Deaf in AI: AI language technologies and the erosion of linguistic rights

Maartje De Meulder

REVIEW 3 major objections 5 minor 19 references

Deaf in AI: AI language technologies and the erosion of linguistic rights

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Framing AI sign language tools as substitutes for human interpreters risks eroding deaf people's linguistic rights.

desk verdict A needed and largely convincing conceptual contribution that names the 'slippery slope' of AI sign language tech, but its strongest warning rests on uncited anecdotes and needs either more evidence or a more modest claim. read the letter →

arxiv 2505.02519 v2 pith:243JAR37 submitted 2025-05-05 cs.CY

Maartje De Meulder This is my paper · ORCID

classification cs.CY

keywords AIlanguagetechnologiessigninterpretinglinguisticrightsdeafcommunitiestechnoableismaccesshierarchiescriptechnoscienceslipperyslope

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the way AI sign language technologies are being framed—as potential substitutes for human sign language interpreters—threatens deaf people's hard-won linguistic and accessibility rights. The central warning is that voluntary use of AI tools can slide into imposed use: when interpreter hours run out or budgets tighten, deaf consumers are increasingly directed toward speech-to-text AI, and what begins as a convenience becomes a replacement. The paper claims this substitution framing is built into the technology itself, because sign language AI is trained on interpreted data and benchmarked against interpreters rather than against deaf people's own languaging practices—the ways deaf people actually use signed and written languages. This matters because the erosion may already be underway, and it would hit hardest deaf people without the literacy, resources, and networks that make AI tools feel empowering.

What carries the argument

The paper's load-bearing concept is 'slippery slope rights': measures initially rejected by deaf communities because of their limitations become normalised over time and then accepted as adequate, so that each small adaptation of AI in low-stakes contexts widens the path to imposed use in high-stakes ones. Alongside it is the 'substitute framing'—the persistent benchmarking of AI sign language technologies against human interpreters in research, funding, and deaf-service advocacy—which the paper argues distorts both the technologies and the policy choices around them. The mechanism is carried by three linked observations: AI sign language systems are trained on interpreted datasets that reflect interpreter signing rather than deaf community usage; deaf people already perform invisible 'access labour' adapting to interpreters, a subordination that AI can compound; and deaf-led perspectives are largely absent from sign language AI research and development.

What would settle it

Compare national interpreter-hour allocation and deaf consumers' actual use of speech-to-text AI in two or three European countries over several years, holding demand for interpreting constant. If deaf people's access to human interpreters does not decline as AI captioning becomes available—or if interpreter funding stays flat or rises—the paper's central warning about erosion of linguistic rights would lose its empirical basis.

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Extended reading notes

Core claim

The paper's central claim is that sign language AI technologies are not, and should not be, competitors with human interpreters; they are meant to support and reflect the diverse languaging practices of deaf communities. Framing them as substitutes sets a benchmark that undermines their purpose and creates a 'slippery slope' in which access rights that deaf communities once rejected as inadequate—such as automated captioning or signing avatars—gradually become normalised and then imposed in high-stakes settings like healthcare and courts. The paper contends that the erosion of linguistic rights may already be underway, driven by governments and institutions that see AI as a cheaper, more efficient alternative to interpreter services, and reinforced by a field of sign language AI development that is dominated by hearing, non-signing actors, trained on interpreted datasets, and lacking deaf leadership.

Load-bearing premise

The argument depends on the assumption that governments and institutions will actually substitute AI for human interpreters at scale; the paper's direct evidence for this is limited to anecdotal reports from some European countries, so if substitution does not materialise, the central warning loses much of its urgency.

