Authenticity in Question: Navigating Qualitative Interviewing in the Era of Artificial Intelligence and Deepfakes

Abstract

As artificial intelligence (AI)-generated personas and deepfakes become increasingly sophisticated, online qualitative research faces urgent challenges to trust and authenticity. This commentary explores how hyperreal digital deception intersects with core qualitative commitments to care, access, and participant co-authorship. Drawing on crip, feminist, and decolonial methodologies, we critique the limitations of verification practices – such as mandatory video or ID checks – that often reinforce ableist, racialized, and classed norms of believability. Rather than turning to surveillance, we propose relational ethics and practical strategies to navigate AI deception with discernment. We argue for new frameworks that uphold research integrity while protecting participant dignity in the digital age.

Keywords

deepfakes and artificial intelligence ethics and authenticity online qualitative research qualitative interviewing relational ethics

Introduction: reimagining qualitative research in the age of artificial intelligence

Artificial intelligence (AI) today is both a source of moral panic and a catalyst for generative, transformative change. Public discourse oscillates between alarmist warnings of AI-fueled deception and optimistic visions of AI-driven innovation. On one hand, sensational narratives about hyperreal impersonations stoke fears that we may no longer trust what we see or hear; on the other, breakthroughs in generative AI are spawning creative applications that redefine everyday practices. For example, some people are outsourcing their job interviews to AI avatars or using voice cloning tools to impersonate others (Fellows, 2025). As AI becomes more sophisticated and accessible, these hyperreal performances are no longer just science-fiction – they are reshaping the everyday. Academia is no exception. The scholarly community finds itself grappling with AI on multiple fronts, from research and ethics to pedagogy and administration, often on the back foot as it struggles to adapt to AI’s rapid evolution.

One arena where AI’s dual impact is especially evident is deepfake technology. In simple terms, a deepfake is a hyper-realistic fake media, typically videos, images, or audio, that appears authentic but is actually generated or manipulated by deep learning algorithms (Alanazi and Asif, 2024; Altuncu et al., 2024). The term ‘deepfake’ is a portmanteau of ‘deep learning’ and ‘fake’, reflecting that these forgeries are made using advanced AI to deeply mimic real people or events (Altuncu et al., 2024). In such context, how do we know our participants are who they say they are? What if an AI bot, meaning an AI-powered program designed to imitate human behavior in interactions, is standing in for a person? And how do we respond with care rather than suspicion, especially when working with marginalized communities who already experience surveillance and doubt? As many researchers turn to online methods for their accessibility and affordability (Locke et al., 2018; Nicholas et al., 2018; Santinele Martino et al., 2024), the risks of digital impersonation grow. But so too does the risk of undermining trust and inclusivity in the name of verification (Drysdale et al., 2023; Pellicano et al., 2024; Santinele Martino et al., 2024). Participants might fear their contributions will not be believed – a sort of byproduct of the ‘liar’s dividend’, where real evidence is dismissed as fake in a deepfake-plagued environment (Birrer and Just, 2025). This commentary grapples with this methodological and ethical conundrum: how can we uphold the integrity of qualitative research in an age where deception can be digitally manufactured? Qualitative inquiry has long resisted the idea of a single, objective truth, advancing instead ‘situated’, partial, and relational accounts of the world (Haraway, 1988; Lincoln and Guba, 1985). The field’s legitimacy has been hard-won precisely because its claims to authenticity rest on reflexivity, thick description, and negotiated meanings rather than positivist verification (Denzin and Lincoln, 2017; Lincoln and Guba, 1985). Hyperreal AI now presses on these commitments: deepfakes force us to re-engage questions of evidence and truth without retreating from the epistemological gains of qualitative work.

The rise of synthetic participants: examples and emerging risks

It has become possible for AI bots to answer dating app messages, simulate therapist sessions, or conduct interviews (Boyle, 2025; Hoover, 2024; Sircar, 2024). Researchers have even tested chatbots as social science research interviewers (Young, 2024). These are not isolated anomalies but growing trends reflecting how people navigate digital spaces – often to optimize performance or avoid vulnerability. One particularly striking trend is the rise of deepfakes – realistic videos, images, or audio recordings created using AI (Karnouskos, 2020). Deepfakes may replicate a real person’s appearance, voice, or movements with such accuracy that they can be mistaken for authentic people (Westerlund, 2019). The increasing sophistication of these synthetic media blurs the line between reality and fabrication, raising new risks for how we assess identity, authenticity, and trust in online interactions.

