Welcome to the Communication and Cultural Policy conference, the session on algorithmic,
digital culture and algorithmic governance.
We have some exciting presentations lined up today.
And our first presenter is Alex Mayhew, who is a PhD candidate at Western working with
Grant Campbell.
His project is creating a new cataloguing paradigm that he calls "final metrics," which
would file a metrics catalogue, allowing users to create links between climate or between
catalogue records and add meaningful connection.
So, I'll let Alex talk more about that project and their presentation today, the Social
Selection of Algorithms.
Hello, my name is Alex Mayhew,
and this is my presentation on an idea that I call "Social Selection of Algorithms."
All right, so let's give you an introduction.
This idea is based off of an idea that was generated during a research project with Danica
Pawlick-Potts in which I was a volunteer interviewee.
So I want to thank her and the research team for giving me this opportunity to engage with
this topic.
The place I ended up is a speculation based off an analogy built on an abstraction
built on a brand new field of technology.
So some scepticism of where I end up is is a bit warranted.
Also, I want to thank those researchers, but anything that is amiss in this idea is not
their fault.
All right, so here's the 30 second version.
I want to make the case that explainable AI, XAI systems, explain the decisions or
reasonings of post AI systems. There's a great deal of interest in their potential. There
will likely be, one day, many XAI systems, and there'll be so many that'll form something
like an ecosystem.
And like biological ecosystems, there will be evolutionary pressures put on various entities
within the ecosystem.
In particular, the next generation of XAI systems will be selectively based off of existing
XAI systems. Users will select for explanations that they've personally find credible, which
often means how well the explanation coheres to our existing beliefs about the world.
And that sort of feedback will be part of what algorithms get selected as the basis
of the next generation.
And thus, through no fault of our own with no malicious people in the situation,
there could be, but there doesn't need to be,
and that's my point, we will be training our XAI systems to generate explanations that
diverged from our best understanding of the actual events.
In other words, we are training our XAI systems to deceive us to one degree or another.
So let's go through this whole thing.
So as I say, place I end up is a speculation built on an analogy, but then an abstraction
built on a brand new field of technologies.
So let's start with that.
Explainable AI is a type of AI itself and AI is generally thought of these days as a
"Black Box."
How it achieves its results are not fully understood.
They use machine learning neural nets, and you can look inside, but it's usually beyond
human comprehension.
And, yeah, explainable AIs are special, dedicated black boxes that are supposed to look inside
other ones and tell you what's going on.
Sometimes they look inside themselves too. Whether or not they're one machine or two machines is
not really relevant for my purposes.
So this is what is supposed to be the case with XAI systems we have today.
With this delightful chart from DARPA, we have our training data, our machine learning
process and learn function, and it produces this decision or recommendation,
and we don't know why it does that.
In comes the XAI system that tells us why that decision or recommendation is reached,
and now everyone's happy, and we can all thank DARPA. Let's use another example here.
We have an AI system for bank loans.
Do you get your loan, yes or no?
Right now the user's neutral, waiting to find out.
User gets his loan, he's happy, no problem.
What happens when the user doesn't get their loan?
Okay,
the user would possibly want an explanation for why their loan plus request was denied.
Fair enough.
What happens right now?
So if you're lucky, a human will review your case and reach a different decision, maybe, or
a human programmer will look inside the bank loan AI and figure out what happened.
Maybe there's usually again beyond comprehensibility.
But maybe.
And there might be other options as well.
But these options always cost time and money, and time and money that the AI system was
supposed to save in the first place.
So what do we do?
Well, XAI is the offered solution.
So the user got their no decision.
But the XAI system comes along and provides an explanation what goes on and the user isn't
thrilled.
But they're mollified, or they're better off than they were.
So let's move on to the abstraction.
Contemplating how the AI-XAI-User interaction works is important.
So that that chunk is very important.
But it's not the only level of abstraction, that challenges might emerge.
And the key notion here is that there are likely going to be more than 1 XAI systems
likely a great many.
You could imagine companies whose whole business model is providing XAI systems, different
generations, different versions, what have you.
Okay, so now we have an XAI ecosystem.
But why will there'll be many?
Well, multiple organizations provide XAI service, different versions will overlap
time.
And for development purposes, it's really useful to deploy multiple different versions.
The Google algorithm does this all the time.
So we have reason to suspect there will be many.
Okay.
Let's describe this XAI ecosystem a little bit more.
So there'll be multiple XAI systems operating in the same conceptual space.
