Transparency and explainability for algorithmic decisions at work
Privacy International's recommendations for improving transparency and explainability for algorithmic decisions at work, including concrete and detailed examples of best practices for platforms to implement.
From years spent investigating, documenting and exposing the impact of algorithmic management and workplace surveillance on wo...
By analyzing the article, several patterns can be identified:
Ambiguity (ARC-0024): The article raises concerns about the lack of clarity in communication to drivers regarding the reasons behind their higher risk ratings and the resulting restrictions on their trip requests.
False Framing (ARC-0039): The system is presented as a tool for detecting fraud, but the potential impact on drivers' livelihoods and lack of transparency suggest that it may be more about control than combating dishonesty.
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