Deepfakes are notoriously difficult to detect, even for experts. Studies show human ability in spotting deepfake videos is often poor (Roe et al., 2024). In one 2023 study, 27%–50% of participants could not identify that a video was a deepfake (Doss et al., 2023). In the context of qualitative research, identifying deepfakes can be even more complex due to the inherently open and trust-based nature of interviews and focus groups. Researchers should be particularly mindful of participants who present overly perfect speech patterns, lack natural pauses or hesitations, or demonstrate facial movements that appear too smooth or robotic. Moreover, when responses seem overly consistent with AI-generated text – lacking personal anecdotes or emotional nuance – it may indicate the use of synthetic responses. We recommend resources, such as MIT Media Lab (2025) and Northwestern Kellogg (2025), for scholars wanting to learn more about deepfakes.

In qualitative research, this trend could manifest in participants using AI-generated responses in interviews, feeding transcripts into generative tools, or even submitting AI-generated narratives for creative qualitative methods (Burleigh and Wilson, 2024; Drysdale et al., 2023; Gibson and Beattie, 2024). While the motivations may vary – from performance anxiety to language barriers or a desire to present oneself in a particular way (Burleigh and Wilson, 2024; Garib and Coffelt, 2024; Lehtimäki, 2024) – the implications are profound. What does it mean to collect ‘data’ when those very data may be synthetic?

While some aspects of this discussion necessarily anticipate future developments, given the rapid pace of AI innovation, there is an emerging empirical basis for these concerns across different domains of online interaction. Researchers have begun to report subtle red flags during online interviews: participants with unusually perfect speech patterns, facial expressions that seem ‘off’, or inconsistent answers that align too closely with AI-generated text (Ridge et al., 2023; Roehl and Harland, 2022; Santinele Martino et al., 2024). These have been labeled ‘imposter participants’, individuals who misrepresent their identity, experiences, or eligibility to take part in a research study, often in pursuit of the study’s financial compensation (Santinele Martino et al., 2024; Sharma et al., 2024), though some may also do so out of personal curiosity, malicious intent, or even to test and exploit research systems using AI-generated content or synthetic personas (Ridge et al., 2023; Santinele Martino et al., 2024). The form, prevalence, and meaning of such deceived practices are likely to vary across socio-cultural contexts, shaped by local norms, technology access, and research infrastructures. As authors located in Canada, we recognize that our own analysis is shaped by this context. Further cross-cultural study is essential to understand how synthetic participation manifests in other settings.

At the same time, we must be cautious not to frame these concerns as a call for stricter surveillance – particularly when working with disabled, trans, or undocumented individuals who may already feel under surveillance in their everyday lives (Pellicano et al., 2024; Santinele Martino et al., 2024). The challenge is to navigate this space without replicating the very power dynamics and inequalities that qualitative research often seeks to critique.

Online qualitative research is here to stay

Despite these concerns, abandoning online qualitative research would be a mistake. Digital methods have expanded access for many: graduate students, disabled researchers, caregivers, COVID-conscious people, scholars working across geographic borders, and those with limited funding (Lobe et al., 2020; Palys and Atchison, 2012; Paulus and Lester, 2021; Santinele Martino et al., 2024). Online interviews and focus groups allow us to include voices that might otherwise be excluded due to travel restrictions, financial limitations, time constraints, or physical access barriers (Keen et al., 2022; Moore et al., 2015). They create opportunities for participants to join from familiar and safe environments, reducing anxiety and enhancing openness (Reisner et al., 2018). This may be especially true for disabled people or those navigating mental health challenges who may find traditional in-person formats difficult to access (Pellicano et al., 2024; Santinele Martino et al., 2024). For many researchers, online methods are not just convenient, they are essential. Graduate students often lack institutional support or funding to conduct in-person fieldwork across multiple sites (Santinele Martino et al., 2024). Community-based researchers, particularly those working outside major urban centers, rely on digital tools to reach participants where they are (Higgins and Metzler, 2001). Scholars conducting research with diasporic, rural, or international populations often depend on online communication to facilitate meaningful dialogue (Parham, 2004).

