AI Comics:

These comics are excellent first resources for schoolchildren and those first encountering the subject material. While this form of reportage might attract a broad audience, it glosses over much of the nuances of data bias, algorithmic development, and coding, and paints a simplistic picture of the existing situation surrounding the presented issues. Complexity and depth are lost at the expense of accessibility to a wider audience.

Ultimately, this creative form of storytelling, and how the information is gathered and presented, is a wonderful resource for younger learners and those with lower levels of literacy, both in the sense of comprehension of words on a page, and in the form of digital literacy. These comics could be also printed and distributed as a zine for English-speaking learners.

ProPublica Article:

This example of data journalism is perhaps most effective in the way it compares various people who are considered ‘low risk’ versus ‘high risk’ in the industrial prison complex. The low risk individuals are, without exception, white. The high risk individuals are exclusively BIPOC. The algorithm rated Black, Indigenous, and People of Color offenders as higher risk than their white and non-BIPOC counterparts, regardless of the nature of the offense and whether or not it was a first offense. Most of the high risk BIPOC individuals went on never to experience trouble with the law again, while their supposedly ‘low risk’ counterparts went on to commit serious or multiple crimes.

In one example given, two DUI drivers were arrested. A light-skinned, perhaps Latino man, who had a history of 3 previous DUIs, as well as a battery charge, was rated a ‘1’ on the risk scale (lowest risk) while a dark-skinned Black woman had two previous misdemeanors and was never convicted of another crime despite being assigned a ‘high risk’ of ‘6.’ The lighter-skinned man went on to commit “domestic violence battery,” despite being considered at a lower risk of recidivism. The risk here is not something inherent in BIPOC individuals.

The perception of risk, rather, lies in algorithmic bias, as BIPOC people were more likely to be arrested, unfairly policed, and heavily surveilled in the past. The algorithm shows a skewed version of reality, one influenced by unfair policing practices as well as an industrial prison complex that is an extension of Jim Crow, chattel slavery, imperialism, and settler-colonialism. BIPOC people who find themselves arrested will also find that the algorithms which decide whether they will be punished proportionately to their crimes are compromised and are quite literally wired against them.

Too many BIPOC people have served disproportionally long sentences for small, petty crimes. And too many white people have been given token sentences and go on to commit worse and more numerous crimes. This is because the industrial prison complex is broken; it is rare that it manages to rehabilitate inmates, and rarer still that it prepares people for life after prison.

This article does an excellent job at potentially mobilizing the public and can be used in advocacy work and to potentially change minds and discriminatory policies, as well as going a long way to ensuring justice is restored – to those who have been unfairly policed, to those who have gotten away with heinous crimes by virtue of being white, and the future victims of those who truly will go on to re-offend, although it is clear that predictive technologies have a long way to go in this arena.

ACM Conference Paper:

This academic journal article highlights issues of accountability and transparency and is intended for an audience in academia or in a university setting. It has the most salient implications for researchers and academics working in this field.  

In contrast to the other readings, this article is relatively dense and inaccessible to those outside of academia. Its reach is limited by its complexity and the language it uses – which includes jargon we as Digital Humanities or Data Visualization scholars are familiar with that others with dissimilar backgrounds might look upon and read without fully comprehending.

Although this paper raises many salient points about bias in machine learning and AI, it is relatively obscure and might not be of interest to the general population, although it is incredibly relevant in discourse about holding human and corporate parties accountable for harm caused by algorithms, instead of diffusing that blame by blaming the victims, “bugs,” the “computer as scapegoat,” or citing the problem of “many hands.”

There are many ways of informing the public, and there is more nuance and proverbial shades of grey than might otherwise be expected, especially in our digital era. Different audiences call for different outreach methods.

Data journalism is duty-bound to the public to tell their stories in ways that challenge rather than reify existing harmful social structures, ideologies, and constructs.

