Upcoming Webinars

AI Technology for Market Research

Capabilities, Limitations, Human Impacts, Ethical Concerns and Implementation Strategies

Date: September 25, 2024 | 12 p.m. EDT | Free

Speaker: Rolfe Swinton, VP Data & AI Innovation and Data Partnerships, North America for NielsenIQ

In this webinar we will discuss: 

  1. AI’s Capabilities and Limitations: We will discuss the unpredictable and varied capabilities of AI, such as its proficiency in some tasks and weaknesses in others. We will discuss the importance of experimenting with AI to understand its strengths and weaknesses.
  2. Human-AI Interaction:  We will discuss importance of human involvement in AI processes and that people need to spend significant time using AI to understand its potential and limitations.
  3. Organizational Impact: We will discuss ways AI can impact organizational structures and workflows and in particular the need for organizations to encourage AI experimentation and usage among employees.
  4.  Biases and Ethical Concerns: We will discuss the biases in AI systems, stemming from training data and human inputs. We will discuss the importance to understand and address these biases to ensure fair and ethical AI usage.  
  5. Future of AI: We will also discuss the fact that AI technology is rapidly evolving, and organizations must adapt quickly and why it is critical for companies to experiment with AI now.  

Learning Outcomes: 

  • You will get some specific suggestions on ways you can approach experimentation starting today, even if you haven’t yet tried as well as some more advanced strategies for experimentation if you are already an advanced user.
  • You will get specific approaches to think about bias detection and comparing that to your existing anti-bias processes. 
  • Learn why you need to be experimenting with the use of AI in your business.
  • Learn what you can do right now to safely experiment with AI.
  • Learn to measure the positive impact of these experiments so you can scale.
  • Learn how this kind of experimentation doesn’t require massive investments and rather how we can learn from others mistakes in making massive investments too soon.

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