Language we use

One of the things I enjoy as a copyeditor is having the opportunity to be involved with a great number of wonderful projects through the documents I edit. One such particular lesson is to learn — although sometimes indirectly — how to build a styleguide of inclusive language where everyone feels welcome and included, regardless of background, ethnicities, sexual orientations, etc. and many other qualities that make us all unique. Some of these styleguides vary according to the nature of the documents I was working on, but most of the times a lot of them serve as a generic framework for the other fields as well. One very important thing I learned is that you always default to put people first, instead of their characteristics e.g. instead of “a blind person”, use “a man who is blind”, instead of “drug user”, use “people who use drugs”. Buffer outlines some good principles of using inclusive language, especially in the tech industry. Some other useful resources include these ones from British Columbia Public Service and Emerson College, which also outline the principles of inclusive language taking into consideration of culture and ancestry, political beliefs, marital or family status, and power-based interpersonal violence.

I also recently read about the language we use to describe our data, if reframed, can help us to fix our problems. Consider all of these data metaphors (also this):

It’s the “new oil” that fuels online business. It comes in floods or tsunamis. We access it via “streams” or “fire hoses.” We scrape it, mine it, bank it, and clean it. (Or, if you prefer your buzzphrases with a dash of ageism and implicit misogyny, big data is like “teenage sex,” while working with it is “the sexiest job” of the century.)

Data is often described as natural resources that are ready to be captured, mined, and capitalised on:

Natural-resource metaphors abound in discussions of data. Whether it is extracting oil or managing floods, data is often conceived as a kind of naturally existing resource ready to be captured, mined, and capitalised on.

What if we reframe our data to a more humane language?

In the digital realm, the idea of data stewardship should extend to how we think about the responsibilities of those tasked with collecting, storing, and making money off of our personal data. It should also extend to the content moderators and other workers labouring behind the scenes to make our online lives liveable. When we do assign a human face to data, it’s rarely of those workers around the world who are actually doing the cleaning, extracting, and labeling—and these folks need consideration and protection too.

As we speak about the health — physical and mental — of content moderators, it reminded me of this article on why sending death threats on a peanut mascot should not be tolerated (and screw you VICE for running the article), the hidden consequences of moderating social media, and three types of content moderation strategies.

Our data represents every bit of ourselves, which is why it’s important to handle it with the humanely care it deserves! And this starts with reframing the language of our data to use people metaphors.

Ethics for data scientists ought to be as explicit about the power dynamics and historical oppressions that shape our world. This means acknowledging and codifying the complicity of data-driven work—from population data collection to algorithmic decision-making—in perpetuating racist, sexist, and other oppressive harms.

The metaphors we use to understand data are powerful. As digital media scholars Cornelius Puschmann and Jean Burgess put it, they’re “cognitively and culturally indispensable for the understanding of complex and novel phenomena,” including new technologies like data analytics, machine learning, and artificial intelligence.

It’s critical to treat data “ethics” not as an end, but as a starting point, and limiting conversations about the societal impacts and obligations of data science solely to professional ethics would be a big mistake. Because if data is the new oil, its benefits will no doubt come with a devastating cost.

How to start doing this? We can start with recognising and questioning why our systems are imbalanced:

Leading the way instead are scientists and engineers who don’t seem to understand how to represent how we live as individuals or in groups—the main ways we live, work, cooperate, and exist together—nor how to incorporate into their models our ethnic, cultural, gender, age, geographic or economic diversity, either. The result is that AI will benefit some of us far more than others, depending upon who we are, our gender and ethnic identities, how much income or power we have, where we are in the world, and what we want to do.

This isn’t new. The power structures that developed the world’s complex civic and corporate systems were not initially concerned with diversity or equality, and as these systems migrate to becoming automated, untangling and teasing out the meaning for the rest of us becomes much more complicated. In the process, there is a risk that we will become further dependent on systems that don’t represent us.

As companies are becoming aware of these repercussions and have taken initiatives to develop ethical guidelines — which is good — more questions should surface: on whose ethical grounds do we base the guideline on?

All of this means that the “ethics” that are informing digital technology are essentially biased, and that many of the proposals for ethics in AI —developed as they are by existing computer scientists, engineers, politicians, and other powerful entities — are flawed, and neglect much of the world’s many cultures and ways of being. For instance, a search of the OECD AI ethics guidelines document reveals no mention of the word “culture,” but many references to “human.” Therein lies one of the problems with standards, and with the bias of the committees who are creating them: an assumption of what being “human” means, and the assumption that the meaning is the same for every human.

One proven way to improve the engineering approach to be more inclusive to AI is by adding the social sciences:

This is why tech companies’ AI labs need social science and cross-cultural research: It takes time and training to understand the social and cultural complexities that are arising in tandem with the technological problems they seek to solve. Meanwhile, expertise in one field and “some knowledge” about another is not enough for the engineers, computer scientists, and designers creating these systems when the stakes are so high for humanity.

Artificial intelligence must be developed with an understanding of who humans are collectively and in groups (anthropology and sociology), as well as who we are individually (psychology), and how our individual brains work (cognitive science), in tandem with current thinking on global cultural ethics and corresponding philosophies and laws. What it means to be human can vary depending upon not just who we are and where we are, but also when we are, and how we see ourselves at any given time. When crafting ethical guidelines for AI, we must consider “ethics” in all forms, particularly accounting for the cultural constructs that differ between regions and groups of people — as well as time and space.

I have also finished reading Edward Said’s The Politics of Dispossession and caught a few pages where he stressed on the importance of the language we use. Here are some passages from the chapter ‘Identity, Negation, and Violence’ on the usage of the word ‘terrorism’ devoid of context:

I must therefore confess that I find the entire arsenal of words and phrases that derive from the concept of terrorism both inadequate and shameful. There are a few ways of talking about terrorism now that are not corrupted by the propaganda war even of the past decade, ways that have become, in my opinion, disqualified as instruments for conducting a rational, secular inquiry into the cause of human violence.

Contemporary “terrorism” is thereafter identified with terrorists, who, as I have been saying, are most often “our” enemies, and are Muslim, Palestinian etc. Similarly, “we” are the West, moral, collectively incapable of such inhuman behaviour.

On how should we operatively dissect the word “terrorism”, as the phenomena does not exist in a vacuum and is often produced out of political opportunities of the country, region, or group:

Terrorism, in short, must be directly connected to the very processes of identity in modern society, from nationalism, to statism, to cultural and ethnic affirmation, to the whole array of political, rhetorical, educational, and governmental devices that go into consolidating one or another identity.

There is a room for intellectual discussion that partakes neither of the expert discourse of counterterrorism, nor of the partisan affirmations about “our” identity. That kind of intellectual discussion may involve taking positions on specific political conflicts in which terrorism or state-violence is regularly employed.

Talking about terrorism can therefore become an occasion for something other than solemn, self-righteous pontification about what makes “us” worth protecting and “them” worth attacking.

TL:DR; words matter, contexts matter even more, hire social scientists (or consider a multidisciplinary team).

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