LESLIE SHADE: Welcome everybody, my name is Leslie Shade, and I'm a professor at the Faculty of information University of Toronto.
I'm serving as a moderator for this Roundtable on Algorithmic Literacy, I'm first going to provide a brief introduction to the purpose
and organization of the Roundtable.
And then, an introduction to the Roundtable participants before diving into our session.
I'm coming to you from my virtual, my very messy virtual office, located in Ottawa, which is on the traditional unseated territory of the
Algonquin people's.
The Algonquin peoples have lived on this land since time immemorial, and we give thanks as their culture impressions have nurtured
and continue to nurture this place.
This Roundtable will discuss the concept and parameters of algorithmic literacy.
A key concern is that algorithms embed socio technical power imbalances that are entrenched in a multitude of our digital devices and applications
that are quite opaque to us. How can we get under it into and infiltrate these black boxes? What does it mean to say that we are
algorithmically literate? What are algorithmic literacy gaps?
Further, how can algorithmic literacy be integrated into formal and informal educational programming and digital literacy initiatives?
For a variety of people, so from young people to seniors to newcomers to young adults, are their best practices for integrating algorithmic
literacy into digital literacy programs and education, and how might this be done from a human rights perspective?
And in terms of governance, are the various codes of practice and principles for algorithmic transparency and digital and data literacy.
from a range of stakeholders, including government and civil society that serve as good practices? Or are there some that serve as really
worst practices that we want to avoid.
I just want to first introduce our panelists in alphabetical order and then I'll get to the series of questions that we're going to be posing
this afternoon, and these introductions will be done in alphabetical order.
Monica Jean Henderson is a resident junior fellow at Massey College and a PhD student in the Faculty of information at the
University of Toronto.
She completed her BA and her Master's Degree in Communication and Media Studies in the Department of Communication at Calgary.
Monica's research interests explored the discursive relationships between material adult literacy practices and framings of digital literacy in
policy. In her doctoral work, Monica investigates how crucial funding is to adult literacy programming, and this is inhibited due to
neo liberal policy bias towards the ideal or the innovative user of technology.
Her research and policy-making goals are to contribute to justice and equity for low literacy adults who are often invisiblized in both
academic discourse and Canadian federal and provincial policy. Elisha Lim is currently writing a PhD
dissertation on digital media at the University of Toronto's faculty of information, where they are also project manager for the
critical design methods Institute.
They are an award winning queer filmmaker and graphic novelists who's high profile activists career is the subject of their
research into social medias distortion of identity politics.
Their concept of identity economics has been published in Social Media + Society, Documenta, Bitch Magazine, C Magazine,
and 2020 Tedx talk. In addition, their community-based award winning claymation films, solo and group exhibits, curatorial projects,
anti-racist Montreal annual festival “Qouleur,” I don't know if I'm pronouncing that right, solo and grorup exhibits, curitorial projects,
anti-racist university curricula internationally, and documentation on academic monographs has been quite phenomenal
in terms of thinking about the various nuances of algorithmic literacy.
Jonathan Obar is an Assistant Professor in the Department of Communication Studies at York University, he also serves as a
research fellow with the Quello Center of communication policy Research Center at Michigan State University.
His teaching and research focuses on information and communication policy, and the relationship between digital technologies,
civil liberties and the inclusiveness of public culture.
Recent academic publications address big data and privacy, Internet routing and NSA surveillance, network neutrality, and digital activism.
And he is co-editor of Strategies for Media Reform: International Perspectives, which was published by Fordham University press.
Karen Louise Smith is an Assistant Professor in Communication, Popular Culture, and film at Brock University.
Her research explores the tensions between openness, privacy and participation and technologically mediated culture.
Some of this research has been conducted in collaboration with Mozilla, a global nonprofit organization which is committed
to the open web. From 2013 to 2015, Karen conducted collaborative research with Mozilla to build the Hive Toronto Digital Literacy Network
consisting of over 60 organizational members, public library systems, after school programs.
In 2017, Karen was awarded a Mozilla research grant for a project titled Add-Ons for Privacy, Open Source advocacy tactics,
for Internet Health.
And she's also currently a collaborator on the eQuality Project, which is examining issues such as privacy and cyberbullying,
which face youth. And her work has been published in a range of journals including Journal of Communication, the International Journal
of the communication - IJOC - Surveillance and Society, and Computer Supported Cooperative Work.
As for me, I'm a Professor at the Faculty of Information, University of Toronto, and my research focus since the mid-1990s
has been on the social and policy aspects of information and communication technologies, with a particular focus on youth,
gender and political economy. I am also a Co-investigator on the eQuality Project, and this is a seven-year partnership -research partnership
of scholars, Community organizations, educators, policy institutes, and youth themselves, funded by the Social Sciences and Humanities
research council of Canada. And we are exploring
what you think about privacy and equality in networked environments, with a particular focus on youth from marginalized communities.
Indeed, my research and interest in algorithmic literacy stems from research I'm currently doing with two RAs on the project,
Monica Henderson on this panel and Katie Mackinnon.
As well as work with other team leaders on the eQuality Project, for instance in some recent research that I conducted with
the co-leads Val Steeves, Jane Bailey, also Jackie Burkell, and Pris Reagan, we note that Canadian youth
find algorithmic sorting confusing and restrictive, especially when it occurs in environments including schools
where they have no choice but to participate.
Further, we know that algorithmic practices have eroded young people's trust in platform corporations.
With the surveillance very endemic in these practices stifling creative uses and eroding their sense of privacy.
So, moving ahead, there are four sets of questions we'll consider in this Roundtable. The first two are interlinked and we'll have each
participant respond individually to these questions.
These are the Roundtable questions, so: How do you define algorithmic literacy? Big question. And what do you think are
best practices for integrating algorithmic literacy into media/digital/data literacy programming?