Journalists who implement data analysis tools and use data visualizations or create their own datasets to analyze have a responsibility to the communities whose stories they are telling. Sometimes, these communities are much wider and further reaching than traditional journalism typically deals with, as a dataset can include thousands of people, while traditional journalism involves interviewing a small number of people, as Alex Howard points out in his keynote on “Data Journalism in the Second Machine Age.”

This extends the responsibility of the journalist across entire segments of the population, and furthermore, this responsibility must be taken with the utmost gravity, as data-driven journalism is often perceived as the definitive answer, and numerical statistics – no matter how they’ve been manipulated – tend to be cited as absolute, irrefutable proof of a fact. This is significant because social issues are often changing and evolving and are not fixed physical constants. Our social mores, constructs, contracts, and our society itself, is ever-changing and must be presented as such – as variable and malleable instead of static.

As Kevin Guyan points out, these same factors are not untouched by the collection of data, and indeed, “social identities” can be and often “are partly constructed through data collection practices.” (Guyan, 51) This intensifies the magnitude of data journalism’s obligation to the communities it represents and reports on. Unethical and biased data collection practices do not merely cause harm to, say, queer communities – they actively shape the wider public’s perception of the LGBTQIA2S+ community and their day-to-day lived experiences, and indeed help mold the social identities of queer and trans individuals.

In “Data Set Failures and Intersectional Data,” Nikki Stevens asks us to consider the question of “Can data, as a concept and/or as a material object, be intersectional?” (Stevens, 13) This is presented in the context of an analysis of the many failures of a survey that limited people to several options for gender, sex, sexuality, and which used words and terms that are inherently laden with bias, such as “gender non-conforming.” Terms like these suggest that those who are cisgender are “conforming” to society’s preset gender binary, while those who did not “conform” were othered by mere virtue of the wording of the question.

Stevens adds that the team was enlightened most by a final question which “asked users to add any other aspects of their identity they felt were important.” (13) The realization that there is more to people than a limited amount of data points, chosen from a limited, biased list, “reinforced for (them) the importance of self-identify boxes.” (13) Often, what people find most important about themselves cannot be quantified; it’s qualitative, and while such data can be compared to other datasets about different people, and attempts can be made to measure such variables, ultimately, people are complex systems, and their social identities are even more complex and can elude quantification or qualification.

Data journalism has an obligation to realize that within any given social system, there will be variables that remain unmeasured and others that are skewed by pre-existing biases. Transparency at all stages of data collection, analysis, and journalism, including being candid about what is being left out or sidelined, is paramount. The goal of journalism is to tell truths, and we must be cognizant that the truth can be biased towards existing social structures, constructs, and contracts.

I particularly enjoyed The Guardian’s article on “Tracking Australia’s Progress on the Climate Crisis and the Consequences of Global Heating.”

It is a very salient, timely article about climate change in Australia. It is important to remember that the seasons are reversed in the Southern Hemisphere, and Australia is just entering what is – paradoxically – likely to be both one of the hottest summers on record and the coolest summer for the foreseeable future.

Climate change is only worsening, as depicted by an alarming countdown clock that details how many tons of carbon dioxide have been emitted by Australia alone in the limited time one has been on the page, as well as how long it will take before Australia uses up its allotment of carbon emissions, as described by Paris agreements. The clock ticks ominously, always in motion, and the numbers are alarming. Our time is running out like sand in an hourglass to fix the problems wrought since the Industrial Revolution – and this serves as a visceral reminder.

This article provides data that can be of use to educators, students, advocates, environmentalists, and citizen scientists. Environmental justice activists can use these numbers to present their case with all due urgency. Governmental officials can use it as justification for more aggressive carbon offset and emission reduction policies.

Numbers alone cannot tell this story, though they are nevertheless needed, useful, and relevant. Despite its timeliness, the one notable thing this article does not do is appeal to pathos – it glosses over the sheer suffering caused by anthropogenic climate change. A chart towards the end of the article describes the dangers faced. Wildfires are listed as being “more severe or more likely to occur,” along with drought across the continent, more extreme cold freezes and hotter summers throughout the nation as well as maritime heatwaves in the oceans off Northern Australia.