Moreover, online methods can offer increased safety and flexibility for participants discussing sensitive topics, such as sexuality, disability, migration, or trauma (Palys and Atchison, 2012; Paulus and Lester, 2021; Pellicano et al., 2024; Santinele Martino et al., 2024). In these cases, anonymity or the ability to choose a pseudonym, blur a camera, or use voice-only options can foster greater participation (Kent, 2022; Lo Iacono et al., 2016). To lose these benefits in the name of verification could be a significant step backward for qualitative inquiry. Thus, rather than abandoning digital qualitative methods due to fears of deception, we must evolve our approaches to accountability, trust, and ethics in these settings. It is a deeper call to reimagine what we mean by ‘authenticity’ in research and how that authenticity can be situated in relationships rather than surveillance.

The limits of current safeguards

As concerns about AI-enabled impersonation grow, some researchers and institutions have introduced safeguards to verify participant identity – such as requiring cameras to stay on, cross-checking consent forms with ID, or conducting screening calls (Sharma et al., 2024). While these tactics may offer reassurance, they are somewhat limited – and at times incompatible – with the ethics of qualitative research (Santinele Martino et al., 2024).

Take, for example, the increasingly common requirement for participants to appear on camera during interviews. Although intended to confirm presence and personhood, it raises serious accessibility and privacy concerns (Schofield and Joinson, 2008). For some participants – such as disabled, trans, undocumented, neurodivergent, impoverished, or experiencing housing insecurity, video may not feel safe or comfortable – or even be possible (Flaherty and Sadler, 2023; Pellicano et al., 2024; Santinele Martino et al., 2024). It may also trigger trauma or reinforce feelings of exposure, particularly in research about stigmatized experiences, such as sex work, migration, or mental health (Flaherty and Sadler, 2023; Havell, 2024). Ironically, the openness enabled by digital formats can be undermined by visual demands that compromise participant comfort and authenticity.

Beyond access, ‘camera-on’ policies can reflect ableist, classed, and white normative assumptions about participation (Melling, 2025; Parent, 2018). They imply that authenticity is visible, legible, and performative (Santinele Martino et al., 2024), overlooking the fact that lived experience, and indeed qualitative richness, often defies easy representation. Requiring people to ‘prove’ their realness via embodiment risks excluding those whose realities do not conform to mainstream visual cues of identity, health, or normativity (Alcoff, 2005; Melling, 2025). Other tactics, such as asking for identity verification or conducting screening interviews, can, in some cases, edge into dangerous territory. These methods can deter participation, especially from those who fear surveillance or systemic retaliation – immigrants, sex workers, or survivors of state violence, for example (Flaherty and Sadler, 2023; Havell, 2024; Pascale et al., 2022). The core values of qualitative research – trust, rapport, care – cannot be easily reconciled with a hyper-focus on credentialing participation (Evans, 2014; Stahl and King, 2020). In fact, such practices may replicate the very dynamics of exclusion and suspicion that many critical qualitative scholars aim to resist (Evans, 2014; Pascale et al., 2022).

Compounding the issue, AI can now bypass many of these safeguards. Deepfake video tools replicate facial movements in real time; voice synthesis mimics natural speech (Karnouskos, 2020; Westerlund, 2019). A participant may appear and speak ‘authentically’ on camera and still not be real in the way we expect. This challenges the assumption that the visual or audible self is inherently verifiable. If traditional methods of verification are both unreliable and potentially harmful, what alternatives exist? How can we assess authenticity without defaulting to surveillance? And how can we ensure ethical practice that protects both data integrity and participant dignity? These are not merely methodological questions. They are also ethical and relational. They call for a shift away from policing and toward relational accountability, emphasizing trust and reciprocity over proof.

Ethics, power, and the problem of policing identity

If qualitative research is grounded in relationships, reflexivity, and care, what happens when we begin to police participant authenticity? Deepfake technologies raise legitimate concerns, but verifying identity through rigid protocols can replicate systems of surveillance, exclusion, and mistrust, especially for those already subject to skepticism in academic, medical, and institutional contexts. Questioning whether someone is ‘real’ can quickly become questioning whether they are ‘valid’. Critical scholars across feminist, critical race, and Indigenous methodologies argue that the concept of ‘validity’ in research is not neutral. Rather, it often encodes assumptions about whose knowledge is credible or valuable. In other words, traditional validity criteria may inadvertently privilege the worldviews of dominant groups (e.g. Western, white, cisgender, male) and cast doubt on knowledge from marginalized standpoints (Beagan et al., 2024; Maddox and Morton Ninomiya, 2025). This is especially dangerous when working with people whose truths are routinely dismissed, and whose bodies and voices are marked as ‘suspect’ or ‘not quite believable’ (Santinele Martino et al., 2024). In these contexts, the demand to verify identity may function less as a safeguard and more as epistemic violence (Santinele Martino et al., 2024).