So if I just ask each participant to answer that question and then after that round will do these next set of questions.
What should be principles for algorithmic transparency? To whom, can we make these systems accountable?
And how can critical race and gender scholarship contribute or be incorporated into algorithmic literacy?
We will open the Roundtable up to questions and comments from the participants, and I will be monitoring the chat.
So without further ado, I believe the first participant is Monica.
MONICA JEAN HENDERSON: Thank you for the introduction and
thank you everyone for joining, I'm kind of happy both my PhD and my Master Supervisor are here so I'm feeling kind of spoiled.
So as for how I define algorithmic literacy so,
I've been studying digital literacy since starting Grad school and its - literacy itself is so difficult to define just because there's so many different approaches.
And competencies that you know intertwine in our locker diverged. And so I usually just kind of return to something a little
theoretical and abstract to talk about literacy, which is
Paulo phrase aphorism to read the word and the world, and so to me, this is about like understanding a text or a medium
and how it reflects or reproduces social, political and economic conditions.
With the goal of achieving critical consciousness and working towards transformative social justice. So, in terms of algorithmic literacy, this
would mean understanding how algorithms reflect or reproduce
those conditions and how to develop a critical consciousness about them to move towards social justice.
And so recently I've been working on an extensive literature review for the eQuality project about algorithmic literacy.
And pretty early on, I found that it's difficult to talk about algorithmic literacy without talking about data literacy
just because algorithms and data are so intertwined. And so I'll ask Elisha, can you move the slide forward please?
So this is a sort of a beginning
concept map I've developed, just as I'm working through some literature, to talk about sort of where algorithmic literacy and data
literacy overlap and how they're different even if they're often talked about in similar discussions.
And so you'll notice that I have in the Center of the Venn Diagram here sort of places that you'll encounter
algorithms or data, of course, not an exhaustive list. And on each side sort of trying to parse out what is different when we're looking at the algorithm in this
medium or situation, versus when we're looking at data. And typically data literacy I've noticed in the literature we're talking about
oftentimes though, privacy, data exploitation and mining, Labor and data commodities, and knowing when and how your datas collection
and how will be used typically in algorithms and understanding how data is a resource. But also importantly, how data is chosen,
such as like when we're training machine learning, how the data set we used to train
that tool is itself important to like question for like race and gender class biases and so on.
And so yeah this is kind of me working towards a definition of algorithmic literacy. Happy to talk about this, I hope that sort of answers,
the first question.
As for the second question about what I think are the best practices for integrating algorithmic literacy into literacy programming,
I particularly am interested in programming for adults, and those adults, specifically that are enrolled in basic literacy or a digital literacy programs.
And so, in my literature review I've, you know, I've come across - and in my own research, I've come across sort of the challenge
to integrate this sort of learning into programs, because many of the students are learning to read, haven't had that haven't finished K to 12 education,
learning to type or use email, for the first time, and so to step up to algorithms which are a lot more, you know,
both concrete and abstract but very much not visible and concrete
to the naked eye. And it's a difficult jump and so you know, there are some
authors that are coming up with
participatory embodied like activities for talking about algorithms and data so
Catherine D'Ignazio, for example, talks about creative data literacy activities such as you know,
have everyone in a classroom lineup in a row, and start sorting by you know, green shirts stand over there, red shirts stand over there
and what happens when somebody has both red and green on their shirt? How can you easily categorize that person what problems, does it cause?
And having this as like sort of a way to start thinking about data and algorithms. But you know there's also the issue of balancing these
discussions about privacy with, you know, the understanding that these technologies can connect people in meaningful ways,
and you know, trying to find that balance between empowering and terrifying people. So there was actually one - when I was
in my Master's Research in the adult basic digital literacy classroom, sorry, the discussion around privacy came up,
and one student raised their hand and was talking about how privacy is important, and at first like you know the instructor was like
"Oh, this is great, this is great", but then she said "Because I'm pretty sure my TV is watching me".
And so, not that that is totally out there, you know, I have a smart TV, and it probably tracks all kinds of stuff like what I choose to watch everything but,
she was like convinced that her TV could see her, and so, how do we, how do we balance those discussions for people in those sort of
situations. And, lastly, the last thing I want to say is that I - before we can really meaningfully integrate these discussions into
or these practices into programming, there are a lot of methodological gaps in algorithmic literacy research that are important to
address. And so, particularly, in a lot of recent research, even a lot in 2020, 2019, 2018, a lot of
researchers are looking at algorithmic awareness as a way to measure algorithmic literacy, because you know when we're
working with algorithms, usually we're just trying to see how they work we're not actually producing them ourselves. And so,
in measuring algorithmic awareness, however, a lot of these researchers are snowball sampling from their own personal networks
like on Twitter, or you know their own university pals. And so they actually say in their limitations that
you know the education level of our participants could be a limitation talking about this. And to me, I find that, like a little
discouraging or - because I work, specifically with populations that haven't finished K to 12 education, and so, if we're only measuring
or defining algorithmic awareness, you know, with participants that have a university education, these things
you know literacy of just reading and writing and math have a lot - on a basic level - have a lot to do with how we understand algorithms.
And so yeah, these are some challenges we have to look at and I will also just plug for practices, the Digital Defense Playbook
by the Our Data Bodies Project, I think, is a fantastic example of participatory,
pedagogic,
pedagogical design for data and algorithmic privacy. So yes, thank you so much, looking forward to hearing the rest of the panel.
LESLIE SHADE: Thanks Monica.
So next up is,
Jonathan.
JONATHAN OBAR: Hi everyone, hope you're doing well, first of all, thank you to the conference organizers and for putting this together,
and of course to Leslie for leading our panel.