Editorial extensions

If this is right

If the substitute framing persists, AI sign language tools will be judged mainly by how well they mimic human interpreters, not by whether they serve deaf users' actual communication needs.
Low-stakes uses such as automated captions in public transport and hospitality will normalise AI access, making it harder for deaf people to refuse these tools when they are proposed in healthcare, legal, and emergency settings.
Deaf people who cannot calibrate their signing or written-language skills to what AI systems can recognise will be left with fewer access options than they have today, widening existing hierarchies among deaf users.
Sign language AI trained on interpreted datasets will encode interpreter-generated signing as the standard, further sidelining regional varieties, Black ASL, queer signing, and idiosyncratic signing styles.
Without deaf leadership in research and development, 'co-creation' will remain performative and will not change the power relations that shape which access problems AI is built to solve.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension would be to track national interpreter-hour allocation and procurement data in several European countries for five years, comparing deaf consumers' use of human interpreting before and after speech-to-text AI is offered, to see whether substitution actually occurs at scale.
The slippery-slope logic suggests that once AI access is embedded as a default feature in mainstream platforms such as video conferencing and phone operating systems, it will be treated as a universal baseline, making it harder for deaf communities to demand interpreter-mediated access even when they prefer it; the paper gestures at this but does not develop the platform-defaults angle.
The same substitute-framing critique could apply to spoken-language interpreting for migrants and refugees, where interpreting is already treated as a temporary measure; a comparative study would test whether deaf and migrant language access are being eroded by the same AI-driven cost logic.
If deaf-led research becomes the norm, one would expect new evaluation metrics for sign language AI that measure alignment with deaf languaging practices rather than equivalence to human interpreters; the paper calls for deaf leadership but does not specify what such metrics would look like.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This paper makes a real contribution: it gives a name and a frame to something people in the field have been circling. The "slippery slope rights" concept is useful, and the application of crip technoscience to sign language AI is overdue. The critique of interpreter-centered benchmarks—training models on interpreted data and evaluating AI against human interpreters—is sharp and well-supported. The author knows the literature and the community, and it shows.

The paper is best when it argues that AI sign language tools should not be framed as substitutes for interpreters but as supports for deaf languaging. That argument stands on its own and is well made. The discussion of access labour, hierarchy, and the double bind of deaf NGOs is nuanced and genuinely useful.

The soft spot is exactly where the stress-test note lands: the claim that erosion of linguistic rights is "already underway" rests on anecdote. The passage about deaf consumers being directed to speech-to-text when interpreter hours run out has no countries, no dates, no citations. That single anecdotal bridge carries the urgency of the whole paper. If the real pattern is optional supplementation rather than imposed substitution, the "erosion" claim weakens. The author should either strengthen this with documented examples or reframe the contribution as a preemptive warning rather than a description of ongoing erosion.

Another, smaller issue: the paper generalizes from Western countries (Europe, Australia, NZ, Canada, US) to "deaf people around the world" at some points, even though the author is careful elsewhere to note the differences. This scope mismatch is fixable with a few careful qualifications.

These are not fatal. The conceptual argument holds up, and the paper is honest about the limits of the evidence in places—but not honest enough about how much weight the erosion claim carries. A serious revision should address that, not by adding hedge language but by either finding real evidence or explicitly lowering the claim.

Who is this for? Researchers in Deaf Studies, HCI, accessibility, and AI ethics. It would be a good reading group piece. I'd send it to review: the field needs this argument, and the author is well placed to make it. I'd also cite the slippery slope rights framing in my own work.

Referee Report

3 major / 5 minor

Summary. The paper is a conceptual and position article arguing that the development and deployment of AI sign language technologies are being shaped by an interpreter-centred ideology of access, which frames these technologies as substitutes for human interpreters rather than as tools supporting deaf languaging practices. Drawing on Deaf Studies, Sign Language Interpreting Studies, and crip technoscience, the author introduces the notion of 'slippery slope rights' to warn that incremental acceptance of AI tools in low-stakes contexts may expand into high-stakes settings, eroding hard-won linguistic and accessibility rights. The paper also critiques the use of interpreted datasets in training AI, the lack of deaf leadership in sign language AI research, and the performative nature of much co-creation work. It concludes by calling for deaf-led, human-in-command approaches to AI development.

