Episode 17: How to Avoid an AI Scandal

06/14/23 • 53 min

Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm.

AI technology has now reached the point where it can potentially damage the reputation of an organisation, if improperly managed. As a result, many data scientists are now becoming very interested in understanding AI ethics and responsible AI.

In this episode of Value Driven Data Science, Chris Dolman joins Dr Genevieve Hayes to discuss strategies organisations and data scientists can apply to de-risk automated decisions, and in doing so, avoid an AI scandal.

Guest Bio

Chris Dolman is the Executive Manager, Data and Algorithmic Ethics at Insurance Australia Group, a Gradiant Institute Fellow and regularly contributes to external research on responsible AI and AI ethics. In 2022, he was named the Australian Actuaries Institute’s Actuary of the Year, in recognition of his work around data ethics, and was also included in Corinium Global Intelligence – Business of Data’s list of the Top 100 Innovators in Data and Analytics.

Talking Points

The risks associated with the use or design of AI-based decision-making tools.
How these risks might potentially be amplified in the case of new, cutting-edge algorithms, such as ChatGPT.
Why “boring” is sometimes better, when it comes to AI.
Examples of where things have gone wrong in the past.
Strategies for identifying and avoiding potential AI scandals before they occur.
The regulation and governance of AI, both now and in the future.

Links

Connect with Chris on LinkedIn
De-Risking Automated Decisions Report
Checkmate Humanity
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Guest Bio

Talking Points

The risks associated with the use or design of AI-based decision-making tools.
How these risks might potentially be amplified in the case of new, cutting-edge algorithms, such as ChatGPT.
Why “boring” is sometimes better, when it comes to AI.
Examples of where things have gone wrong in the past.
Strategies for identifying and avoiding potential AI scandals before they occur.
The regulation and governance of AI, both now and in the future.

Links

Connect with Chris on LinkedIn
De-Risking Automated Decisions Report
Checkmate Humanity
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Previous Episode

Episode 16: Improving the Data Science Customer Experience

The launch of Chat-GPT turned the business world upside down and left many people wondering about the future of their careers. How do you compete against AI? One solution is by delivering a superior customer experience.

In this episode, Dasun Premadasa joins Dr Genevieve Hayes to discuss why technical people often trip up when it comes to customer experience and what data scientists can do to overcome these issues.

Guest Bio

Dasun Premadasa is the founder of DASCX, an independent business analyst consultancy that helps businesses with their digital transformations and IT project delivery. He is also the host of the DASCX Show on YouTube.

Talking Points

How delivering a superior customer experience can boost your value as a data scientist.
Why technical people, such as data scientists, tend to neglect CX.
What does good CX look like?
The consequences of bad CX for data scientists and end users.
The importance of identifying the right customer when pitching a data science solution.
Strategies data scientists can employ to improve the experience of their end user.

Links

Connect with Dasun on LinkedIn
DASCX Show episode with Dasun and Genevieve
DASCX
Connect with Genevieve on LinkedIn
Be among the first to hear about the release of each new podcast episode by signing up HERE

Next Episode

Episode 18: Making AI Commercially Viable

Many data scientists dream of using their skills to develop ground-breaking AI technology. Yet, few manage to translate their dreams into commercially viable products – or even know where to begin.

In this episode, start-up founder Dr Jeroen Vendrig joins Dr Genevieve Hayes to discuss his experiences in developing AI-driven products, both in an academic setting and in a variety of organisations within the commercial world.

This is the first part of a three-part special focussing on the use of data science in start-ups.

Guest Bio

Dr Jeroen Vendrig is the Chief Technology Officer of ProofTec, an Australian technology start-up specialising in the development of AI-driven software for damage detection and assessment of high value assets. He has over 20 years’ experience in video analytics with world leading R&D labs and has over 25 patents in force.

Talking Points

The key differences between doing data science/AI in an academic setting and doing it in the commercial world.
How to go about translating academic research into commercially viable AI-based products.
What makes for a successful university/commercial collaboration?
The challenges of building AI products from scratch, including lack of data and how to tell if a project has the potential to be commercially viable.
Protecting IP for AI systems.
The impact of having real end users on AI product development.
The most valuable skills data scientists can develop for building commercial AI technologies.

Links