One thing I want to comment on is how important it is that critical cultural scholars be involved in this debate, in this dialogue,
The dialogue, the deliberations ongoing about algorithmic policy are often dominated
by computer scientists, lawyers, and economists, and so a panel like this is, I think, exactly the sort of thing that we need
in these policy debates. We need more
of this sort of material, these perspectives, especially if we're going to, again, have these political, cultural perspectives reflected in
in the future of these systems.
So, in terms of, and maybe you could go to the next slide please, I'm an assistant professor at York University in the Communication Studies Department.
Very happy to be here, of course. So when I was trying to think of how to define algorithmic literacy, there's a lot of related terms
and it reminded me of
Kennedy and Moss' concept of known or knowing publics, or rather that article, where they talk about strategies for moving individuals from
being a part of a known public, to what they call a knowing public. And they describe,
efforts to, you know - in order to do that, what you're attempting to do is to democratize data power, and in that article in Big Data & Society they
talk about ways to alter the power dynamics that currently exist and that are escalating things like
I'll just quote here, speaking about individuals in terms of becoming a more powerful in the big data and algorithmic space, right.
Data analytics should be subject to greater public supervision and regulation, the data and the algorithms should be available and accessible to all.
And that the systems while they're being used to help industry, government, etc, should also
have the role of helping to train and create more reflective, active and even knowing publics. I'll just say this also
we can say that this is sort of a more specific way to think about algorithmic literacy, right, understanding collection, management
retention, disclosure, use of data, descriptive analytics, predictive analytics, prescriptive analytics, right, this sort of like
specific checklists that you might want to create.
More generally, I think we could also refer to Shade and Shepherd's Digital Policy Literacy, which I teach a lot in class,
which is a great way, I think, to think about sort of the bigger
Again critical cultural questions that we need to be engaging people in when we're trying to move towards something like algorithmic
literacy, right, teaching and engaging people in policy processes that have to do with algorithms.
Again, teaching and engaging people with regards to the political economy of media systems, as well as
the third element of their definition of digital policy literacy, right, teaching and engaging people in elements of infrastructure,
as it relates to these technologies. So we just the next slide please.
So, moving to best practices, now I don't want to - and I sort of mentioned to Leslie at the beginning that I don't want to be overly critical
of a lot of the -
I don't want to be overly critical of you know, the likely forward-thinking policy, and other proposals that my colleagues are presenting but,
what I published in the literature tends to be fairly critical of the possibility of things like algorithmic transparency, or what I call
data privacy self-management, or rather what Daniel Stallone calls data privacy self-management, and I've adapted some of his work.
What I do in a paper published in Big Data & Society is I adapt Walter Lippmann's argument he presented in The Phantom Public,
where he talks about something called the fallacy of democracy, and I adapted towards what I call the fallacy of data privacy self management.
While it's great to have these idealistic notions of what we should be teaching people as they move towards
digital policy, literacy, or to becoming knowing publics, we need to be careful that we're not too idealistic in that
we are proposing things that could never actually be achieved. And this is what Lippmann argues in The Phantom Public almost 100 years ago.
He says, "an ideal should express the two possibilities of its subject".
What he talks about in The Phantom Public is he says in the concept of sort of a direct democracy, right,
the idealistic scenario is that we have 30 million people
deciding about how to fix potholes and run a health care system and the military and all the different things that government does he says,
this is a fallacy.
Because we don't have what he calls omni competence.
We don't have the time to figure all of these things out, even if we did have the omni competence. And also what's the system like
to get 30 million people into a room to decide on everything.
So I adapt this argument in the paper Big Data and The Phantom Public, and call for what's called the fallacy of data privacy self management.
I similarly say, are we omnicompetent enough to learn all about all the algorithms, about all these big data systems? Do we have the time
to address all of them? And by you know in sort of what system exists that would facilitate this, so I think.
What I'm - if you take one thing away from this presentation, it's this: it's great to be idealistic, but we have to be careful that our idealism
isn't just something we talk about. We must make sure that we move towards something that is practical.
And I have - maybe I'll save my answers for the next round, because there are some more practical things about like let's say the information
fiduciary, the infomediary, things that people are starting to talk about that move us away from things like the problematic notice and choice
privacy policy model. And so we can just move to the last slide please.
And so, I just wanted to close on this because
you know, you start - I started this brief talk thinking, sort of, again with these idealistic notions that we've seen in the literature,
but moving towards knowing publics,
and digital policy literacy, and then I'm saying "well we're maybe we're not smart enough, or we don't have the time".
One thing that Lippmann says that I think is important to reiterate, is he says, "as I watch one thing, 1000 others undergo great changes".
And so, if we're monitoring Google's algorithm, what about Walmart's algorithm? And the bank's algorithm? Right. And on and on and on
the government's algorithm.
What Lippmann is doing here, it can sound undemocratic and certainly Lippmann's critics have called this work undemocratic,
pointing to John Dewey and saying we should use John Dewey's notion of participatory democracy as a guide.
But John Dewey himself -and this is what I'll close on, apologies for going a bit long -
read Lippmann's work, and this is what he said:
"Mr. Lippmann's essay is in reality a statement of faith in a pruned and temperate democratic theory, and the presentation of methods
by which a reasonable conception of democracy can be made to work, not absolutely,
but at least better than democracy works under an exaggerated and undiscipline notion of the public and its powers".
So let's make sure that our idealism is not exaggerated and undisciplined. Thank you.
LESLIE SHADE: Thank you, Jonathan.
Karen.
KAREN SMITH: Hi, thanks for the intro Leslie.
I'm going to try and build upon ideas that both Jonathan and Monica have already presented, and adapt my comments on the fly as we go.
So from Jonathan, I really appreciated that he brought up ideas about The Phantom Public and
how challenging it is for an individual to manage all of their data and to have a command of where it goes and what happens to it.
Monica mentioned really a lot of the methodological gaps which exist in really understanding how we get to or how we do
research on algorithmic literacy.
