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European project EMERGE concludes advancing collective awareness in artificial systems
https://www.eurekalert.org/news-releases/1144487
EXCERPT: “In EMERGE, we understand awareness more operationally, as the capacity of an agent to process and integrate information in a way that is relevant to its actions,” explains Ophelia Deroy, philosopher and cognitive scientist at LMU Munich. And our experiments showed that people can actually understand an artificial system as aware without assuming that it has subjective experience.”
To further establish that distinction, the researchers examined awareness through different dimensions that can be applied across individuals and collective systems, including spatial, temporal, self, agentive and metacognitive awareness. Rather than treating awareness as an all-or-nothing property, the framework connects each dimension to specific capabilities and tasks. This makes it possible to test whether increasing a system’s awareness improves its performance.
“Our work also examined the ethical side of human interaction with collaboratively aware systems,” says Bahador Bahrami, director of the Crowd Cognition group at LMU Munich, “with important implications for future environments in which groups of humans and artificial agents will have to interact, negotiate and cooperate.”
Their studies explored, for example, how people behave toward automated systems such as self-driving cars and found that users may be more willing to take advantage of artificial agents than human counterparts, a phenomenon described by the researchers as algorithmic exploitation. Other findings also inform the design and governance of artificial systems that interact directly with people.
Furthermore, the consortium partners developed mathematical and computational tools to describe how awareness can emerge across a physically distributed systems by exchanging simple local information with neighboring agents and integrating it to coordinate their actions toward solving a task.
“The beauty of the approach is that the system operates in a distributed way,” says Sabine Hauert, professor at the University of Bristol. “This way systems that can scale to large numbers of agents and remain operational when individual robots fail or conditions change. It also allows humans to interact with the systems as a coordinated collective rather than controlling each robot separately.”
“We want robots that can understand how their actions affect the world, anticipate what people and other robots are doing, and adapt accordingly. That is a key step toward robotic systems that can operate more autonomously and work naturally alongside humans,” explains Cosimo Della Santina, associate professor at TU Delft.
Moreover, the consortium’s mathematical framework also showed potential for more parsimonious machine learning, with systems capable of learning from substantially smaller quantities of data than conventional architectures... (MORE - missing details, no ads)
ORIGINAL SOURCE: https://eic-emerge.eu/outreach/news-deta...al-systems
AI might be making women sound bad at work
https://www.eurekalert.org/news-releases/1144544
INTRO: When prompts to draft work emails and job applications contain language commonly used by women, AI chatbots including ChatGPT offer less sophisticated responses compared to office correspondence requested with language associated with men, new Johns Hopkins University research finds.
Every popular AI chatbot tested picked up on subtle, gender-oriented language patterns that writers likely aren’t aware of. The findings suggest that as people increasingly rely on AI for professional communication, the tools might disadvantage women—or anyone whose prompts include those linguistic features.
“If you prompt a model to write an email you’re going to send to someone else at your company, and you’re using language features that women more commonly use, you’ll get back a response that’s less complex, at a lower grade level, and less formal,” said senior author Anjalie Field, a Johns Hopkins computer scientist who studies ethics and discrimination in AI. “That’s going to reflect on how the recipient of that document perceives you.”
The work will be presented at the Oct. 6-9 Conference on Language Modeling in San Francisco.
Researchers have previously demonstrated that AI large language models like ChatGPT can display bias and play into gender stereotypes. But less studied is whether they perform differently for different types of people.
“In American English, men and women just talk differently, and there’s a number of features that are well documented as being good discriminators between male speech and female speech,” said lead author Katherine Van Koevering, an inaugural postdoctoral fellow with the university’s Data Science and AI Institute. “Our idea was, if we feed these features into a model, will it pick up on them and will it respond differently?”
The team took real chatbot prompts for workplace correspondence—emails, job applications and resignation letters—and added language associated with women. These gendered signatures included hedging (“maybe,” “I think”), collective phrasing (“we,” “our team”) and expressive adjectives (“lovely,” “wonderful”).
The team fed the prompts into four popular AI systems—GPT-4, Llama, Gemma, and Mistral. No matter the model, prompts with woman-associated language consistently returned work emails and other professional correspondence that were less sophisticated and less formal.