Significance. If its central claim is accepted, the paper makes an important contribution by reframing sign language AI from a purely technical problem to a question of linguistic rights, access hierarchies, and collective autonomy. It brings crip technoscience into dialogue with sign language interpreting studies in a way that is largely missing from the current AI literature, and it gives explicit attention to the perspectives of deaf users rather than treating them as passive beneficiaries. The author is transparent about her own positionality and about the fact that some of her earlier work is cited by AI developers as motivation, which strengthens the credibility of the institutional critique. The paper also offers a concrete, useful warning about the risks of training sign language AI on interpreted data. At the same time, the empirical basis for the claim that erosion is already underway is thin and regionally narrow; the paper is best read as a well-argued interpretive essay calling for vigilance, rather than as an established empirical account of current policy trajectories.

major comments (3)

[The intersection of AI sign language technology development (paragraph beginning 'Anecdotal evidence')] The central urgency of the paper rests on the claim that 'the erosion of linguistic rights may already be underway' (Introduction) and that deaf consumers are 'increasingly directed to rely on speech-to-text AI' when interpreter hours are exhausted. This load-bearing premise is supported only by 'anecdotal evidence from some European countries,' with no countries, dates, organizations, or documentary sources given. If the paper is to make the strong claim that erosion is currently happening rather than merely possible, that claim needs systematic documentation or at least named, checkable cases. Alternatively, the paper should be reframed as a risk analysis: the warning about a plausible trajectory does not require current prevalence, but the present wording overstates the evidentiary base.
[Introduction and Discussion (the 'slippery slope rights' concept)] The concept of 'slippery slope rights' is introduced as a central analytical device but is not defined with sufficient precision, and the argument that low-stakes AI use will expand into high-stakes contexts is asserted rather than supported. The VRI example is a historical analogy showing that a technology initially rejected by deaf people can become normalized, but it does not demonstrate that AI sign language technologies are following the same path, nor does it specify the causal mechanism by which voluntary low-stakes use becomes imposed high-stakes use. The paper would be strengthened by an explicit account of the conditions under which this slippage occurs (e.g., funding pressures, legal obligations, interpreter shortages) and by evidence of at least one case where such a transition is already observable.
[Throughout, but especially the Introduction and Discussion and conclusion] The paper generalizes from a small set of Western countries—Europe, Australia, New Zealand, Canada, and the United States—to broad claims about 'deaf people' and 'linguistic rights' worldwide, including the statement that the shift 'could have devastating consequences' for 'the reality for most deaf people around the world.' The argument is explicitly built on the existence of institutionalized interpreting services and legal recognition frameworks, which are not present in most of the world. The paper should either restrict its empirical and predictive claims to the jurisdictions on which it draws, or add a substantive discussion of how the argument transfers to contexts without such legal protections. Without this scope clarification, the central warning risks overgeneralization.

minor comments (5)

[Keywords] The keyword 'lanugage' is a typo and should read 'language.'
[References] Several references contain spacing artifacts such as 'V andeghinste', 'Y oung', 'V an', and 'Resarch'; these should be corrected for consistency and professional presentation.
[Introduction / Discussion and conclusion] The opening vignette describes a deaf person voluntarily choosing a speech-to-text app in a shop and a court hearing where an AI translation is imposed, yet later the paper states that current AI sign language technologies are mainly targeted at low-stakes contexts. The vignette's court example appears to depict current practice, so the text should clarify whether it is a hypothetical future scenario or a documented case.
[Setting the stage] The description of the SignGPT project states that all Principal Investigators are hearing academics; this is an empirical claim about individuals and should be supported by a source or softened to avoid overstatement.
[Discussion and conclusion] The reference to the EU Accessibility Act 'set to take effect in June 2025' would benefit from a more precise legal citation or a note on the transposition deadline, as the practical effect in member states depends on national implementation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an interpretive argument that is self-contained; self-citations are contextual and not load-bearing.

full rationale

This paper does not present equations, fitted parameters, formal derivations, or quantitative predictions, so the standard circularity patterns that reduce a result to its own inputs do not apply. The central claim—that framing AI sign language technologies as substitutes for human interpreters creates a problematic benchmark and risks eroding linguistic rights—is developed through Deaf Studies, crip technoscience, policy discussion, and the author's qualitative analysis of deaf community experiences. The author's prior work (De Meulder, 2021; De Meulder & Haualand, 2021) is cited mainly to acknowledge earlier thinking and to describe how other researchers have used that work; it is not used as the sole justification for the paper's conclusions. The closest evidentiary weakness is the paper's own acknowledgment that current erosion is supported only by 'anecdotal evidence from some European countries,' but this is a limitation in evidence quality, not a circular derivation: the claim is not redefined as true by construction, nor is it derived from the author's prior publications as premises. No uniqueness theorem, ansatz, or renamed empirical result is imported from the author's own prior work to force the conclusion. Therefore the derivation chain, such as it is in an interpretive essay, is independent of its inputs and the paper is not circular.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper's contributions are interpretive, so the ledger lists the domain assumptions it imports from crip technoscience, Deaf Studies, and interpreting studies. It introduces one new conceptual entity, 'slippery slope rights.' No numerical free parameters are involved.