And in preparing my comments for today, really what I've done is a little bit of a backwards design exercise to look at some of my
research over the last few years.
And to try and surface, really, what questions underpin the research about algorithmic literacy. And really what I've done what
my tactics of been to try and get at and what it is and how we achieve it.
So I have this first slide with our first question. And this resonates really, really strongly with Monica's Venn diagram about - the bottom
point was about inputs and outputs of data.
And much of my research over the past few years has shared a commonality in asking the question: Do citizens understand
how their inputs are linked to outputs?
And these might include content, which is presented to an end user, as well as the kinds of categorization which are
made about individuals. Which lead to
particular experiences, predictions, or even administrative or bureaucratic decision making that may impact your life and your circumstances.
So just as some really quick examples, I've noticed that a lot of my undergraduate students have a lot to say about what they see on
Tik Tok on their For You pages,
And I think this is really emblematic of, really, the kind of curation of content by algorithms.
And I've been really fascinated by the diversity and experiences of what people see and what kind of explanations and mental models,
they have to make sense of what is presented to them, and why.
So this is anecdotal through teaching versus through research, but it's definitely something I hope to build upon in the future.
Many people who are in attendance here today, and my fellow panelists are also probably very familiar with examples like,
Target's use of algorithms to calculate a pregnancy score, to kind of predict if you're doing something like buying vitamins,
how likely, you are to be
needing a crib eight months in the future, and really to categorize people, and market to them, based on predictions and categorizations.
And finally, I wanted to bring up the idea of redlining and digital redlining, and the idea that really decisions about
really any financial decision or access to an educational institution, that there's all kinds of ways that algorithms can be implicit and
complicit in determining what kind of opportunities exist for individuals in society. So with this background sketched,
The way this is played out in a recent project, in my next slide, I have just a quick review of some research which Leslie and I did,
where we looked into YouTube Kids, the app, as well as a smart toy by Fisher Price.
And we were really interested here in conducting a privacy sweep to look at a variety of indicators, including the political economy
and the ownership model associated with these apps and toys, Internet of Things related,
apps and devices, which are collecting children's data through their interactions and their play time with these devices,
and echoing much of what Jonathan said, is incredibly challenging for researchers, as well as privacy regulators. Our method was
adapted from a method that's used by organizations like the office of the Privacy Commissioner of Canada to try and explore
the practices of various commercial data collectors and data custodians.
So we were very challenged in tracing
the privacy practices and the implications for the collection of children's data in these circumstances and that challenge definitely
echoes much what Jonathan said around really the high burden that's put on individuals if they're expected to be responsible for
the custodianship and the responsibility of the decision making of
controlling where their data goes. Another kind of way that this question around inputs and outputs of data, a way that I've explored it
in recent work, on the next slide I have a brief overview,
I am currently working on some research with colleagues Jacqueline Botterill, Marian Bredin, Jennifer Good, and Brittany Melton and
this is just one question from a survey where we've been exploring the resistance practices of generation Z in relation to advertising,
and one way, when I looked back at our survey instrument, one way that we've tried to surface I think some kind of attitudes and perspectives
about algorithmic literacy is by asking young people who they believe, who has a stronger argument and this survey question
was very directly influenced by
the report Why Am I Seeing This? from New America's Open Technology Institute and it's by authors Spandana Singh, and
many of you may be familiar with Netflix changing the thumbnail image
for a movie or for a program based on really who they think you will be interested in viewing content of and this could tie to the race
of the actor, of the performer who's involved in the programming. And we were interested in asking members of generation Z if
really, they find a lot of joy in the content that's presented to them by recommender algorithms. This is very consistent with a lot of the
statements by Netflix around the purpose of their recommender algorithmic systems.
Do they - do they agree most strongly with Netflix's stance on this, or do they agree more closely with
The New America think tank, who are interested in policymakers protecting user privacy, and trying to prevent harm from
algorithmic recommendation systems.
So, once again, this is one question, it's hard to get a full picture of the research, but I just wanted to give you a glimpse of
how I'm trying to bake this in methodologically to various projects. Another question that underpins some of my recent work
is on the next slide.
In terms of kind of an overarching question about algorithmic literacy, I'm also really interested in how people can probe systems.
And I think that it's important for people to understand algorithms. Once again, I am also a skeptic of a lot of transparency and fairness initiatives.
And I think that's it's integral because of this, that we retain space for people to critically question algorithms, to question their existence,
and their application and to contest their use in their everyday life. So how do we do this and how do we make this happen?
Once again I just like to give you kind of a glimpse into how I'm using some of these ideas in recent work.
So during the intro, Leslie mentioned that I've done some research on privacy extension software, and the next slide has a screenshot
from a privacy extension software interface. I worked with Elysia Guzik on a project, where we looked at 26 privacy extension software titles.
And we interviewed 30 individuals associated with producing privacy extension software, and so this is only one
screenshot from one software title.
But Data Selfie, I'm showing you a little glimpse into this today, because this particular privacy extension software piece
it received funding in 2016,
And part of what's happening when users download Data Selfie and they add it to their browser, it allows them really to keep track
of their engagement in and with Facebook, and then to
see some of the data crunching that may be happening behind the scenes. And in the case of Data Selfie, it's unfortunately
no longer available to download,
you can't use it anymore, because they've stopped paying to access the APIs that made it work and produce visualizations like this.
But in brief, Data Selfie used APIs, including the Apply Magic Sauce API from the University of Cambridge. And this particular API
helps use 'Like' engagements from Facebook to predict psycho demographics, and this is very, very readily applicable to
What happened in terms of Cambridge Analytica data and the Cambridge Analytica Scandal, and so I find it really fascinating
that people were building tools,
to help users take a look under the hood and understand what was happening,
well before the Cambridge Analytica Scandal hit, but that there's a much longer effort and then people have been aware of these issues
for quite some time, so this is definitely one extension that I think has a lot of significance in relation to algorithmic literacy
and some possibilities of what we might do to try and increase it and to support it.