These stakes are heightened in our field of critical disability studies, where disabled people have been historically silenced, rendered unintelligible, or required to ‘prove’ their realities to skeptical institutions (Price, 2011; Titchkosky, 2007). Researchers warn that treating a participant’s self-reported identity or experience as ‘unbelievable’ can enact epistemic violence – a harm done to someone in their capacity as a knower. Epistemic injustice occurs when societal biases lead us to unfairly doubt or discount a person’s credibility (Carel and Kidd, 2014; Fricker, 2007). Thus, while AI deception is a real methodological risk, responses that default to credentialing or heightened visibility risk re-inscribing precisely the exclusions qualitative research has labored to undo.

These concerns are not theoretical. Disabled people with invisible disabilities are frequently asked to ‘prove’ their needs – to doctors, social workers, professors, and peers (Abney et al., 2022). Trans and non-binary participants regularly have their identities interrogated under the guise of ‘accuracy’ (Fiani and Han, 2020; Garrison, 2018). Refugees must recount trauma in granular detail to establish ‘credibility’ (Ali, 2024; Small, 2024). In all these cases, verification reinforces structural inequality by granting authority figures the power to decide whose experiences are ‘legible’ or ‘valid’ (Caretta and Pérez, 2019; Cooke and Kothari, 2001). We must ask: Are we reproducing this logic in our own work? Over-correcting for AI-generated deception through strict protocols risks turning qualitative research from a space of meaning-making into one of compliance – a fundamental departure from our commitments to participant-led inquiry and ethical reflexivity.

This does not mean ignoring risks. It means addressing them differently – through relational accountability, not institutional mistrust. This involves ongoing dialogue, transparency, and mutual trust over rigid eligibility criteria. It means fostering conditions in which participants feel invested and seen, not just scrutinized. We should ask: Why equate ‘authenticity’ with fixed identity? In many qualitative traditions – feminist, Indigenous, crip, Black, queer – the very notion of identity is fluid, contingent, and performed. Rather than demanding proof of ‘realness’, we might ask whether the knowledge shared is meaningful, situated, and co-created in relationship.

This reframing allows us to approach AI interference thoughtfully. If we suspect a participant has used generative AI to help answer questions, we might ask: What does that reveal about the pressures they feel to perform expertise? About their access to language or perceived expectations of research? Rather than dismissing such contributions, we can analyze them as reflections of everyday human-AI entanglements.

Of course, deception can sometimes undermine a project’s goals (Harding et al., 2024; Stafford et al., 2024). But even then, our responses should be rooted in care and transparency (Gibson and Beattie, 2024). When something feels ‘off’, researchers can pause data collection, consult advisory boards, or reflect in memos – without jumping to punitive conclusions (Gibson and Beattie, 2024; Santinele Martino et al., 2024; Wang, 2026). This is particularly important in projects involving vulnerable groups where harm can come not just from being excluded but also from being disbelieved (Santinele Martino et al., 2024).

Research ethics boards must evolve too. Most frameworks treat deception as a binary – a threat to be eliminated. Yet in the AI era, deception is increasingly complex, distributed, and entangled in structural pressures. Ethics protocols should shift from surveillance toward collaborative, justice-informed approaches that protect research integrity while respecting participant agency. We must resist letting fear drive our methods. Instead, we can return to the heart of qualitative inquiry: listening, relationship-building, and recognizing that truth is not always visible – but always relational.

‘Crip’ methodological interventions

Rather than viewing the threat of AI-generated deception as an exceptional crisis, we might instead turn to fields that have long dealt with the instability of identity, the politics of representation, and the ethics of uncertainty. ‘Crip’ methodologies offer powerful tools for rethinking the terms of participation, trust, and data authenticity – not by demanding proof, but by centering care, access, and situated knowledge (Dronkert, 2023; Heilig and Sandell Hardesty, 2024).

Crip methodologies, grounded in lived experience of disability, challenge normative expectations of communication, embodiment, and temporality (Bennett, 2022; Frese, 2022). Within disability studies and critical access work, researchers have consistently rejected rigid models of what counts as legitimate knowledge. For instance, slowness, silence, echolalia, or mediated speech are not treated as failures to communicate but as meaningful in themselves (Friedner et al., 2024; Donnellan et al., 2013). A crip approach to online qualitative research would, we argue, resist calls for ‘proof’ of personhood or participation, and instead foreground access intimacy, consent-as-process, and relational validity (Furlong, 2023; Price and Kerschbaum, 2016). It would ask: how can we create conditions that invite genuine participation, rather than conditions that filter it out?