assumptions (4)

domain assumption Access should be understood as collective and interdependent, not merely individual accommodation.
The paper adopts crip technoscience's framing (citing Hamraie and Fritsch; Bennett et al.) in Section 'Access and crip technoscience' to argue that AI tools cannot simply replace interpreters without redistributing access and creating hierarchies.
domain assumption Sign language interpreting is the dominant and institutionalized access model in the countries discussed.
Section 'The intersection of AI sign language technology development...' states that 'the availability of sign language interpreters is often equated with access.' This premise frames the entire analysis.
domain assumption Interpreted language data are not equivalent to natural sign language use and will skew AI systems.
The paper asserts that interpreted datasets contain scripted, interpreter-influenced signing, citing Desai et al. (2024) and Fox et al. (2023) in Section 3. This is an imported empirical claim from prior work.
domain assumption Deaf-led development of AI would produce more equitable outcomes than hearing-led development.
The paper's call for deaf-led research (Discussion and conclusion) presumes that leadership composition materially changes design outcomes, an untested assumption though consistent with participatory design literature.

invented entities (1)

Slippery slope rights
purpose: A concept that describes how access measures initially rejected by deaf communities become normalized and accepted over time, used to argue that AI adoption may erode rights gradually.
Coined by the author in the Introduction ('what I call'), it is a framing device with illustrative examples such as video remote interpreting. It is not an independently validated empirical construct, but it captures a documented community concern.

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Cite this review

Pith. "Pith review of Deaf in AI: AI language technologies and the erosion of linguistic rights." pith.science (2026). https://pith.science/paper/243JAR37

@misc{pith2026250502519,
  author       = {Pith},
  title        = {Pith review of: Deaf in AI: AI language technologies and the erosion of linguistic rights},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/243JAR37}},
  note         = {Machine review of arXiv:2505.02519}
}

read the original abstract

This paper explores the interplay of AI language technologies, sign language interpreting, and linguistic access, highlighting the complex interdependencies shaping access frameworks and the tradeoffs these technologies bring. While AI tools promise innovation, they also perpetuate biases, reinforce technoableism, and deepen inequalities through systemic and design flaws. The historical and contemporary privileging of sign language interpreting as the dominant access model, and the broader inclusion ideologies it reflects, shape AIs development and deployment, often sidelining deaf languaging practices and introducing new forms of linguistic subordination to technology. Drawing on Deaf Studies, Sign Language Interpreting Studies, and crip technoscience, this paper critiques the framing of AI as a substitute for interpreters and examines its implications for access hierarchies. It calls for deaf-led approaches to foster AI systems that remain equitable, inclusive, and trustworthy, supporting rather than undermining linguistic autonomy and contributing to deaf aligned futures.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 16 canonical work pages

[1]

AI”, they said, “might finally free us from this system. Maybe in the not-so-distant future, we won’t need human interpreters anymore

1 Deaf in AI: AI language technologies and the erosion of linguistic rights Maartje De Meulder1 HU University of Applied Sciences, Utrecht Abstract This paper explores the interplay of AI language technologies, sign language interpreting, and linguistic access, highlighting the complex interdependencies shaping access frameworks and the trade-offs these t...

work page 2024
[2]

https://doi.org/10.3390/soc9010002 Skaaden, H., & Wadensjö, C. (2014). Some considerations on the testing of interpreting skills. In C. Giambruno (Ed.), Assessing Legal Interpreter Quality through Testing and Certification: the Qualitas project (17-26). Sant Vincent del Raspeig: Alicante Publications. Sloane, M., Moss, E., Awomolo, O., Forlano, L. (2020)....