In terms of other work, if I could get you to forward to my next slide,
What can we do? So Leslie's second question for the panelists was: What do you think are best practices for integrating algorithmic
literacy into media/digital/data literacy programming?
And I don't think that there's really one thing that we can do, or one path forward, I think there's probably many interventions that will
be required.
But I like to try and leverage windows of opportunity, and I'm building upon John Kingdom's work around policy windows,
and I think there's a lot of opportunity to see and find policy windows and then also to build in and use Open Source technologies
to try and leverage, or to try to enhance or build the kind of future that you hope to see in the world. And it's by no means perfect and it is
a little idealistic, but it is a step, it is something that can be done to intervene.
And just as a couple examples to wrap it up, a couple of projects that have inspired me recently. They're on the next slide and,
I really love Tomo Kirihara's, I think I've got a typo in the name I apologize I've fixed that on the slide deck later - typo in the name -
but TheirTube is a really fantastic project that lets you kind of look into different filter bubbles, to see what kind of content
you may see if you're a Fruitarian versus a Climate Change Denier and,
I see a lot of value in these kinds of creative expressions and tools, and I was also really fascinated to see yesterday
that Amnesty International, they're calling people to help map where cameras that are involved in facial recognition
really biometric data collection, where they're located around New York City.
I do not know this, but I highly suspect that they're scaffolding upon kind of open source mapping tools to do this work.
And these kinds of examples and their connections to kind of open-source software development are something I find interesting
but a little bit hopeful in a space where there are many persisting and problematic challenges. So I'm going to leave it there,
and I look forward to continuing the conversation.
LESLIE SHADE: Thanks Karen.
It's fun to think about all these projects, creative initiatives in order to see where algorithms are going or not.
Elisha.
ELISHA LIM: So, yeah I really want to thank
Leslie for organizing this panel and it's really exciting to get together and talk to everyone else on this panel whose ideas
I can't wait to engage with.
I think that, in particular, Jonathan, I really liked
your quote about the fallacy of privacy self-management, and that's something that I personally really seek to combat.
Especially the emphasis on privacy and contemporary algorithmic literacy curriculum that's currently recommended to elementary and
high school education and that's what this slide represents.
Media Smarts Canada's Centre For Digital Media Literacy, and is recommended currently by OISE for implementation in
elementary and secondary schools
in terms of its algorithmic literacy model. And it focuses very much on privacy, privacy awareness, data collection transparency,
user self protection and data control.
And I would actually argue that very much like Monica pointed out,
This doesn't teach algorithmic literacy, maybe falls more into the category of data literacy. So I define algorithmic literacy
as an awareness of how a platform organizes human activity, not just the content, not just the images or the information,
but our activity, and then which values this emphasizes, and what kind of norms this produces. And, finally, the critical skills to evaluate these norms.
I really want to drive home the importance of this definition, through a parallel example of traditional critical literacy that I hope is fun
I'm going to use the case of the notorious 2017 Pepsi commercial starring Kendall Jenner.
So you might remember this to quote the New York Times, Pepsi apologized for a controversial advertisement that borrowed imagery
from the Black Lives Matter movement after a day of intense criticism.
The ad shows attractive young people holding signs with nonspecific pleas like 'join the conversation'. And in a climactic scene,
a police officer accepts a can of Pepsi from Kendall Jenner, the youngest Kardashian, setting off ruckus approval.
his image was compared to a widely shared photo of it Ieshia Evans, a black woman who stood firm, while being charged by
riot police during a protest against police brutality in Baton Rouge in 2016.
Critics accused Pepsi of appropriating imagery from serious protest to sell its product, while trivializing the widespread protests
against the killings of Black people by the police.
So, arguably, what the New York Times article is doing here is critical media literacy.
According to Ontario's elementary school curriculum, which has a stellar definition of critical literacy, I thought,
'Critical literacy goes beyond conventional critical thinking, by focusing on issues related to fairness, equity, and social justice.
Critically literate students adopt a critical stance, asking what view of the world the text advances, and whether they find this new acceptable.
I would argue that as a parallel,
the algorithmic literacy that has gained traction and is promoted by organizations like Media Smarts, might teach students
that if you watch this ad, Pepsi might be using cookies to track your actions.
Or that this image is an ad and not footage of a real protest.
My point is that algorithmic literacy must do so much more than merely instruct people about how to protect their privacy or recognize misinformation.
And that such a curriculum teaches technical skills and like Jonathan argues potentially a fallacy, but it doesn't teach critical literacy and certainly not critical algorithmic literacy.
So algorithmic literacy, it can and I think it must teach users to recognize the values that algorithms
embed in a platform, not just how they curate information, how they structure user activity and what kind of norms this promotes.
In the sense I align with David Buckingham's 2010 argument that digital literacy must evaluate culture and values.
I also build on computer scientists, I know Jonathan that they dominate these conversations, but I love Philipp Eger the computer scientist who 30 years ago explained algorithmic capture.
That too much yeah, Eger argued that too much attention was being paid to algorithmic surveillance, privacy infractions, and a fear of big brother, this is 30 years ago and it's still so true.
That's only half what algorithms do he argued, the second potentially more impactful half is algorithmic capture, which is the deliberate reorganization of human activity.
Eger explains that engineers identified domain specific ontologies and variables based on existing human behavior and then create algorithms that formalized our online activity.
And these algorithms are then apply to standardize and enforce this formalized version of activity through incentives and disincentives.
Eger explains the purpose of these algorithms is to organize actions into parsable units and these units are recorded in taxonomies
and what I think is interesting today is that these taxonomies are the platforms product.
