Data Science Leaders Domino Data Lab

AI has enormous potential for good, not least in helping us make more ethical, sustainable decisions as investors and consumers. In this week’s episode Ron Potok, Head of Data Science at Clarity AI, explains how AI helps us overcome the challenges of collecting, normalizing and assessing Environmental, Social and Governance (ESG) data and making that data useful and convenient to humans when making decisions. Indeed, he reveals how AI can bring transparency to human-only ESG ratings that can be more opaque and prone to bias than an AI model, and the benefits of leveraging humans and AI models in tandem.

Join us as we discuss:Overcoming the ESG data quality challenges with AILeveraging AI to contextualize data and drive consistency How AI can provide greater transparency than human-only ratings

How do you trust black-box AI models with decisions that will make-or-break your business?

This week we speak with Gregory Zuckerman -- special writer at the Wall Street Journal and author of The New York Times bestseller of The Man Who Solved the Market -- to find out how the pioneers in algorithmic trading learned to stop worrying and trust their AI systems.

Join us as we discuss:
How trust in AI relies on trust in people and processesThe limits of explainability and transparencyThe power of systems over stories

Who doesn’t have a data science talent gap? Anyone? Most organizations struggle to realize their AI ambitions because of a lack of data science skills, a disconnect between the technology and the business domain, and a lack of leadership experience with AI.

Halliburton has been solving all three of these challenges with one of the earliest and largest corporate data science programs in the energy sector.

In today’s episode of Data Science Leaders, we are extremely fortunate to be joined by Dr. Satyam Priyadarshy, Managing Director, Technology Fellow and Chief Data Scientist at Halliburton who shares their best practices for upskilling talent, bridging the data science - business divide, and ensuring executive engagement.

Join us as we discuss:
How to upskill existing domain experts on data science methodsHow to engage and drive alignment with corporate stakeholders through workshopsThe benefits of upskilling domain experts on code-based data science toolsThe importance of involving and upskilling leadership

It’s been said: “When everything is important, nothing is important.” So how do you succeed with AI-driven transformation where everything – across people, process, and technology – is important? It requires leadership, a deliberate strategy, and ongoing organizational change.

Here to share insight on these transformational challenges and best-practices are Jen Stave and Catherine Feldman from the Digital, Data, and Design (D^3) Institute at Harvard. In this wide-ranging conversation, the duo draws upon seminal research from the Harvard Business School – such as professor Clay Christensen’s theory of Disruption – to explain how organizations must adapt their business and operating models, and make experimentation part of their organizational DNA.

Join us as we discuss:Disruption and the reasons so many AI projects failThe need for a holistic approach and strong leadership for AI successApplying a jobs to be done” approach to generative AI
Also don’t miss HBS professor Karim Lakhani’s Rev 4 Keynote, “Competing in the Age of AI”.

Generative AI is here and, unless you’ve been cloistered in a cave, you already know it’s making waves in nearly every industry. But when it comes to this shiny new technology, separating fact from fiction can become quite a challenge.

Luckily, in this episode, Rowan Curran, Analyst at Forrester, joins the show to demystify the latest leaps in AI tech, help you apply it to your business today, and give a glimpse of how it will affect the business landscape of tomorrow.

Join us as we discuss:Separate generative AI facts and fictionTake a closer look at AI applications you can start using todayExamine the future of AI and its impacts on the workforce and the workplace

“It is on the shoulders of leaders that they build and maintain an ethical AI risk program.” That’s the message Reid Blackman – author of “Ethical Machines” and founder CEO at Virtue Consultants – shares in this episode. He discusses the real ethical AI concerns — blackbox models, bias, hallucinations, privacy violations and more — and explains the crucial need for leadership accountability, buy-in from the very top of the organization, and a multi-party effort in building and maintaining AI ethical risk programs.

Join us as we discuss:Why AI poses greater ethical risks than other technologies (and humans)Leadership and the other key elements of a successful AI / digital ethics programThe importance of explainability