Yes, this data-based article appeals to the logical mind. However, I believe there must be an attempt made to petition our common humanity as well. Our minds are simply not evolved for comprehending such staggeringly large numbers as 10.4 billion tons of carbon dioxide, which was and remains Australia’s total “fair share” allotment for the years including and between 2013 and 2050.

Please allow me to share an excerpt of the end of Zbignew Herbert’s powerful poem, “Mr Cogito Reads the Newspaper.”

I believe that what Herbert calls “the arithmetic of compassion” plays a large role in data journalism and must be carefully weighed and considered to avoid dehumanizing people into datasets while still telling the stories that must be told and brought to public attention.

Note: I regret that I will be unable to join you for a discussion on the 2^nd of October; please accept my apologies and allow me briefly to offer my thoughts on the reading I found most salient and compelling this week.

In Design Justice: Community-Led Practices to Build the Worlds We Need, Sasha Costanza-Chock argues for widespread, large-scale changes in the realm of design, whether that design is digital or exists in the physical realm. Costanza-Chock presents what they call the “matrix of domination,” which disproportionately discriminates against those who are members of one or more marginalized communities.

Costanza-Chock offers several solutions for combating the matrix of domination and spends time examining past efforts at peaceful protests in the name of justice, particularly justice for groups who have been historically enslaved, imprisoned, killed, forced to relocate, disenfranchised, and discriminated against at all levels of society. They present a compelling argument about how to prevent our machines, algorithms, and new technologies from inheriting our biases, the biases of generations prior, and the legacy of settler-colonialism.

They give one example of how “a Black person might experience a microaggression if their hands do not trigger a hand soap dispenser that has been (almost certainly unintentionally) calibrated to work only, or better, with lighter skin tones” (p. 45) but the problem is much more widespread. Arguably, the example given is a minor, perhaps even daily inconvenience, but BIPOC individuals are discriminated against on much larger, high-stakes scales.

Banking algorithms are also biased against Black people and people of color. These biases are a result of the ‘black box’ of AI as well as the historical data that the algorithms are fed in order to make decisions about the riskiness of offering a loan or a mortgage to a given person. Historically, BIPOC individuals were less likely to get mortgages, and now AI has taken up the baton of that same settler-colonist, Jim Crow, discriminatory practices that prevent BIPOC people from building generational wealth.

Costanza-Chock reminds us that is important, when undertaking the task of designing any new system or redesigning an existing one, to consider those who are at a different location upon the matrix of domination than oneself. They argue that the technology, user experience, and design fields are dominated not only by men but also by white people. They cite that the overwhelming majority of tech workers are white men and that many are white women. BIPOC individuals make up only a smattering of tech workers, and women, BIPOC people, LGBTQIA+ individuals, particularly trans folx, make up a small percentage of executive boards and are vastly outnumbered by white men.

There is an old Italian saying – “the fish rots from the head down.” When the very highest levels and the upper echelons of tech companies are made up of the same demographic, it is easy to overlook the needs of the many in favor of the needs of the privileged few.

The author posits that harm and benefit must be distributed equally throughout the population when one is (re)designing a new or existing product, space, service, etc., and that designers need to make deliberate decisions about how to distribute these harms and benefits to those at various locations on the matrix of domination.

I agree with many of their observations, and as an Artificial Intelligence Studies Major in university, I have long known about data bias and have been concerned about just what the author describes. I have also encountered new perspectives in this book and am grateful for the opportunity to have taken a dive into the world of design justice. We must build a future we can all be proud of; one where equity, equality, and justice for all are more than mere ideals to which we pay lip service – one where we can start to heal the scars wrought upon the world by the transatlantic slave trade, settler-colonialism, late-stage capitalism, and anthropogenic climate change.

It is important to design and build a future where all are welcomed and valued, at all levels of society; everywhere.