And of course what happens when users conform to these taxonomies on a global scale, scholars have recorded two dimensional performativity
naturalized forms of discrimination, the internalization of market norms, governmental compliance, the commodification of affect
reputation anxiety, deepening political polarity, and an intimately competitive attention economy.
Moreover, as a testament to the power of algorithmic norms self promoting behavior that was once criticized as narcissistic is now taught as a proficiency in mandatory Ontario high school career courses.
So to summarize my definition of algorithmic literacy combines algorithmic studies
with a standard definition of critical literacy as an awareness of how a platform organizes human activity.
The kinds of values and norms this produces and the critical skills to evaluate the view of the world that algorithms advance in relation to fairness equity and social justice.
My brief answer to the second question of best practices for integrating algorithmic literacy is maybe more optimistic.
An idealistic.
I don't agree that algorithmic literacy needs to teach a thorough competence and understanding of every algorithm.
Just as I don't think we need, for example, to understand what kind of executive conversations went into making that Pepsi ad, but we do need
to emphasize the ability to recognize the norms that algorithms promote and how that impacts our lives, and I really loved Karen's
suggestion about finding joy, so this is my framework that I call radical algorithmic literacy.
And it comes in a set of seven questions that could be modulated differently for different context and I think these could be very elementary questions or very complex sophisticated questions.
What are the platforms incentives, what are its social incentives, what are its technical incentives, what kinds of behavior do these incentives encourage, what do they discourage.
What kind of norms do these behaviors produce and then, just like finding joy, do these norms make me happy, are these values that I cherish, and this question is to emphasize situated rather than standardized literacy.
And finally, the radical question number seven what do I want to do about it.
Yeah and so that's my answer to the two questions and here are image credits.
Take care.
LESLIE SHADE: Fantastic
Thank you, wonderful
I've got a smile on my face because I think joy is excellent. I've got a smile on my face from all the presentations.
There is a chitchat here from
somebody in the audience that I'll read hey Michael Ridley.
I'll read this if you don't mind, if I think of traditional literacy reading and writing the emphasis is on both consuming and creating.
Most of what I've heard from the panel algorithmic literacy is about critical consumption, what about the skills and
expertise to create and use algorithms, isn't this also an essential piece of algorithmic literacy.
Excellent question.
I agree with you Mike.
Panelists who want to comment about Mike's question.
If I can give it a shot.
Mike how did you enjoy my presentation, I really, that is exactly what I tried to address, not just the content that we're digesting but the ways that algorithms are
shaping the way that we behave and the norm is that that develops and do these norms make me happy did that answer your question.
MICHAEL RIDLEY: Yeah I think in partially it did, I though we actually need to think about actually constructing algorithms.
Actually being able to build them in a sense of writing is that flip side of reading.
And it's the externalizing ideas and developing a power through writing, I think creating algorithms
is just another piece of that and I say this because, and not to disparage any of the comments, but often
folks like us, who are working in the social science perspective tend to shy away a little bit from the technical details of
how actual algorithms actually work the techniques and tools and the reason I think that that needs to change is a lot of the technology around creating algorithms are becoming virtually desktop
applications they're kind of like, if I could make an analogy, the word processing of algorithmic creation and I think
if we want to change the impact of these algorithms the way that we've all just discussed, then we need people
who can create them differently as well, so I guess I think what you were saying was in that sort of area, but I just want to clarify a little bit.
LESLIE SHADE: Thank you.
Thanks for that comment Mike, any, Karen do you wanna.
KAREN SMITH: Yeah I just volunteered to respond in the chat, thanks so much for that comment, I know
in relation to my own comments today, I think the reason that I didn't get that far was
there's so many steps that might scaffold up towards being able to write algorithms and it's still
to me often it feels like there's things to be done first, before I might get to that point in my own work, but as you were speaking I was kind of imagining what
many of you might be familiar with the game development program an app of scratch, where you can basically build a game by dragging and dropping
different pieces and I can entirely envision that that kind of tool could exist in this space to make it really accessible for
anyone, including children to
design an algorithm, to me what's important is not removing the step, where people think about whether data should be processed
and whether an algorithm should exist for a particular purpose but I absolutely take the point that writing algorithms and understanding that
citizens should and could have agency in determining what those algorithms do is an important point and important contribution around algorithmic literacy.
LESLIE SHADE: Yeah, and that gets to one of the questions that I had for the panelists was you know.
Is around algorithmic transparency okay if you're building them and creating them and knowing how they work, you know it does that contribute towards
algorithmic transparency so might this making and doing be some of the principles in terms of encouraging algorithmic transparency.
And this is a way of making these systems accountable for those that it might impact, so that might be one way of thinking about that.
TAMARA SHEPHERD: Can I ask something.
LESLIE SHADE: Absolutely, please Tamara, yes.
TAMARA SHEPHERD: I just want to first thank everybody on this panel. This is like such an interesting discussion and I appreciate all of your perspectives.
And, as I have like a lot of questions but I'm just trying to narrow it down to one for now, and maybe ask more later, but first.
Something came up in Karen's presentation, Karen when you were describing the privacy sweep method, I think this is really interesting and it led me to think like
What level of expertise we're sort of talking in general about users, it seems, but what about regulators like what level of expertise do regulators need to have, and I know I'm bringing this back to the conference theme
of regulating platforms and how do we deal with platform regulation which no one really knows the answer to but we're trying to
trying to get into and how much do regulators need to know, like all this stuff has come up around bill C-10 right, right now, like the most immediate example that comes to mind.
And it's all kind of like based on sort of ignorance at the level of of
the politicians who are crafting these legislations so I'm wondering if the panelists would like to comment on this idea of
literacy expertise for not just general public or children, or you know, whatever whoever it is but regulators specifically.
JONATHAN OBAR: Yeah thanks for the questions.
And I'm teaching a class called fake news and fact finding at the first year
level and I've spent a lot of time the last two years looking through information literacy materials.
