Session on "Use Cases and Risks of ML in Capital Markets" | 23rd Dec at 4pm Hi everyone! The AI and Risk Mitigation project is well underway and for the third session, we will be joined by Rachna Maheshwari, Associate Director at CRI… more
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Mitigation of Racist & Biased AI by navigating towards ethical path of innovation
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.
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:
- The critical problems that major companies face due to the lack of transparency in machine learning models.
- We will explore the concept of responsible AI and tackle the pressing challenges that demand our attention.
- 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.
- Discover effective strategies and best practices for constructing responsible AI systems that empower individuals and communities. By prioritizing transparency, fairness, and accountability