The Fifth Elephant 2023 Monsoon

My Background

I am Kritika Saraswat, a passionate data scientist currently employed at AB InBev, the world’s largest brewing company headquartered in Leuven, Belgium. They own more than 500+ beer brands across the globe. With a strong background in data science and machine learning, I have been actively involved in driving impactful solutions within the domain. Prior to my current role, I gained valuable experience working at Sopra Steria, a prominent European company.
I am dedicated to sharing my knowledge and expertise with the community, and I am honored to have had the opportunity to contribute through interviews and thought leadership initiatives.

• You can find one of my Interview at this link: https://www.youtube.com/watch?v=nh2ytzYQTKI
• Additionally, I have also published an article on mitigating bias in AI systems, which you can explore here: https://3ai.in/mitigating-bias-in-ai-systems/
• LinkedIn Handle: https://www.linkedin.com/in/kritika-saraswat-130121158/
  I am excited to engage in meaningful discussions, exchange ideas, and collaborate with fellow professionals in the upcoming session.

Problem statement:

Consider the facial recognition software that mistakenly labeled two Black individuals as “gorillas,” exposing the inherent biases ingrained in AI systems. This incident served as a stark reminder of the perils of unchecked bias. Similarly, gender bias in a translator application caused the systematic translation of gender-neutral pronouns to masculine pronouns across multiple languages, perpetuating stereotypes and exclusion. Furthermore, a troubling investigation in 2006 uncovered that the compass tool, utilized by judges to determine bail decisions, exhibited bias against black defendants. The algorithmic prediction system incorrectly flagged black individuals as higher risk, while wrongly categorizing white defendants as low risk, amplifying racial disparities within the criminal justice system.
Solution: Responsible AI
Above instances underscore the critical importance of embracing responsible AI. It is very clear that Artificial intelligence bias can create problems ranging from bad business decisions to injustice and this has sent organizations scrambling to provide guidelines for responsible usage of AI, and the term used for it is known as Responsible AI.

In this session we uncover:

1. The critical problems that major companies face due to the lack of transparency in machine learning models.
2. We will explore the concept of responsible AI and tackle the pressing challenges that demand our attention.
3. We will delve into the areas where bias can seep into the machine learning pipeline and discuss the significance of ethical implementation in creating a fair and inclusive AI system.
4. Discover effective strategies and best practices for constructing responsible AI systems that empower individuals and communities. By prioritizing transparency, fairness, and accountability