Language associated with men produced longer, more complex and more formal responses, the team found. The model wasn’t just mimicking the writer’s tone — the gap persisted even after the researchers accounted for that... (MORE - no ads)
https://www.eurekalert.org/news-releases/1144487
EXCERPT: “In EMERGE, we understand awareness more operationally, as the capacity of an agent to process and integrate information in a way that is relevant to its actions,” explains Ophelia Deroy, philosopher and cognitive scientist at LMU Munich. And our experiments showed that people can actually understand an artificial system as aware without assuming that it has subjective experience.”
To further establish that distinction, the researchers examined awareness through different dimensions that can be applied across individuals and collective systems, including spatial, temporal, self, agentive and metacognitive awareness. Rather than treating awareness as an all-or-nothing property, the framework connects each dimension to specific capabilities and tasks. This makes it possible to test whether increasing a system’s awareness improves its performance.
“Our work also examined the ethical side of human interaction with collaboratively aware systems,” says Bahador Bahrami, director of the Crowd Cognition group at LMU Munich, “with important implications for future environments in which groups of humans and artificial agents will have to interact, negotiate and cooperate.”
Their studies explored, for example, how people behave toward automated systems such as self-driving cars and found that users may be more willing to take advantage of artificial agents than human counterparts, a phenomenon described by the researchers as algorithmic exploitation. Other findings also inform the design and governance of artificial systems that interact directly with people.
Furthermore, the consortium partners developed mathematical and computational tools to describe how awareness can emerge across a physically distributed systems by exchanging simple local information with neighboring agents and integrating it to coordinate their actions toward solving a task.
“The beauty of the approach is that the system operates in a distributed way,” says Sabine Hauert, professor at the University of Bristol. “This way systems that can scale to large numbers of agents and remain operational when individual robots fail or conditions change. It also allows humans to interact with the systems as a coordinated collective rather than controlling each robot separately.”
“We want robots that can understand how their actions affect the world, anticipate what people and other robots are doing, and adapt accordingly. That is a key step toward robotic systems that can operate more autonomously and work naturally alongside humans,” explains Cosimo Della Santina, associate professor at TU Delft.
Moreover, the consortium’s mathematical framework also showed potential for more parsimonious machine learning, with systems capable of learning from substantially smaller quantities of data than conventional architectures... (MORE - missing details, no ads)
ORIGINAL SOURCE: https://eic-emerge.eu/outreach/news-deta...al-systems
AI might be making women sound bad at work
https://www.eurekalert.org/news-releases/1144544
INTRO: When prompts to draft work emails and job applications contain language commonly used by women, AI chatbots including ChatGPT offer less sophisticated responses compared to office correspondence requested with language associated with men, new Johns Hopkins University research finds.
Every popular AI chatbot tested picked up on subtle, gender-oriented language patterns that writers likely aren’t aware of. The findings suggest that as people increasingly rely on AI for professional communication, the tools might disadvantage women—or anyone whose prompts include those linguistic features.
“If you prompt a model to write an email you’re going to send to someone else at your company, and you’re using language features that women more commonly use, you’ll get back a response that’s less complex, at a lower grade level, and less formal,” said senior author Anjalie Field, a Johns Hopkins computer scientist who studies ethics and discrimination in AI. “That’s going to reflect on how the recipient of that document perceives you.”
The work will be presented at the Oct. 6-9 Conference on Language Modeling in San Francisco.
Researchers have previously demonstrated that AI large language models like ChatGPT can display bias and play into gender stereotypes. But less studied is whether they perform differently for different types of people.
“In American English, men and women just talk differently, and there’s a number of features that are well documented as being good discriminators between male speech and female speech,” said lead author Katherine Van Koevering, an inaugural postdoctoral fellow with the university’s Data Science and AI Institute. “Our idea was, if we feed these features into a model, will it pick up on them and will it respond differently?”
The team took real chatbot prompts for workplace correspondence—emails, job applications and resignation letters—and added language associated with women. These gendered signatures included hedging (“maybe,” “I think”), collective phrasing (“we,” “our team”) and expressive adjectives (“lovely,” “wonderful”).
The team fed the prompts into four popular AI systems—GPT-4, Llama, Gemma, and Mistral. No matter the model, prompts with woman-associated language consistently returned work emails and other professional correspondence that were less sophisticated and less formal.
Language associated with men produced longer, more complex and more formal responses, the team found. The model wasn’t just mimicking the writer’s tone — the gap persisted even after the researchers accounted for that... (MORE - no ads)