12-01, 15:00–15:30 (UTC), Talk Track I
In today’s digital age, we use machine learning (ML) and artificial intelligence (AI) to solve problems and improve productivity and efficiency. Yet, there’s risk in delegating decision-making power to algorithmically based systems: their workings are often opaque, turning them into uninterpretable “black boxes.”
This risk is especially acute when algorithms are tasked with making life-changing decisions (e.g., legal, law enforcement, credit scoring, and risk assessment), and it can be difficult to know if an AI/ML-based decision was made in a fair manner, reflecting the values of society. To be clear, this “black box” nature of AI/ML doesn’t necessarily imply that these algorithms were designed with malicious intent. These possible negative outcomes simply arise from the power and complexity of AI/ML algorithms at scale, combined with potential inequities latent in the data used to train the model. The question remains, however: if we permit AI/ML to make life-altering decisions, what are the implications for our social, economic, technical, legal, and environmental systems?
Significant work has been done to try to solve this challenge, leading to development of over 160 “ethical AI principles,” with the goal of providing guidance to organizations to act responsibly to avoid causing societal harm. However, although the intentions of this work are good, this maelstrom of guidance, none of which is compulsory, can sometimes add confusion instead of clarity.
It’s important to think carefully about how we implement these algorithms, and how we delegate decisions and data usage, given the difficulty of enacting effective human oversight and governance over AI/ML-based decision making. This talk focuses on harmonizing and aligning approaches, illustrating the opportunities and threats of AI, while raising awareness of society’s responsibility to demystify governance complexity and to establish an equitable digital society.
No previous knowledge expected
Dr. Lalitha Krishnamoorthy is the Co-Founder and CEO of OpenTeams Global. In her role at OpenTeams Global, she is responsible for driving the organization's innovation, expanding the partner and network ecosystem across industries and academia, defining the portfolio and investment strategy, and delivering transformative experiences. She leads a team of open source experts that work with clients and business partners on their transformation journey through Data and AI methodologies to achieve stellar business results.
Prior to her current role, Lalitha served as Director of IBM Digital Commerce, SaaS, and Data Platforms where she directed IBM's Digital Business Transformation strategy through data-driven, commerce-ready, subscription-first product offerings. During her 20 year career at IBM, Lalitha held roles of increasing responsibility and oversaw numerous portfolio's leading IBM’s transition from core databases to federated data to advanced analytical capabilities, and eventually data, cloud and artificial intelligence.
She serves on the champions board of the Texas Girls Collaborative Project, a University of Texas at Austin statewide network committed to motivating and supporting women and girls to pursue and thrive in careers in science, technology, engineering, and mathematics (STEM).
Lalitha holds a doctorate in Neuro-Symbolic Artificial Intelligence, holds several invention patents, is a regular speaker at industry conferences, is on the board of directors for many startups, and a strong advocate for diversity and inclusion in technology.
Today, Lalitha is most interested in making technology equitable for everyone, as this creates a new set of market leaders and competitive disruption.