The system, first reported by Protocol, claims to scan users’ faces and their speech to determine their emotions.

Zoom detailed the system more in a blog post last month. The company says ‘Zoom IQ’ will be particularly useful for helping salespeople improve their pitches based on the emotions of call participants.

Naturally, the system is seen as rather dystopian and has received its fair share of criticism.

On Wednesday, over 25 rights groups sent a joint letter to Zoom CEO Eric Yuan. The letter urges Zoom to cease research on emotion-based AI.

The letter’s signatories include the American Civil Liberties Union (ACLU), Muslim Justice League, and Access Now.

One of the key concerns is that emotion-detecting AI could be used for things like hiring or financial decisions; such as whether to grant loans. That has the possibility to increase existing inequalities.

“Results are not intended to be used for employment decisions or other comparable decisions. All recommended ranges for metrics are based on publicly available research,” Zoom explained.

Zoom IQ tracks metrics including:

• Talk-listen ratio
• Talking speed
• Filler words
• Longest spiel (monologue)
• Patience
• Engaging questions
• Next steps set up
• Sentiment/Engagement analysis

Esha Bhandari, Deputy Director of the ACLU Speech, Privacy, and Technology Project, called emotion-detecting AI “creepy” and “a junk science”.

(Photo by iyus sugiharto on Unsplash)

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The ability to recognise emotions is a longstanding goal of AI researchers. Accurate recognition enables things such as detecting tiredness at the wheel, anger which could lead to a crime being committed, or perhaps even signs of sadness/depression at suicide hotspots.

Nuances in how people speak and move their facial muscles to express moods have presented a challenge. Detailed in a paper (PDF) on Arxiv, researchers at the University of Science and Technology of China in Hefei have made some progress.

In the paper, the researchers wrote:

“Automatic emotion recognition (AER) is a challenging task due to the abstract concept and multiple expressions of emotion.

Inspired by this cognitive process in human beings, it’s natural to simultaneously utilize audio and visual information in AER … The whole pipeline can be completed in a neural network.”

Breaking down the process as much as I can, the system is made of two parts: one for visual, and one for audio.

For the video system, frames of faces run through a further two computational layers: a basic face detection algorithm, and three facial recognition networks that are ‘emotion-relevant’ optimised.

As for the audio system, algorithms which process sound are input with speech spectrograms to help the AI model focus on areas most relevant to emotion.

Things such as measurable characteristics are extracted from the four facial recognition algorithms from the video system and matched with speech from the audio counterpart to capture associations between them for a final emotion prediction.

A database known as AFEW8.0 contains film and television shows that were used for a subchallenge of EmotiW2018. The AI was fed with 653 video and corresponding audio clips from the database.

In the challenge, the researchers AI performed admirably – it correctly determined the emotions ‘angry,’ ‘disgust,’ ‘fear,’ ‘happy,’ ‘neutral,’ ‘sad,’ and ‘surprise’ about 62.48 percent of the time.

Overall, the AI performed better on emotions like ‘angry,’ ‘happy,’ and ‘neutral,’ which have obvious characteristics. Those which are more nuanced – like ‘disgust’ and ‘surprise’ – it struggled more with.

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