Artificial Intelligence

Gender-neutral voice assistant developed to ‘combat gender bias’ in artificial intelligence

Gender-neutral voice assistant developed to ‘combat gender bias’ in artificial intelligence

If you’ve been looking for the interesting sociology paper topics, good news for you – you can take a few hints from this article. Researchers and campaigners have designed a gender-neutral voice for use in virtual assistants to promote inclusivity in voice technology and help end gender bias.

Built by creative agency Virtue, Copenhagen Pride and equality campaign organisation EqualAI, the voice is designed to reflect the growing number of people who define themselves as gender-neutral.

The developers said they hope to eventually have the voice, known as Q, supported by major platforms which use voice technology in virtual assistants, including Apple, Amazon and Google.

The final voice was created using a number of recorded voices from people who do not identify as male or female, which were then modulated to make them sound genderless, before being tested on more than 4,500 people in Europe to gauge how they were perceived.

Researchers and campaigners have designed a gender-neutral voice for use in virtual assistants to promote inclusivity in voice technology and help end gender bias (stock image)

Researchers and campaigners have designed a gender-neutral voice for use in virtual assistants to promote inclusivity in voice technology and help end gender bias (stock image)

Testers were asked to grade the voice between one and five, with one meaning a male voice and five a female one, with the voice modulated and tested again until it was widely perceived as gender neutral, the developers said.

Some of these platforms currently support the option of having either a male or female voice for interactions with their voice assistant.

Julie Carpenter, a human and robot interaction researcher who consulted on the creation of Q, said the voice challenged existing belief systems.

‘Q adds to a global discussion about who is designing gendered technology, why those choices are made, and how people feed into expectations about things like trustworthiness, intelligence, and reliability of a technology based on cultural biases rooted in their belief system about groups of people,’ she said.

‘Q is a step forward in true innovation because it forces a critical examination of these belief systems.’

Thomas Rasmussen, head of communication for Copenhagen Pride, said those behind Q hoped to challenge gender stereotypes as well as get the attention of big tech firms.

‘Copenhagen Pride works to challenge the gender binary and combat strong, harmful and often very limiting gender stereotypes that fail to recognise non-binary gender identities,’ he said.

‘With Q – a neutral voice with no preassigned gender – we aim to get the attention of leading technological companies that work with AI to ensure they are aware that a gender binary normativity excludes many people and to inspire them by showing how easy it would actually be to recognise that more than two genders exist when developing artificial intelligence devices.

‘This is about giving people choices and options. It is about freedom and inclusion.’

HOW DOES ARTIFICIAL INTELLIGENCE LEARN?

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.

ANNs can be trained to recognise patterns in information – including speech, text data, or visual images – and are the basis for a large number of the developments in AI over recent years.

Conventional AI uses input to ‘teach’ an algorithm about a particular subject by feeding it massive amounts of information.

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information - including speech, text data, or visual images

AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn. ANNs can be trained to recognise patterns in information – including speech, text data, or visual images

Practical applications include Google’s language translation services, Facebook’s facial recognition software and Snapchat’s image altering live filters.

The process of inputting this data can be extremely time consuming, and is limited to one type of knowledge.

A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.

This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.

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