And as I've been saying, as I say in class, you almost need information literacy about information literacy, to be able to figure out
like which literacy model you want to use in which situation, and so I think to answer both questions, one thing that I think is helpful in terms of framing the conversation is that
there are different elements of the literature that focus on literacy as skill development and literacy as knowledge development.
And I think
the Canadian Government, for example, has been criticized for its conceptualization of digital literacy is focusing mainly on
like teaching people how to build businesses online.
While not teaching them about you know literacy as knowledge types of things that I think most of us are talking about.
There is overlap, there are ways that you can learn by doing, and that I think is something that's important to keep in mind.
But I think if the question is, what should we be focusing on that policymakers
should really be learning about in terms of algorithms and algorithmic transparency, I think we should be focusing on the literacy as knowledge space
and making sure that people who are making policy understand the dangers in particular
of algorithms, biased algorithms, problematic data sets that algorithms attempt to analyze.
And then of course problematic decisions that result from using algorithms to interpret data sets to then get to
decisions, so I would say that making sure that we're focusing on sort of like the different types of literacy that emerge in the literacy as knowledge space.
KAREN SMITH: Hi I would love to chime in and add on a little bit here. I think the question around really how regulators may be able to respond to this.
I think that in my ideal world, I would really like to see the possibility for
data and privacy related offices, so let's say at the federal level in Canada, the office of the privacy commissioner of Canada, I would love to see them have some kind of
short term, agile hiring process for technologists on particular issues of concern so
Basically, this idea stems from my concern that it's challenging to hire a technologist who can possibly understand every issue that may be under review and
I imagine that there's the need to hire someone who's an expert in facial recognition technology
internet of things, other people who are experts around encryption, or signals and
Those needs to conduct the inquiry and to conduct the research into the privacy issues, I think there's probably kind of
A different combination of individuals that are needed to tackle various problems at various moments in time.
In a different kind of sector consumer reports recently had a call out, they were hiring technologists to start working to
conduct privacy assessments of various technologies, and I can imagine that
while you would want to have long term staff and that that would be important that there would also be the need for shorter term
agile hiring to happen to really do the work that needs to be done to interrogate and investigate.
Monica, your comment in the chat sort of segues in terms of thinking about you know how do you build robust
interdisciplinary teams that share the various knowledges that they have from different
realms of expertise and you know we emphasize interdisciplinary a lot, but does it actually really
mobilize and actually work in practice, but do you have anything you want to comment about your
question or your comment in the chat Monica.
MONICA JEAN HENDERSON: Yeah I guess just that, like I don't know about all of you, but I'm already overwhelmed
trying to understand how to just consume algorithms, I do not have the time to learn how to write them.
It's not that I don't think it's important I just think that you know, there are people who know how to do that and so why not encourage collaboration with those people
instead of trying to take, like everyone take everything on, however, it is really
hard, I think like a lot of just like academic protocols like disallow that kind of interdisciplinary like
I've even like suggested to my partner who's an engineer that we write something together and he said yeah I don't know if my supervisor would go for that
Just because
LESLIE SHADE: Go for it, I say go for it.
MONICA JEAN HENDERSON: Yeah they have to publish in certain journals and spend their time right you know
And so yeah I think there's a lot more layers to just like are we learning to write them or not, I think there's a lot of things
inhibiting us from doing that and I will just reiterate, but you know, this is a challenge that I think about a lot in again with the population that my research focuses on with adults with low
literacy is that you know we are trying to read and write just regularly first
and then where do we go first, do we go to the technical side or the consumption side, what is the most useful to them immediately.
Because you know it could be a very time sensitive issue of like how they're interacting with service Canada or you know applying for their CERB.
So yeah it's a really important question I don't think there's like a quick answer.
Is that a satisfactory answer Tamara.
It's complicated, yeah it's complicated, any other panelists want to jump off of that before I get to some of the questions in the chat, Elisha.
ELISHA LIM: Yeah I mean maybe, so
I really push back against the idea that understanding the problems of algorithms is understanding how algorithms work.
And I really build on Taina Bucher's definition
or her approach to algorithms which she conducted ethnographic interviews and she called it, the socio materiality of algorithms as their most valid product.
It's not about the engineers and what they're doing it's about the results, the consequences, how we're behaving, you know so many people express especially my students at school express
the incredible reputation anxiety that they feel, they're afraid to speak in class
because of algorithmic ranking that happens on social media and that penalizes them, and the only solution we have right now is more privacy, that's not going to solve the problem.
The problem is the algorithmic organization and ranking that's really around
building better classifications of customers it's really about these commercially driven algorithms, and so I think that the best place for policymakers to go
to understand algorithms is not the engineers, but is this socio material products, and by that I mean
interviewing the people that are impacted by it, you know how anxious are you, how sad are you what kind of impact
has this had on your community, on your activism, on your cohesion, on your ability for solidarity.
And I think, as with so much policy it's most effective when it centers people most impacted by it.
So that's really what I push for, not algorithmic technical understanding, I think that's a fallacy.
I love your word Jonathan, I'm running with it.
LESLIE SHADE: Thank you Elisha, I'm just going to mention a comment that ME put in and then get up to
Christine's question because it's seven minutes after seven and we have about eight minutes but yeah ME you talked about
advisory groups, so government frequently puts together advisory groups where they have various folks with specific expertise, so they could be technologists, humanists, social scientists
members of civil society, etc, and often they hire short term so yeah that is one way of thinking about
building up that disciplinary, or interdisciplinary base for sure.
You know, go back to the 1970 the tele Commission reports and there where poets, there where poets that were on that commission which is really kind of cool.
ME LUKA: I think every committee, every commission should have an artist on it, because you need it.
and that's, yeah.
LESLIE SHADE: Yeah great, so Christine.
