Labeled Faces in the Wild

Transparency and accountability 🙈

The Labeled Faces in the Wild (LFW) dataset is seen to suffer from several transparency and accountability limitations:

• Data collection. The images were scraped from the internet without obtaining consent from the individuals pictured, raising privacy concerns. 

• Inadequate documentation. The dataset lacks comprehensive documentation regarding its limitations and potential biases.

• Licensing issues. LFW was released without a specific license, potentially leading to uncontrolled use and derivation of the dataset.

• Complaints and appeals. The creators have acknowledged errors in the dataset but have chosen not to correct them to maintain consistency with previous research, making it challenging to address known issues.

• Derived datasets. The creators have limited control over datasets derived from LFW, which may perpetuate or exacerbate existing biases and privacy concerns.

Risks and harms 🛑

The Labeled Data in the Wild dataset has been criticised for privacy abuse and bias, and its potential misuse for surveillance and other purposes.

Research, advocacy 🧮

• Raji I.D., Fried G. (2021). About Face: A Survey of Facial Recognition Evaluation 

• Peg. K., Mathur A., Narayanan A. (2021). Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers

• Shmelkin R., Friedlander T., Wolf L. (2021). Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution

Investigations, assessments, audits 👁️

• Murgia M., Financial Times (2019). Who’s using your face? The ugly truth about facial recognition