Crip practices emphasize relationships, reflexivity, and co-construction of knowledge, offering researchers practical tools to navigate increasingly complex online research environments. One such practice is relational onboarding. This approach reframes the early stages of participant engagement not as a gatekeeping mechanism or screening tool, but as an opportunity to build trust and mutual understanding. In the digital era, where participants might misrepresent themselves, building a real relationship is crucial (Roehl and Harland, 2022; Santinele Martino et al., 2024). Initial conversations are treated as relational groundwork rather than eligibility assessments. These conversations – whether held over email, video call, or asynchronous messaging – allow participants to get a sense of the researcher’s ethics and intentions, while researchers begin to understand the participant’s communication style, values, and broader context. In a digital landscape where identity cannot always be easily verified, the strength of the researcher-participant relationship itself can become a form of verification, grounded in mutual recognition and growing familiarity over time.

Another foundational practice involves the use of storytelling and narrative-based methods (Bruce et al., 2016; Roller and Lavrakas, 2015). These approaches prioritize lived experience over externally validated credentials or static identity categories (Pino Gavidia and Adu, 2022). In doing so, they enable richer, more textured data collection (Scheffelaar et al., 2021) that can also make AI-generated or synthetic responses more noticeable. Scholars have noted that dishonest or synthetic responses tend to be more superficial or inconsistent when compared to authentic narratives (Roehl and Harland, 2022; Santinele Martino et al., 2024). Narratives that unfold over time; incorporate emotion, contradiction, or complexity; and reveal the idiosyncrasies of human memory and reflection tend to be difficult for generative AI to convincingly replicate (Kabashkin et al., 2025). Thus, narrative methods can offer an organic way of distinguishing between human and non-human contributors without requiring direct confrontation or disclosure. Moreover, they honor the kinds of knowledge that emerge through life stories rather than checkboxes.

We would caution, however, against using storytelling as a tool for detection and policing. Such a framing risks shifting the focus away from care, curiosity, and meaning-making toward surveillance and suspicion. It also reproduces dynamics of gatekeeping that have long excluded marginalized voices from research, particularly those whose ways of communicating may already fall outside normative expectations. Instead, we advocate for narrative methods as a way to invite richness, relationality, and mutual vulnerability into the research encounter. When approached with humility and openness, storytelling becomes less about verifying authenticity and more about deepening understanding – of participants’ lived realities, of the social worlds they inhabit, and of the complexities that shape their identities. In an age where digital environments can blur the lines between real and artificial, returning to narrative does not promise certainty, but it does offer depth, texture, and a humanizing lens that resists flattening people into data points.

Participant-checked analysis represents another critical safeguard. By returning preliminary interpretations and thematic insights to participants for feedback, researchers can ensure that their findings resonate with those whose voices are being represented (Birt et al., 2016). This step not only supports ethical and collaborative practice but can also serve as a useful mechanism for identifying inconsistencies or red flags in synthetic data. Participants are often well-positioned to clarify their own meaning, point out where the researcher has misunderstood, or respond to how their words are being used. When this process is not possible or produces ambiguous results, it can serve as an important cue for deeper inquiry or methodological reflection.

Questions for the field

As AI-generated identities and hyperreal personas proliferate, qualitative researchers must not only confront these new realities but also lead in modeling thoughtful, reflexive, and relational responses. The future of online qualitative research will not be safeguarded through more surveillance or institutional gatekeeping. Instead, it will be strengthened by deepened trust, methodological transparency, and community accountability. We must consider how ethics, training, and publishing standards are evolving. This moment calls not for reactionary skepticism but for imaginative, ethically grounded adaptations to how we conceptualize identity, authenticity, and relationality in research.

One urgent shift involves reimagining identity verification not as a transactional checkpoint but as a relational, iterative process. Traditional models of verification – asking for documents, screening participants through static demographic questions, or treating verification as a hurdle to clear at the start – no longer suffice. In digital contexts where identity may be fluid, anonymized, or even algorithmically generated, researchers must adopt slower, more layered approaches. Building relational trust over time through ongoing dialogue, multiple points of contact, participant-led journaling, or longitudinal engagement can help researchers make sense of who is speaking and how their narratives develop across interactions. This kind of processual verification resists the logic of extraction and centers connection and context instead. However, these slower, trust-centered approaches can be at odds with funding regimes that impose tight timelines and deliverable-driven milestones. Recognizing this tension highlights the need to advocate for research designs and grant structures that value relationship-building – notably, a keystone of decolonial research methodologies (Smith, 2021) – as a legitimate and necessary use of research time.

