Recognising & Mitigating AI Bias: Strategic Insights and Practical Approaches

Recognising & Mitigating AI Bias: Strategic Insights and Practical Approaches

Overview

In an era where artificial intelligence is reshaping media content production and journalism, leaders and teams working with AI are faced with the pivotal responsibility of ensuring that AI-driven tools and systems uphold the values of accuracy, fairness, and inclusivity.

This highly practical course is designed to empower you and your colleagues to

  • Understand how bias can arise in AI systems,
  • Through hands-on exercises, learn techniques to recognise and mitigate AI bias throughout the AI lifecycle,
  • Better support cross functional collaboration to promote inclusive and ethical AI adoption in content-driven environments.

Who it is for

  • Leads and members of AI cross functional teams: AI Task force, AI Strategy or Responsible AI teams
  • Diversity, Equity and Inclusion leads and officers
  • Newsroom AI or digital transformation leaders
  • Media content creators and programme makers interested in ethical AI practices
  • AI tech experts working in cross-functional teams with product teams.

Where possible, we recommend that several team members with complementary professional backgrounds attend the course together to maximise its impact on their collective work - i.e. Journalists and Tech teams.

What you will learn

By the end of the course, participants will be able to:

  1. Define key concepts of bias and fairness in AI, particularly in content production and journalism contexts
  2. Recognise how bias manifests across the AI lifecycle from data collection to algorithmic design and deployment
  3. Facilitate cross-functional collaboration between editorial, DEI, and technical teams
  4. Identify potential biases in AI use cases for journalism and gain practical insights to mitigate these biases

NB this is not a technical course but the concepts require collaboration between leadership, editorial and technical teams.

Guest Speaker

Desislava Vasileva

Desislava Vasileva is a leader in data science with over 10 years of experience in the field. Until recently, she was the Lead Data Scientist at the Financial Times, where she became a go-to expert on AI, shaped the technical side of the recommendation strategy, served on the Generative AI use case panel, and helped launch an editorial-focused AI acceleration team.

Previously, she spent five years at Experian working on regulatory and credit risk applications of machine learning. Desislava has now just begun a new role as a Data Science Manager at a leading telecommunications firm in Sofia, Bulgaria.