A 16th-Century Sketch Claims to Depict Anne Boleyn. A.I. Says It’s Her Mom.; The New York Times, May 19, 2026

Katherine Kornei, The New York Times; A 16th-Century Sketch Claims to Depict Anne Boleyn. A.I. Says It’s Her Mom.

Using facial-recognition technology, scholars have concluded that a 500-year-old drawing labeled “Anna Bollein Queen” more likely showed her mother, Elizabeth Howard.

"To dig into this mystery, Ms. Davies and her colleagues, including David G. Stork, a computer scientist and electrical engineer at Stanford University, turned to computational facial recognition. “This has one foot in art history and one foot in computer science,” Dr. Stork said...

Amit Roy-Chowdhury, a computer vision scientist at the University of California, Riverside, who was not involved in the research, said that facial recognition can play an important role in art history. But going forward, he added, it will be important to assemble larger training data sets of faces in artwork, as algorithms trained strictly on photographs can introduce uncertainties. And that could be a challenge, since there are millions of faces in photographs but far fewer in art. “For artwork, you don’t have that many examples,” Dr. Roy-Chowdhury said."

Where Automated Job Interviews Fall Short; Harvard Business Review (HBR), January 27, 2022

Zahira Jaser, 

Dimitra Petrakaki, Rachel Starr, and 

Ernesto Oyarbide-Magaña

, Harvard Business Review (HBR) ; Where Automated Job Interviews Fall Short

"The use of artificial intelligence in HR processes is a new, and likely unstoppable, trend. In recruitment, up to 86% of employers use job interviews mediated by technology, a growing portion of which are automated video interviews (AVIs).

AVIs involve job candidates being interviewed by an artificial intelligence, which requires them to record themselves on an interview platform, answering questions under time pressure. The video is then submitted through the AI developer platform, which processes the data of the candidate — this can be visual (e.g. smiles), verbal (e.g. key words used), and/or vocal (e.g. the tone of voice). In some cases, the platform then passes a report with an interpretation of the job candidate’s performance to the employer.

The technologies used for these videos present issues in reliably capturing a candidate’s characteristics. There is also strong evidence that these technologies can contain bias that can exclude some categories of job-seekers. The Berkeley Haas Center for Equity, Gender, and Leadership reports that 44% of AI systems are embedded with gender bias, with about 26% displaying both gender and race bias. For example, facial recognition algorithms have a 35% higher detection error for recognizing the gender of women of color, compared to men with lighter skin.

But as developers work to remove biases and increase reliability, we still know very little on how AVIs (or other types of interviews involving artificial intelligence) are experienced by different categories of job candidates themselves, and how these experiences affect them, this is where our research focused. Without this knowledge, employers and managers can’t fully understand the impact these technologies are having on their talent pool or on different group of workers (e.g., age, ethnicity, and social background). As a result, organizations are ill-equipped to discern whether the platforms they turn to are truly helping them hire candidates that align with their goals. We seek to explore whether employers are alienating promising candidates — and potentially entire categories of job seekers by default — because of varying experiences of the technology."