LESLIE SHADE: Yes, Monica did mention methodological gaps about digital literacy, so you are asking a question specifically about the last of the round table
questions and you say in terms of the methodological making and doing of digital literacy research, what can be done in digital literacy.
What can be done in digital literacy to become reciprocal with the racial and gender scholarship
from which it draws to enrich itself, how do we position your methods to be non extracted and circulate knowledge mutually with contributions
of racialized and gendered scholarship not directly tied to info literacy, an excellent question, thank you so much.
JONATHAN OBAR: So I may not focus so much on the methodological component, but happy to
to start here and then maybe move into that direction, I think that any approach to solving some of
JONATHAN OBAR: the challenges we've addressed requires what we call distributive justice approaches to policy making.
It requires acknowledging that certain individuals, certain communities are most likely to be harmed by big data discrimination.
And so policy efforts
must, whether it's through the design of support mechanisms
The, such as the development of infomediation services.
That might help individuals handle, address their reputation for example, the reputation management
industry is emerging, whether it's direction for information fiduciary relationships
which might connect to what we were talking about briefly about these expert panels that the government is putting together to
address ethical algorithms, whether its funding for research or
funding for advocacy or whether it's the direction of our methodologies.
Distributive justice approach is drawing from you know Rawls and Sen, which acknowledges
that equity, not equality, should be the focus, I think is what we need to really be emphasizing and so.
As we I think struggle to try and figure out what the best policy approaches are, I think always we have to keep in mind
this again distributive justice approach to whatever it is that we're doing.
LESLIE SHADE: Thanks Jonathan, any other panelists want to
take up that question, I also think there's a lot of participatory methods that are being developed
with communities themselves, so it's not a top down process it's a bottom up and there's some interesting examples.
Monica is working on this annotated bibliography on algorithmic literacy and it's big and it keeps
growing but there's a whole section on looking at various different sorts of toolkits that have been developed in different ways that involves
really inclusive, participatory methods, so I think that's one way of going for sure.
ME yes, Elisha's questions are a good example of an ethics of care that promotes inclusive research processes, yes.
ELISHA LIM: Yeah I can maybe add to that.
LESLIE SHADE: Please do.
ELISHA LIM: Thanks so much for that ME and to Christine, what I've been working on in my research is really emphasizing the importance of combining critical race
and critical social media scholarship, so I would argue, critical race scholarship and communities are not outside, there's not a need for reciprocity when we're inside right so.
I think that this is such an important combination because
as I was explaining in my personal definition of algorithmic literacy, I emphasize the questions of what are the values and norms that platforms circulate and here critical race.
and gender scholarship are so essential for evaluating these norms, so I think this work can be done without you know these scholars being central
to the work and, if I can also add I'm so interested in the question of how algorithms connects to moral rhetoric.
And I'd love to hear what the panel thinks about that because that's another place where I think it's so important to be working intersectionality.
I'm thinking of the rise of corporate social responsibility, for example, recently that has blossomed alongside social media
and the idea that you know it's profitable to be moral and moral to be profitable.
And we see how algorithmic ranking really promotes you know solidarity statements right now with Asian communities.
And so yeah, in this case I think there's so much about like legacies of colonial capitalist exploitation
of social difference that algorithms absolutely promote and that make critical race so central to the discussion.
LESLIE SHADE: We're all nodding our head in agreement, like true, yep absolutely, anybody want to comment on that.
And it sort of gets, we have two minutes left in the panel, Erica's question about
potential challenges we might face, lacunas in the current scholarship or methods that could be effective.
So sort of was wrapping in response to Elisha's and Erica's comment, anybody want to take that on.
MONICA JEAN HENDERSON: Yeah I can talk about that just because that's something I care a lot about, and so my
The initial paper that got me really interested in methodological gaps and evaluating algorithmic literacy was
It's Hargittai et al. from last summer.
Black box measures, how to study people's algorithm skills in ICS
and there's a really interesting acknowledgement there that's like you know how do we evaluate
algorithmic literacy when the researchers themselves don't even necessarily know what's going on in there and so
I think that relates back to Michael's question of like how much that technical knowledge is important, but like you know
to what extent do we need to know that in order to make claims or evaluate others algorithmic literacy or
You know, should it be actually the technical experts who are evaluating this and not us, like I don't think that it's one or the other but it brings up a lot of questions.
But I think most important is like just kind of reiterating what I
brought up before about you know what populations, like Erica mentions in her question, we are
including in this conversation because, like Elisha says, critical race and gender and
class are very heavily implicated in you know who's most heavily impacted by algorithms and yet you know Hargittai and
other authors are you know snowball sampled from like their Twitter feed, and you know
the people following Eszter Hargittai aren't necessarily the people who are having like their medicaid taken away like in Virginia Eubanks's book right.
And that's just making a very broad assumption of course but I think we just need to be very intentional about how we develop ideas
about what algorithmic literacy is around like the populations that we're including in that conversation because right now
it's very easy to have very high level discussions about why it's important where it happens.
But you know, on the everyday, who is really at risk of suffering or
You know, being excluded from certain processes or included in the wrong processes because of this, and what is the most like.
Yeah to evaluate their skills I think is, you know, not a question or answer I really have a full fledged idea for Erica
but I think it is really important of yet just being intentional about choosing those recruiting populations, yeah.
LESLIE SHADE: Thanks Monica.
So unless there's any burning responses to that or any burning questions from the audience.
I don't see any, I'd like to thank the panelists, Monica, Elisha
Karen, Jonathan, I'd like to thank Tamara and David and Sara Bannerman
for being part of the organizing committee and thanks
LESLIE SHADE: for, you know, all sorts of folks for asking the great questions, Erica, Mike, Christine
excellent, and you know to be continued, to be continued 2022
you know in Hamilton itself maybe
would be great, so thanks everybody, I appreciate it, a good time was had by all, thank you.