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The Electronic Privacy Information Center (EPIC) has filed a petition asking the Federal Trade Commission (FTC) to establish fair trade practices regarding the commercial use of artificial intelligence (AI) and charged that “HireVue, a leading provider of AI=based pre-employment screenings, is flouting national and international standards of transparency, fairness and accountability.” In particular, EPIC argued that applicants who seek employment at a company using HireVue’s assessment services have to submit to screening, and HireVue itself refuses to disclose its algorithm’s selection criteria.
Use of AI in employment screening is only one facet of EPIC’s petition, “which also cites the commercial use of AI for ranking tennis players, evaluating potential Airbnb guests, use of facial recognition for criminal justice purposes, etc.” According to EPIC, the unregulated use of AI technologies has caused serious harm to consumers who are subject to opaque and un-provable decision-making in multiple areas that is now carried out by AI. Even when business disclose their use of AI decision-making tools, they frequently fail to justify those tools’ accuracy, reliability, and necessity.
When emotion recognition research is used for lucrative commercial applications, a great deal of nuance is lost. Companies like HireVue have deployed AI for a number of commercial applications from AI-assisted hiring to school assessments, claiming that the algorithms’ analysis of micro-expressions and voice can pick out the best workers or students. This hype around emotion recognition has a shaky basis in science, and “has created a backlash from tech ethicists and activists who fear that the technology could raise the same kinds of discrimination problems as predictive sentencing or housing algorithms for landlords deciding whom to rent to.”
This hype worries the researchers too, who agree that their work is being co-opted for commercial applications and say that a lack of government regulation is bad for both consumers and researchers. Emotion recognition is still a nascent technology, and no researcher in the field would claim that it can actually assess an individual’s internal emotions and experience. It can estimate how an individual’s emotions might be perceived by others or suggest trends, but the technology requires observing characteristics like body posture and gait, as well as using biometric sensors and audio, in addition to looking at someone’s face.
Many researchers think that a ban on emotion recognition technology is too broad, but also believe that a regulatory vacuum is harmful. In such a regulatory vacuum, companies are able to make outrageous claims about the emotion recognition technology they are using, putting buyers who are not well educated at risk. Because of the resulting use of these technologies in things like job assessments, the technologies have the power to determine people’s access to resources. With more restrictions, or perhaps even a ban, on the use of emotion recognition in commercial applications, science will have time to catch up to commercialization and either validate or repudiate the claims made by companies like HireVue.
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Play this bingo game with your kids to teach them about AI - Artificial intelligence is all around us. But we often don’t notice how much it’s incorporated into the different aspects of our lives. This game challenges you and your kid(s) to notice.
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Boston Dynamics’ dog robot Spot is going to patrol an oil rig in Norway - Spot, a four-legged robot made by Boston Dynamics, has got one of its first real-world assignments: it will be start being used by oil and gas firm Aker BP to patrol a rig in the Skarv field in the Norwegian Sea this year.
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MIT CSAIL’s AI corrects outdated Wikipedia articles - The English Wikipedia has over 6 million articles, and the combined Wikipedias for all other languages contain over 28 billion words in 52 million articles in 309 languages. In search of an autonomous solution to Wikipedia’s need for constant, large-scale pruning, researchers at MIT have developed an system that uses AI and machine learning to address inconsistencies in Wikipedia articles.
Sony Envisions an AI-Fueled World, From Kitchen Bots to Games - Hiroaki Kitano, a research scientist at Sony, is leading a new effort to infuse cutting-edge AI across the company. The Japanese giant believes AI will create smarter cameras, more cunning videogame characters, and even the first helpful kitchen robots.
Intuition Robotics raises $36 million to bring AI companions to everyone - Israeli robotics startup Intuition Robotics has raised $36 million in a series B round of funding for its mission to create “social companion” robots and related technologies. The company’s initial focus is on reducing loneliness and isolation in elderly people.
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Learning to See Transparent Objects - To enable machines to better sense transparent surfaces, Google AI teamed up with researchers from Synthesis AI and Columbia University to develop ClearGrasp, a machine learning algorithm that is capable of estimating accurate 3D data of transparent objects from RGB-D images.
Boston Dynamics’ Spot robot is surprisingly good at pulling a rickshaw - Boston Dynamics has always talked about Spot’s ability to go places too dangerous for humans, whether it’s checking for gas leaks or disposing of bombs and other hazardous materials. But now, former MythBusters host Adam Savage has developed a new use for the expensive robodog: pulling him around in a homemade rickshaw.
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The Future of Deep Learning Is Unsupervised, AI Pioneers Say - Machines can perform well at recognizing images and understanding language when humans spoon-feed them labeled data. But, according to recent Turing Award winners Yann Lecun, Geoffrey Hinton, and Yoshua Bengio, the next step in AI advancement will require the technology to figure out how to learn on its own.
AI on steroids: Much bigger neural nets to come with new hardware, say Bengio, Hinton, and LeCun - According to the three Turing Award winners and deep learning pioneers, new hardware designed for deep learning will allow for much larger neural networks, and in addition to allow researchers to tinker more with the transformation built into such networks.
Google Head of Ethical AI Research on Data Biases and Ethics - Margaret (Meg) Mitchell, Co-Head of Ethical Research Group at Google AI, addresses data biases, algorithms, regulation, and more.
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