Garbage in, landfill out: How data worship amplifies bias, division, and oppression
Around the turn of the century, theologian and retired professor at Harvard Divinity School compared our economic system to religion in Market as God. I have wondered lately if science and data have also become deities, and their offspring Data Science, an omnipotent, unquestionable god.
Don't get me wrong, I use data plenty in my work. I especially love infographics. What I'm concerned with is the practice of spreading data without interrogating the research machinery behind it, the blind faith, the worship of data. Methods, people, funding, circumstances all affect outcomes of any scientific query, not to mention whether or not the results of a certain study are suppressed or promoted (ahem, Exxon).
In the hopes of bringing data off the altar on which it resides for so many and back into the toolboxes we pull out only when necessary and in which we have an array of tools to choose from, here are some thoughts from pretty smart thinkers in various fields of life.
Safiya Noble, the author of Algorithms of Oppression: How Search Engines Reinforce Racism: Google is not just telling people what they want to know but also determining what’s worth knowing in the first place.
Ann Cairns, Vice Chair at Mastercard (on Why AI is failing women): We feed algorithms data that introduces existing biases, which then become self-fulfilling. In the case of recruitment, a firm that has historically hired male candidates will find that their AI rejects female candidates, as they don’t fit the mould of past successful applicants. In the case of crime or recidivism prediction, algorithms are picking up on historical and societal biases and further propagating them.
Cathy O’Neil, the author of Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy: The discriminatory and even predatory way in which algorithms are being used in everything from our school system to the criminal justice system is really a silent financial crisis.
Harvard Life Sciences Professor Stephen Jay Gould: [S]cience is often regarded as the most objective and truth-directed of human enterprises, and since direct observation is supposed to be the favored route to factuality, many people equate respectable science with visual scrutiny—just the facts ma'am, and palpably before my eyes. But science is a battery of observational and inferential methods, all directed to the testing of propositions that can, in principle, be definitely proven false. […] At all scales, from smallest to largest, quickest to slowest, many well-documented conclusions of science lie beyond the strictly limited domain of direct observation. No one has ever seen an electron or a black hole, the events of a picosecond or a geological eon.
Irreverent and hilarious blogger Vu Le of Nonprofit AF: In the past few years, there has been more and more pressure on nonprofits being able to produce good data. Getting more and better information on practices and outcomes can only be good for our sector. However, like fire or Jager Bombs, data can be used for good or for evil. When poorly thought out and executed, data can be used as a weapon to screw over many communities. Usually this is unintentional, but I’ve seen way too many instances of good intentions gone horribly awry where data is concerned.
Journalist and philosopher Rob Wijnberg: Behind every report, every feature, every news item, lies a worldview rooted in assumptions ontological (what’s real?), epistemological (what’s true?), methodological (how do we find out?), and moral (why does it matter?). Or, to put it in Gelauffian terms, all news comes from a position.
Tess Thackara (on Bina, the female black AI bot made by a team of white male developers): As has been widely publicized, the unconscious biases of white developers proliferate on the internet, mapping our social structures and behaviors onto code and repeating the imbalances and injustices that exist in the real world.
Author of Decolonizing Methodologies Linda Tuhiwai Smith: Research in itself is a powerful intervention, even if carried out at a distance, which has traditionally benefited the researcher, and the knowledge base of the dominant group in society. When undertaking research, either across cultures or within a minority culture, it is critical that researchers recognize the power dynamic which is embedded in the relationship with their subjects.
Researchers are in receipt of privileged information. They may interpret it within an overt theoretical framework, but also in terms of a covert ideological framework. They have the power to distort, to make invisible, to overlook, to exaggerate and to draw conclusions, based not on factual data, but on assumptions, hidden value judgements, and often downright misunderstandings. They have the potential to extend knowledge or to perpetuate ignorance.
In 2015, WSJ blue feed, red feed experiment showed how social media create echo chambers. I am currently experiencing that on LinkedIn. I keep hearing from a couple of dozen people and organizations. I have to spend extra time to select interests and interact with people outside of my narrow feed parameters. The default is tunnel vision.
All that to say, be careful out there and stay curious.