work page doi:10.3390/soc9010002 2014
[4]

"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

https://doi.org/10.1007/s10676-024-09775-5 Hill, J. (2013). Language ideologies, policies, and attitudes towards signed languages. In R. Bayley, R. Cameron, & C. Lucas (Eds.), The Oxford Handbook of Sociolinguistics (pp. 680-697). Oxford University Press. Hill, J., Isakson, S. K., Nakahara, C. (2022). Infusing Social Justice in Interpreting Education. In ...

work page Pith review arXiv 2013
[5]

bound together in mutual systems of precarious interdependence

and as oppressive actors and 8 gatekeepers unaware of their own power and privilege (Robinson, Sheneman, & Henner, 2020). For a large part, this also has to do with how they are being trained (Sheneman & Robinson, 2021; De Meulder & Stone, 2024). Ye t, in Deaf Studies and Sign Language Interpreting Studies, the relationship between deaf people and sign la...

work page 2022
[8]

participation washing

or are perceived as “participation washing” (Sloane et al., 2020), referring to the superficial or performative involvement of participants in these processes. In large research consortia focused on AI and people with disabilities, I often find myself to be the only disabled researcher in the room. There is a growing acknowledgment of this under-represent...

work page 2020
[9]

higher-stakes

and hospitality (Leeson et al., 2024), developers are also pursuing “higher-stakes” areas like healthcare (Esselink et al.,

work page 2024
[10]

slippery slope

and emergency services (Guo et al., 2023; Martin et al., 2013). This shift, if not carefully managed, may exacerbate existing inequalities and undermine trust in these AI systems. These concerns do not come from thin air, considering the cost-effective approach often pursued by governments to address linguistic diversity and the “slippery slope” of access...

arXiv 2021
[11]

good enough

De Coster, M., Shterionov, D., V an Herreweghe, M., Dambre, J. (2023). Machine translation from signed to spoken languages: state of the art and challenges. Universal Access in the Information Society, 23, 1305–1331. De Meulder, M. (2021). Is “good enough” good enough? Ethical and responsible development of sign language technologies. In Proceedings of th...

work page arXiv 2023

Show all 19 references

[12]

https://doi.org/10.1515/multi-2023-0203 Erdocia, I., Migge, B., Schneider, B. (2024). Language is not a data set—Why overcoming ideologies of dataism is more important than ever in the age of AI. Journal of Sociolinguistics,

2024 doi
[13]

https://doi.org/10.1111/josl.12680 Esselink, L., Roelofsen, F., Dotlacil, J., Mende-Gillings, S., De Meulder, M., Sijm, N., Smeijers, A. (2023). Exploring automatic text-to-sign translation in a healthcare setting. Universal Access in the Information Society. https://doi.org/1...

2023
[15]

https://doi.org/10.1111/josl.12678 Kushalnagar, P ., Paludneviciene, R., Kushalnagar, R. (2019). Video Remote Interpreting Technology in Health Care: Cross-Sectional Study of Deaf Patients’ Experiences. JMIR Rehabil Assist Technol, 6(1), 1–8. Kusters, A., Green, M., Moriarty, ...

2019 doi
[16]

https://doi.org/10.70484/vakki.145616 Marie, A. (2019). Enacting Dependence. Somatosphere, February

2019 doi
[19]

It's not what they say but the way they say it

https://somatosphere.com/2019/enacting-dependence.html/ Marie, A., Friedner, M. (2021). Entangled Interdependence: Sign Language Interpreting without Recognition in India and Vietnam. PoLAR: Political and Legal Anthropology Review, 44(2), 192–206. https://doi.org/10.1111/plar....

2021
[119]

Stone, C. (2010). Access all areas – Sign language interpreting, is it that special? The Journal of Specialized Translation, 14:41-54. Stone, C., Adam, R., Müller de Quadros, R., & Rathmann, C. (2022). (eds.) The Routledge Handbook of Sign Language Translation and Interpreting...

2010
[2019]

the same conditions enabling access for some can create hierarchies of belonging for others

offers a critical lens to understand the complex, often ambiguous relationship between disabled people and technology. This may mean disabled people adopt imperfect, uncomfortable, or less functional tools, while simultaneously critiquing their limitations (notice the parallel...

