Computer-assisted CI fitting: Is the learning capacity of the intelligent agent FOX beneficial for speech understanding?

Meeuws, Matthias; Pascoal, David; Bermejo, Iñigo; Artaso, Miguel; Ceulaer, Geert De; Govaerts, Paul

doi:10.1080/14670100.2017.1325093

Cited by 29 publications

(28 citation statements)

References 20 publications

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“…Artificial intelligence (AI) and machine learning (ML) are applied in many areas [1] such as medical technologies [2,3], big data [4], various neuro linked technologies [5][6][7][8][9][10][11][12][13][14][15], autonomous cars, drones, As such, disabled people have a stake in AI/ML advancements and how they are governed. Furthermore, disabled people have many distinct roles to contribute to AI/ML advancement discussions in general and in particular to AI/ML ethics and governance discussions, such as therapeutic and non-therapeutic user, knowledge producer, knowledge consumer, influencer of the discourses, and victims.…”

Section: Introductionmentioning

confidence: 99%

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Lillywhite

Wolbring

2020

Societies

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Artificial intelligence (AI) and machine learning (ML) advancements increasingly impact society and AI/ML ethics and governance discourses have emerged. Various countries have established AI/ML strategies. “AI for good” and “AI for social good” are just two discourses that focus on using AI/ML in a positive way. Disabled people are impacted by AI/ML in many ways such as potential therapeutic and non-therapeutic users of AI/ML advanced products and processes and by the changing societal parameters enabled by AI/ML advancements. They are impacted by AI/ML ethics and governance discussions and discussions around the use of AI/ML for good and social good. Using identity, role, and stakeholder theories as our lenses, the aim of our scoping review is to identify and analyze to what extent, and how, AI/ML focused academic literature, Canadian newspapers, and Twitter tweets engage with disabled people. Performing manifest coding of the presence of the terms “AI”, or “artificial intelligence” or “machine learning” in conjunction with the term “patient”, or “disabled people” or “people with disabilities” we found that the term “patient” was used 20 times more than the terms “disabled people” and “people with disabilities” together to identify disabled people within the AI/ML literature covered. As to the downloaded 1540 academic abstracts, 234 full-text Canadian English language newspaper articles and 2879 tweets containing at least one of 58 terms used to depict disabled people (excluding the term patient) and the three AI terms, we found that health was one major focus, that the social good/for good discourse was not mentioned in relation to disabled people, that the tone of AI/ML coverage was mostly techno-optimistic and that disabled people were mostly engaged with in their role of being therapeutic or non-therapeutic users of AI/ML influenced products. Problems with AI/ML were mentioned in relation to the user having a bodily problem, the usability of AI/ML influenced technologies, and problems disabled people face accessing such technologies. Problems caused for disabled people by AI/ML advancements, such as changing occupational landscapes, were not mentioned. Disabled people were not covered as knowledge producers or influencers of AI/ML discourses including AI/ML governance and ethics discourses. Our findings suggest that AI/ML coverage must change, if disabled people are to become meaningful contributors to, and beneficiaries of, discussions around AI/ML.

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Section: Introductionmentioning

confidence: 99%

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Lillywhite

Wolbring

2020

Societies

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“…Artificial intelligence (AI) and machine learning (ML) are applied to many scientific and technological endeavors such as personalized medicine (Feng, Badgeley, Mocco, & Oermann, 2018), medical diagnostics (Ilyasova, Kupriyanov, Paringer, & Kirsh, 2018), big data (André et al, 2018), virtual reality (Falconer & Ortega, 2018), neuroimaging (Feng et al, 2018), brain computer interface (BCI) (Lee, 2016), artificial brain (Buttazzo, 2001), deep brain stimulation (Camara et al, 2015;Catherwood, Finlay, & McLaughlin, 2016), cochlear implants (Meeuws et al, 2017), transcranial magnetic stimulation (Erguzel et al, 2015), gaming (Shubik, 1960), autono-mous cars (Gonzalez et al, 2016), drones for military purposes (Sharkey, 2011), and various assistive technologies.…”

Section: Introductionmentioning

confidence: 99%

Coverage of ethics within the artificial intelligence and machine learning academic literature: The case of disabled people

Lillywhite

Wolbring

2019

Assistive Technology

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Disabled people are often the anticipated users of scientific and technological products and processes advanced and enabled by artificial intelligence (AI) and machine learning (ML). Disabled people are also impacted by societal impacts of AI/ML. Many ethical issues are identified within AI/ML as fields and within individual applications of AI/ML. At the same time, problems have been identified in how ethics discourses engage with disabled people. The aim of our scoping review was to better understand to what extent and how the AI/ML focused academic literature engaged with the ethics of AI/ML in relation to disabled people. Of the n = 1659 abstracts engaging with AI/ML and ethics downloaded from Scopus (which includes all Medline articles) and the 70 databases of EBSCO ALL, we found 54 relevant abstracts using the term "patient" and 11 relevant abstracts mentioning terms linked to "impair*", "disab*" and "deaf". Our study suggests a gap in the literature that should be filled given the many AI/ML related ethical issues identified in the literature and their impact on disabled people.

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“…The study performed by Matthias Meeuws should be noted, nemaly “Computer-assisted CI fitting: Is the learning capacity of the intelligent agent FOX beneficial for speech understanding? 27 : the processor was programmed with a predictive mode after their patient’s responses to verbal and tonal stimuli. In our study, responses to music frequency bands are considered.…”

Section: Discussionmentioning

confidence: 99%

Improvement of speech perception in noise and quiet using a customised Frequency-Allocation Programming (FAP) method

González

Barreiro

Miguel³

et al. 2019

Acta Otorhinolaryngol Ital

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SUMMARY The objective of this study is the evaluation of speech recognition of experienced CI recipients with standard MAP settings using the default frequency-allocation tables with the optimised frequency-allocation MAP. This is an observational, cross-sectional and quantitative-approach study on 50 adult cochlear implant recipients, 20 bilateral and 30 unilateral implant recipients. 23 men (46%) and 27 women (54%). All subjects were ≥ 18 years old. Differences between the means of the thresholds in tone audiometry in free fields were statistically significant in both unilaterally and bilaterally implanted patients; similar results were obtained between average benefits in disyllabic for both unilaterally and bilaterally implanted patients (p < 0.005). The differences between the means of scores were statistically significant for unilaterally and bilaterally implanted patients, similarly occurred with the average in HINT test between standard SRT50% programming and frequency allocation fitting (p < 0.005). Patients using the frequency allocation method, which assigns frequencies based on fundamental frequencies, showed better perception of disyllabic words and open-set sentences in quiet and in noise than patients previously fitted with standard fitting techniques. The method can be applied to different processors and used with different strategies of stimulation. It allows reduction of current intensity levels as well as an increase in the dynamic range and improves the quality of the representation of the signal.

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Computer-assisted CI fitting: Is the learning capacity of the intelligent agent FOX beneficial for speech understanding?

Cited by 29 publications

References 20 publications

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Coverage of Artificial Intelligence and Machine Learning within Academic Literature, Canadian Newspapers, and Twitter Tweets: The Case of Disabled People

Coverage of ethics within the artificial intelligence and machine learning academic literature: The case of disabled people

Improvement of speech perception in noise and quiet using a customised Frequency-Allocation Programming (FAP) method

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