My Journey with Galvia – Data Lead Girija Shingte shares Learnings

My journey in AI began during the final year of my Bachelor’s degree back in my home country India when I was working on a capstone project that could determine a rare eye-disease in retinal images using Artificial Intelligence. Realising the limitless potential of AI and its positive implications was a huge inspiration for me. After completing my Masters in Data Analytics, I started working as a Data Scientist at Galvia. Here, we are developing an AI-powered decision intelligence platform for Enterprise Service Delivery teams. As a team, we are always encouraged to voice our unique perspectives, to ensure we are building a product that is safe, inclusive and trust-worthy. 

For those of you who have embarked on a career in AI and like me, want to make technology more inclusive and ethical here are a few things I learned from my journey so far:

1. Seek Inspiration and Guidance:
You can’t be what you can’t see. Before beginning my journey in AI, I looked for people who had similar objectives like myself and who could guide me at every step in designing my learning path. I was fortunate enough to connect with Tushar Gawande, an alumnus from National University of Galway, Ireland, who generously helped me select the right modules for my Data Analytics course that were aligned with the industrial requirements, advised me on approaches to study and selecting the subject for my thesis, and also helped me prepare for my job interviews. Having someone to look up to, with whom your objectives and values align, is extremely important.

2. Acquire Skills and Experience:
AI comprises of different disciplines such as Machine Learning, Natural Language Processing, Robotics, Computer Vision, etc. Depending on your area of interest, you can learn the skills either through courses and material available online or through a formal course. 

One online course that I highly recommend is Jose Portilla’s “Python for Data Science and Machine Learning Bootcamp” available on Udemy. The concepts I learned in this course helped me throughout my Master’s course, interview preparations, and in my current work. 

3. Build and Maintain a Network
Build yourself a network. Since COVID-19, a lot of conferences and events are being conducted virtually, which is a great opportunity to build your network with people from all over the world. Connect with people over LinkedIn not just when you are looking for a job referral, but to maintain your network. Discuss about ethical AI and get a high level understanding of the steps they and their organisations are taking towards standardising and governing data to fight AI biases.

4. Be Vocal
It’s important to have diversity in your team to highlight the inequities in data and algorithms from the very beginning. Also, being mindful of the recruitment process in your organisation and being vocal about what you think is unfair is important. I recently attended a webinar with data science team leads from IBM, Microsoft, and Facebook who all mentioned that they were extremely diligent about the jargon used in job descriptions. Their objective was to ensure that a female applicant should never feel that the job is meant for a male applicant, which I thought was brilliant. There have been instances where I have looked at a job description and assumed the employers would prefer a male candidate over a female. 

5. Give back to the Community
Share your work with the world and contribute to the development of open source packages as well. This way you are also giving back to the AI community. 

Every time I work on a mini-project, I share it on GitHub. It is a great way to present your work to the world.

The AI community needs a large, inclusive, and a diverse support system to bring about a significant change and minimise the skewness in data. Inspiring, encouraging, and mentoring newcomers to take up AI, and helping them understand the repercussions of biased AI and why we need a change is extremely critical. We need some overcorrection in the field of AI. Acknowledging that biases exist, understanding their negative implications, and fixing them are all vital so that people can use AI-powered technologies in a secure manner. 

Good luck on your journey!

ENDS

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