What does bias in AI technology mean and what can it look like? What can be done about it in practice? What are helpful tools for this? These are some of the questions Laura Alonso Alemany reflects on in her talk.

Video

Summary and transcript

Best-of

Here you will find short excerpts from the talk. They can be used as micro-content, for example as an introduction to discussions or as individual food for thought.

Ideas for further use

• Laura Alonso Alemany’s talk is a valuable resource for educational purposes. It can be integrated into courses focusing on ethics, technology, and social impact. The talk encourages critical thinking and discussions among students about the consequences of AI biases in various sectors such as justice, education, and health.
• The talk serves as a catalyst for discussions and training programs within companies involved in AI development. It can be used to train staff on the importance of auditing and inspecting AI systems. Laura’s examples of real-world mistakes underscore the potential costs of overlooking biases, motivating companies to adopt thorough auditing practices.
• Local organizations can organize screenings of the talk to raise awareness within communities about the potential biases and mistakes in AI systems. This can spark community dialogues on the implications of AI, empowering people to recognize and address biases that might affect them directly.

Licence

Laura Alonso Alemany: Now that you are concerned about AI, what can you do about it? by Laura Alonso Alemany, Fundación Via Libre / Universidad Nacional de Córdoba for: Goethe Institut | AI2Amplify is licensed under Attribution-ShareAlike 4.0 International

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