At the same time, ethics boards must update their frameworks to reflect that ambiguity is not a flaw but a condition of contemporary research. Rather than policing the boundaries between ‘real’ and ‘fake’, ethics protocols should acknowledge the inevitability of uncertainty in online spaces and support researchers in preparing for it. This means empowering researchers to anticipate how they might respond to moments of doubt – when a participant’s story shifts, or when signs of AI-generated content emerge – without defaulting to removal or invalidation. Instead of punishing ambiguity, we need ethics processes that ask: How will you stay present with complexity? What strategies will you use to stay in relationship with your data and participants when things feel unstable?

To do this well, the field must also invest in meaningful methodological training focused on AI and digital life. It is no longer enough to understand how to conduct interviews or analyze transcripts – researchers must also be equipped to recognize, interpret, and ethically respond to the presence of AI-generated content. This requires not only technical literacy (e.g. how AI tools function, how generative models are trained) but also critical reflection on the social and political contexts in which these tools are being used. Workshops, graduate courses, and continuing education initiatives should encourage researchers to grapple with questions like: What does it mean to interview an avatar or quote synthetic text? We need detection skills and ethical imagination.

These stakes are not only epistemic; they are material and institutional. Publicly funded grants, high-profile awards, and impact metrics confer symbolic and career capital on researchers and institutions (Shore and Wright, 2015). When AI-enabled deception contaminates data, the costs include misallocated public funds, distorted evidence bases that travel into policy and practice, and erosion of public trust in research (Park et al., 2024). Attending to authenticity is therefore a matter of research integrity and stewardship, not only methodology: we owe participants, publics, and funders procedures that protect both rigor and inclusion.

Finally, to navigate these shifts with integrity, we must center the lived experiences and knowledges of those most affected by digital surveillance, algorithmic bias, and representational harm. Community and diversity advisory boards – especially those composed of disabled, racialized, queer, and neurodivergent participants – are essential partners in this work (Williams et al., 2023). These groups can help researchers and institutions interpret identity in more nuanced ways, drawing from their own encounters with being misrecognized, misrepresented, or deemed ‘inauthentic’ (Hearn et al., 2022; Williams et al., 2023). Their input can inform everything from how protocols are designed to how findings are framed, ensuring that our methods reflect the realities and concerns of those we aim to represent (Hearn et al., 2022). Moreover, involving community collaborators in reviewing emerging cases of AI deception can model collective ethical response rather than unilateral decision-making by researchers or institutions.

Instead of asking, ‘Is this participant real?’, we might ask:

Is the knowledge shared meaningful and contextually grounded?

Are the relationships we are building accountable and ethical?

Are our practices inclusive of those who do not or cannot conform to normative expectations of presence?

In sum, qualitative researchers must respond to AI not solely by retreating into more stringent verification or by erecting new barriers to participation, but by embracing a richer, more flexible, and justice-oriented practice. This moment calls us to reimagine – not retreat.

Conclusion: toward a new ethic of qualitative practice

The rise of synthetic identities is not the end of online qualitative inquiry. It is an invitation to evolve, and to reimagine what trust, presence, and meaning-making look like in the digital age. By grounding our work in relational ethics, care, and humility, we can respond to this new era not with fear – but with rigor and creativity. The path forward will be complex, but it will also be deeply human.

Footnotes

Data availability statement

Not applicable. No empirical data associated with this article.

Declaration of conflicting interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Ethical approval and informed consent statements

Ethical approval was not required. Consent to participate is not applicable.

ORCID iD

Alan Santinele Martino

Author biographies

Alan Santinele Martino (he/him) is an Associate Professor in the Department of Community Health Sciences at the University of Calgary in Canada. His main research interests are in disability; gender and sexualities; feminist and critical disability studies theories; and qualitative and community-based research, particularly participatory and inclusive research methodologies.

Kristen Hardy (she/her) is a Sessional Instructor in the Faculty of Arts at the University of Winnipeg and holds service positions with Brandon University. She studies relations between bodyminds and systems of power, exploring how forms of physical and mental diversity are pathologized and marginalized.

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