2024
[2021]

in the wild

is that the primary bottleneck for developing AI sign language technologies is and remains the scarcity and quality of available data. Here again, sign language interpreting plays a significant role. AI systems are inherently data-hungry and due to the lack of data “in the wil...

2024
[2022]

requires substantial time and resources. Virtually all sign language interpreter training programs struggle with teaching sign language acquisition and the acquisition of subject-specific knowledge within a three or four-year (BA or MA) program (Webb, Napier, & Adam, 2025). As...

2025
[2023]

Co-creation

have helped some individual researchers improve their practices, the field continues to face systemic biases. These include an overfocus on addressing perceived communication barriers, a lack of use of representative datasets, reliance on annotations lacking linguistic foundat...

2023
[2024]

if we could do without interpreters, we would

“if we could do without interpreters, we would” or saying out loud that AI could be a replacement for or even serve as a tentatively acceptable addition to human interpreters. They knew very well this could trigger governments and institutions to abandon interpreter services a...

2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

AI”, they said, “might finally free us from this system. Maybe in the not-so-distant future, we won’t need human interpreters anymore

1 Deaf in AI: AI language technologies and the erosion of linguistic rights Maartje De Meulder1 HU University of Applied Sciences, Utrecht Abstract This paper explores the interplay of AI language technologies, sign language interpreting, and linguistic access, highlighting the complex interdependencies shaping access frameworks and the trade-offs these t...

work page 2024

[2] [2]

https://doi.org/10.3390/soc9010002 Skaaden, H., & Wadensjö, C. (2014). Some considerations on the testing of interpreting skills. In C. Giambruno (Ed.), Assessing Legal Interpreter Quality through Testing and Certification: the Qualitas project (17-26). Sant Vincent del Raspeig: Alicante Publications. Sloane, M., Moss, E., Awomolo, O., Forlano, L. (2020)....

work page doi:10.3390/soc9010002 2014

[3] [4]

"We do use it, but not how hearing people think": How the Deaf and Hard of Hearing Community Uses Large Language Model Tools

https://doi.org/10.1007/s10676-024-09775-5 Hill, J. (2013). Language ideologies, policies, and attitudes towards signed languages. In R. Bayley, R. Cameron, & C. Lucas (Eds.), The Oxford Handbook of Sociolinguistics (pp. 680-697). Oxford University Press. Hill, J., Isakson, S. K., Nakahara, C. (2022). Infusing Social Justice in Interpreting Education. In ...

work page Pith review arXiv 2013

[4] [5]

bound together in mutual systems of precarious interdependence

and as oppressive actors and 8 gatekeepers unaware of their own power and privilege (Robinson, Sheneman, & Henner, 2020). For a large part, this also has to do with how they are being trained (Sheneman & Robinson, 2021; De Meulder & Stone, 2024). Ye t, in Deaf Studies and Sign Language Interpreting Studies, the relationship between deaf people and sign la...

work page 2022

[5] [8]

participation washing

or are perceived as “participation washing” (Sloane et al., 2020), referring to the superficial or performative involvement of participants in these processes. In large research consortia focused on AI and people with disabilities, I often find myself to be the only disabled researcher in the room. There is a growing acknowledgment of this under-represent...

work page 2020

[6] [9]

higher-stakes

and hospitality (Leeson et al., 2024), developers are also pursuing “higher-stakes” areas like healthcare (Esselink et al.,

work page 2024

[7] [10]

slippery slope

and emergency services (Guo et al., 2023; Martin et al., 2013). This shift, if not carefully managed, may exacerbate existing inequalities and undermine trust in these AI systems. These concerns do not come from thin air, considering the cost-effective approach often pursued by governments to address linguistic diversity and the “slippery slope” of access...

arXiv 2021

[8] [11]

good enough

De Coster, M., Shterionov, D., V an Herreweghe, M., Dambre, J. (2023). Machine translation from signed to spoken languages: state of the art and challenges. Universal Access in the Information Society, 23, 1305–1331. De Meulder, M. (2021). Is “good enough” good enough? Ethical and responsible development of sign language technologies. In Proceedings of th...

work page arXiv 2023