe FINclusive will be a huge value add to progressive organizations in driving their diversity and inclusion agenda.

What we learned

We learnt that technology can be used to uncover our unconscious biases. The possibilities with deep learning and neural networks are unlimited. With dedication, hard work and patience, we can define machine learning models that are helpful for human beings to reduce micro-stressors in their daily life.

What's next for FINclusive

FINclusive demo addresses one arena of conversational dominance, namely, interruptions. There are a range of possibilities for incorporating areas such as detection of topic changes, sarcastic tones, confrontations etc. In addition, the solution can be of use in meetings and discussions, wherein everyone gets notified when a conflicts arises or when one person is targeted. This will add to workplace professionalism, eliminating workplace stress. Deployment at customer support solutions will better the inclusiveness for customer experiences.

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