The Hidden Biases in Big Data

By Kate Crawford. 2013

293 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Safety, Robustness & Interpretability

Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Entire communities may be missing or misrepresented in datasets, exacerbated by digital divides, leading to wrong conclusions [30, 52, 53, 119] and residual unfairness [103].”
Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology (2019, background): “This is because NLP systems depend on language corpora, which are inherently “not objective; they are creations of human design” (Crawford, 2013).”
Critique and Contribute: A Practice-Based Framework for Improving Critical Data Studies and Data Science (2017, background): “5,25,26 While lack of contextual understanding is often at the center of big data critiques, context is also a central concept for the work of human computer interaction, ubiquitous computing, and data science.”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “When overlooked, such reporting biases can also lead to discrimination (Crawford, 2013; Barocas, 2014).”
Algorithms as culture: Some tactics for the ethnography of algorithmic systems (2017, background): “Out of context, his remarks were interpreted as evidence of what Kate Crawford has called ‘‘data fundamentalism’’—‘‘the notion that correlation always indicates causation, and that massive data sets and predictive analytics always reﬂect objective truth’’ (2013).”
The challenges and limits of big data algorithms in technocratic governance (2016, background): “The veneer that because algorithm are mathematically driven and data centric, the outputs are impartial and unambiguous (Boyd & Crawford, 2012; Crawford, 2013) has been challenged (Gillespie, 2014) considering the economic and cultural imperatives of how algorithm influencing the wayswe consume cult”
The limits of crisis data: analytical and ethical challenges of using social and mobile data to understand disasters (2015, background): “The data is further slanted due to extended power blackouts after the hurricane, resulting in overrepresentation of areas that have more access to power (Crawford 2013).”
Is Bigger Always Better? Potential Biases of Big Data Derived from Social Network Sites (2015, background): “Scholars have called attention to the decisions that go into what data are generated and collected in the first place (Baym 2013; Bowker 2005; Crawford 2013; gitelman 2013; tufekci 2013), different understandings of the notion of big data (Neff 2013), thorny issues of privacy (Lazer et al. 2009;…”
Mapping the Data Shadows of Hurricane Sandy: Uncovering the Sociospatial Dimensions of ‘Big Data’ (2014, background): “In arguably the most visible critique of big data so far, danah boyd and Kate Crawford note that ‘‘Big Data and whole data are also not the same’’ (boyd and Crawford, 2012: 669).”
The Implicit Fairness Criterion of Unconstrained Learning (2018, background): “Many criticize the practice of unconstrained machine learning for propagating harmful biases (Crawford, 2013; Barocas and Selbst, 2016; Crawford, 2017).”
Lessons for artificial intelligence from the study of natural stupidity (2019, methodology): “When Boston released its Street Bump smartphone app to detect potholes using the GPS and accelerometer data of drivers, the city faced the challenge of ensuring detection in neighbourhoods with low-income and older residents, who are far less likely to own smartphones 12 .”
Fairness without Harm: Decoupled Classifiers with Preference Guarantees (2019, background): “Such performance disparities are now regularly reported (Angwin et al., 2016; Dastin, 2018), eliciting calls for fairness in machine learning (Crawford, 2013), and prompting the development of technical solutions (Zliobaite, 2015; Barocas et al., 2018; Corbett-Davies & Goel, 2018).”
Critiquing Big Data: Politics, Ethics, Epistemology (2014, background): “What these arguments fail to fully consider, however, is that data sets—including predictive data—may lead to new concentrations of power, and they are never methodologically removed from human design and bias (Crawford, 2013).”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, background): “Algorithms reflect the norms and values of its makers and funders (Crawford 2013a; Hallinan and Striphas 2014; Jasanoff 2016).”