Friday, October 29, 2021
In the first part we cover five current specific challenges through examples: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) phrenology (e.g., biometric based predictions); (3) unfair digital commerce (e.g., exposure and popularity bias); (4) stupid models (e.g., Signal, minimal adversarial AI) and (5) indiscriminated use of computing resources (e.g., large language models). These examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles (e.g., principles vs. techniques), (2) cultural differences (e.g., Christian vs. Muslim); (3) regulation (e.g., privacy, antitrust) and (4) our cognitive biases. We finish with some recommendations to tackle these challenges and build responsible and ethical AI.
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