The idea of ‘queer data’ is one of a few emerging literatures that are deeply concerned with the harms that data can do to traditionally marginalised, disadvantaged and oppressed groups, but also remain hopeful about alternate possibilities for more inclusive and liberatory data practices and forms. Queer data therefore sits alongside ‘data feminisms’ (D’Ignazio & Klein 2020), ‘black data’ (McGlotten 2016) and ‘crip technoscience’ (Hamraie & Fritsch 2019) in striving to develop more radically-minded approaches toward the issues and concerns that form the focus of ‘critical data studies’.
‘Queer’ as a challenge to dataism
In one sense, the idea of ‘queer data’ raises a straightforward challenge to the ambitions of data science – in short, to what extent can data ever ‘ethically represent’ queer people? This therefore foregrounds longstanding concerns in the social sciences over the representational limits of data. To what extent can data fully represent anyone who self-identifies as non-straight and/or non-cisgender? To what extent can data fully represent identities, experiences and political positions related to gender and sexuality that defiantly resist dominant societal norms?
Seen along these lines, queer people present an obvious challenge to the promised capacity of data science to accurately and insightfully model the social world in statistical terms. Indeed, as we have written about before, quantitative data is notoriously limited in its capacity to account adequately for the complexities of lesbian, gay, bisexual, transgender, and queer lives. As Ruberg & Ruelos (2020) put it:
“an individual’s sexual and gender identities, especially for LGBTQ people, cannot be understood as a set of static, fixed data points … traditional notions of demographic data do not allow for the fluidity and multiplicity of gender and sexual identities that characterize the lived experiences of many LGBTQ people”.
This epistemological challenge cannot be addressed simply by including additional categories of ‘Other’ or ‘X’ alongside traditional binaries of male/female, man/women. Instead, this raises the more fundamental contention that perhaps some things cannot be subject to quantification if they are inherently fluid, non-fixed and polyvalent. For example, the complex relationship between gender identities and transgender identities fundamentally complicates any presumptions that a ‘category’ of ‘gender’ might be effectively determined along a single axis of data.
Moreover, the idea of queer data also raises questions of intent and effect – even if it were possible, why would these be facets of a person that we would want to quantify, for what purposes, and with what anticipated outcomes? From a queer point of view, history suggests that most attempted acts of quantification constitute acts of control and marginalisation. This applies to even the most well-intentioned acts of quantification. For example, as Jen Jack Gieseking (2018) observes, the very premise of ‘big data’ privileges the perspectives and interests of (pre)dominant social groups, and further marginalises the viability and voices of other groups that have historically be made ‘small’ through administrative forms of (mis)measurement, erasure and violence.
The possibility of ‘queering data’
This fundamental incompatibility between queer lives and digital data therefore leads us on to the provocative move of rejecting conventional data science assumptions and instead exploring ways of ‘queering data’. This involves the use of ‘queer’ as a verb to challenge and resist expectations and norms that currently surround data and datafication – particularly in terms of challenging the entwinement of digital data with power and the politics of oppression (Jakobsen 1998).
In this light, then, data can be seen as profoundly oppressive and exploitative – a intentionally hostile act of dispossession rather than a passive neutral act of measurement. Here Ruberg & Ruelos (2020) draw on Joanna Drucker’s (2011) description of the etymological origins of ‘data’ not with the Latin term ‘datum’ (something which is given and observed, like a given truth), but the Latin term “capta” (something which is created and then captured or taken). Seen along these lines, it make little sense to argue that we simply need to develop ‘better’, more ‘accurate’ or more ‘nuanced’ ways of categorising the true identities and desires of queer people. As noted earlier, these are not stable or fixed entities that can be classified in the form of a permanent measure or indicator. Moreover, attempting to classify these entities is itself an attempt to control, regulate and limit the identities and desires of queer people. As Ruberg & Ruelos (2020) reason:
“Even as we strive for social justice through and within data, we must acknowledge the worrisome tension in calling for marginalized lives to be better captured, translated into data, and put to use by corporations and regulatory bodies”
Instead it is more productive to consider the tricky question of what – if anything – alternate forms of ‘data for queer lives’ might look like. Is datafication something that needs to be resisted in all instances and at all costs? Alternately, is it possible to imagine what Ruberg and Ruelos speculate could be “a more nuanced approach that embraces a queer understanding of gender and sexuality—one that is more inclusive, acknowledges complexity, and affirms the identities of respondents”? This raises a series of challenges:
- How might alternate forms of data be constructed that better represent and serve the lives of people that hitherto have been the subject of data-driven reduction and oppression?
- How might data be put to use in ways that complicate and disrupt dominant cultural narratives about how marginalised groups are structured, organised and arranged?
- Is it possible to reconstruct our understandings of data in ways that are constructed around queer reconsiderations of identity, information, and meaning as fundamentally non-fixed, heterogenous, fluid, and open to constant change?
- How might data be reimagined in forms that signify diversity, incoherence and messiness?
These are not challenges that are easily addressed, but certainly point to need to reimagine the basic premises of what has been described elsewhere as the dominant ideology of ‘dataism’. The challenge for anyone not wanting to dismiss the idea of data and society altogether (and a response of total rejection certainly does have merit from a queer perspective), is therefore how to reimagine and reinvent data science along non-objectivist and non-positivist lines. This suggests an initial step of stripping data of any ‘supposed objectivity’ – instead, acknowledging (and making use of) the incoherence of most aspects of people’s everyday lives, and rethinking the tools and ambitions of any quantitative analysis. In short, Ruberg & Ruelos argue that this requires us to ‘shake loose’ any heteronormative assumptions and ‘destabiliz[e] the very belief that demographic data can sufficiently reflect the realities” of our messy everyday lives.
Of course, rethinking data along these lines is not simply a queer issue. These are clearly conversations and contentions that need to be continued throughout all aspects of ‘critical data studies’. Reimagining data and datafication along these lines raises the need for our discussions of data to confront dataism from the intersectional perspectives of queer people who might be of colour, working class, with different dis/abilities – in other words, paying close and prolonged attention to disrupting the ways in which data has traditionally impacted negatively on “any lives and bodies of those whom data has across its cultural history, sought to regulate, surveil, devalue, and even dehumanize”.
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.
Drucker J (2011) Humanities approaches to graphical display. Digital Humanities Quarterly 5: 1
Gieseking J. (2018) Size matters to lesbians, too: Queer feminist interventions into the scale of big data. The Professional Geographer 70(1): 150–156
Hamraie, A, & Fritsch, K. (2019). Crip technoscience manifesto. Catalyst: Feminism, Theory, Technoscience, 5(1): 1-33.
Jakobsen, J. (1998). Queer is? Queer does? Normativity and the problem of resistance. GLQ: a Journal of Lesbian and Gay Studies, 4(4): 511-536.
McGlotten S (2016) Black data. In: Johnson PE (ed.) No Tea, no shade: new writings in black queer studies. Duke University Press (pp.262–286)
Ruberg, B. & Ruelos, S. (2020). Data for queer lives: How LGBTQ gender and sexuality identities challenge norms of demographics. Big Data & Society, 7(1), 2053951720933286.