Dewey notes that “the public consists of all those who are affected by the indirect consequences of transactions to such an extent that it is deemed necessary to have those consequences systematically cared for.” (pgs. 15-16)

The public is perhaps best defined by what it is not, rather than what it is. The public as an entity is not: a governing body (although an argument can be made for governing officials being part of the public) nor is it a corporation, a medium-sized or small business. Nor is the public a legislative force, or a judicial one, and the public does not include law enforcement agencies (though can, of course, include law enforcement agents, especially when they are acting as private citizens and not on behalf of the governing bodies whose laws they enforce.)

When we define “the public” in the context of public interest, we must consider that the public is a very human entity. Though the public cannot consist of a single person, it does indeed consist of groups of people. The public is a term that best lends itself to the first-person plural; namely, we, our, and us – as in, “We, the people,” or as in, our lands and what is best for us. The public is a plurality. It is a collective.

In an increasingly globalized world, “the public” might refer to the entire human population; in other cases, it is used to refer to the inhabitants (or, more troublingly, merely the citizens) of a particular nation.

The commons, despite its tragic nature and the past and present mismanagement of shared resources, is ultimately a site for political and social contest.

The problem of the commons, if ill-regulated and brought to its logical extreme in which each rational user acts in their own best interest while ignoring the common good because its negative effect on the individual is negligible, can – in certain contexts – become a tragedy.

The commons might have begun as small, agrarian societies navigating the waters of resource allocation, but it has since become a problem that includes the world entire, including the oceans and all the biodiversity contained therein. For untold scores of species, human use and abuse of the commons has led to extinction, dwindling and fragmented habitats, forced changes in migration routes, or other disastrous consequences. Scientists warn we are in the midst of a sixth mass extinction on our planet, and the effects of anthropogenic global warming are felt by many of the world’s creatures – not just human beings.

I was struck by Hardin’s words that “the most important aspect of necessity that we must now recognize, is the necessity of abandoning the commons in breeding.” (p. 211) Here, he is speaking about overpopulation and the depletion of natural resources, biodiversity, and common property resources. As someone who is childless by choice, and does not want nor plan on having children, I wholeheartedly agree with this sentiment. However, I might be inclined to feel differently if I wanted children or planned on having them in the future.

Hardin goes on to say that “freedom to breed will bring ruin to us all,” (p. 211) but of course, this raises thorny questions about who should be allowed to breed. The biological impulse to leave a legacy in the form of a child is strong for a reason: because our species would have long since died out, if most of us had felt otherwise. Natural selection naturally favors the biological impulse to have children, and the genes of someone who wants children are more likely to be passed on. A return to eugenics might prove tempting, if we were to limit the right to have children. Widescale cultural, legislative, and judicial changes would be required, and penalties for non-compliance would likely seem both draconian and dystopian.

The tragedy of the commons is ultimately our inability as individuals to act in the common good of others when it means sacrificing our own personal gain. Part of the reason for this, I would speculate, is the fact that we live in a capitalist society with imperialist roots. Members of historically marginalized groups whose ancestors lived in poverty or who were enslaved generations ago, or whose land was stolen and never returned, cannot be expected to sacrifice personal gain for the greater good; especially when it is clear that the future envisioned by that greater good does not include them, their children, or their communities.

A fairer, more equal distribution of wealth might avert a tragedy of the commons, or even present itself as a solution to re-envisioning the commons. Most people would be more willing to sacrifice personal gain for the greater good, were they to (a) trust in their government and that such a governing body has their best interests at heart, (b) believe that the vision of the future they are sacrificing for in the present will include people like them, their family, and their children and communities, and, perhaps most importantly, (c) have enough resources to live without food insecurity or the threat of homelessness.

This is, perhaps, where the concept of an “undercommons” can be useful in illuminating one of the most fundamental problems of the commons – that equal access to a common resource is a theoretical possibility yet is, in reality, an improbable situation. The commons cannot be considered sans cultural context, because there are a great many groups of peoples who have historically not had access to many aspects of the commons, and other groups who must reckon with the difficult history of their ancestors taking more than their fair share of common goods, properties, and resources.

P.S. I had many more thoughts about these readings than could fit into a single post and am eager to share them with you next class!