The next generation of XAI system could well inherit some aspects of the current version,
like programming architecture, or training data, maybe even the neural net itself, but
more likely, just the parameters that are going into it.
Only some of the XAI systems will be selected to serve as the template for the next generation,
the actual situation's, very complicated, but that's, I think, a reasonable simplification.
And so the XAI system, the different versions that exist at the same time, do sit in something
of competition with one another to pass on
their architecture, or their inheritable characteristics
to the next generation.
So let's let's build that analogy.
In an XAI ecosystem, we have the makings of an analogy to biological evolution, various
species of XAI eat with each other to pass on their genes.
But what exactly is being selected for? What's causing us to choose one over the other
to build the next system based on?
Well, there are many selection pressures on the XAI systems.
There's the element cost, setup cost, the energy costs, the speed of the system.
But also importantly, user reactions: how the user gives feedback what type of feedback
the user gives about their experience with the system.
And user reactions can take many forms.
After experience surveys, complaint sent to higher up people voice stress or facial monitor
an expression monitoring is frightening.
But yeah, that's probably coming.
And this does raise the question what sort of explanations do users prefer?
And this is where I suggest by looking at some of the research, cognitive science research
that humans typically prefer coherence over proof.
This means that XAI and users judgement of explanation quality is not necessarily based
on the reality of how the original decision was made, but is instead based on how convincing
the explanation is.
This suggests that explanations that are consistent
with user expectation, expectations and free
of noticeable contradictions are more likely to be accepted,
even if that explanation is not an accurate fit to reality.
Truth is stranger than fiction. Fiction, after all, has to make sense.
So say the writers.
So, let's look at the speculation.
I claim that we will be training our XAI systems to deceive us, how will that happen?
Let's bring all the bonuses together.
So we have the bank system, the AI system, bank loans that has what happens inside of it.
And that's
A: whatever mechanism goes on.
And XAI System 1 tells the user that that's what happened.
A is what decision was made.
But user likes D. D would be something that they've had in mind.
So System 2 looks at the personal information that it can see about the user inside the
bank loan system, because you have your personal information apparent path to in order for
the bank loans AI to process your information, and can get so the XAI 2
system and guess a bit about what you are like and generate an explanation that's tailored
to you.
And it gives you B and now the users even more qualified and likes explanation B better.
So XAI system 2 will receive more positive feedback and thus be more likely to serve
as the template for the next generation.
This is what I call the social selection. It is distinct, but analogous to natural selection.
And selection effects are very powerful, especially
given enough inter iterations.
But they can also be very hard to notice.
This seems likely to result in a Malignant Failure Mode, an undesirable outcome that
is not caused by any malicious actors.
The problem does get a bit worse.
So as I said, we can read the person's information from the XAI system and read the person's
information directly from the bank loan system.
So not only does it give a generic answer, B, that is more likely to be accepted.
But it's specifically tailored to that specific user.
Now, the user can be really happy to have gotten a no answer, which is suboptimal.
So earlier, I suggested that explanations that are consistent with user expectations
and free of noticeable contradictions are
more likely to be accepted, even if that explanation
is not an accurate fit to reality.
But the most effective lies are often half truths. XAI systems could preferentially
focus on some factors without paying others— thank you to Mike Ridley, for pointing this out—
And this sort of manipulation would be even more difficult to detect.
And no one involved needs to have set out to make XAI systems work this way.
They are socially selected to work this way.
And what can be done?
I am genuinely not sure.
I have suggested potential technical solutions
and social solutions like education and legislation.
I genuinely don't think any of these actually are able to operate at this ecosystem level.
And I'm not sure what could.
But I look forward to hearing what you might think of it,
and I look forward to our conversation.
Thank you.
Have a good one.
Thank you so much for that really fascinating
presentation, I had no idea that XAI actually existed
So it's really a door, a window into a new area for me.
So thank you for that.
I think what we'll do is we'll go through the different presentations, and then we'll
have a broader Q&A at the end.
And there is the opportunity as well, because some of these presentations are pre recorded.
There is the opportunity to just ask questions live and the presenters can actually, they're
not presenting live, so can present or can answer them in the Q&A.
But our next presentation is live.
Robert Hunt is a doctoral candidate at the Department of Communication Studies at
Concordia University.
And he's presenting a paper that's co-authored with Fenwick McKelvey,
who's an associate professor
at Concordia.
And their paper is called
Algorithmic Regulation in the Era of Platform Governance.
So, I'll let you do that presentation now.
Robert, we got about 12 minutes.
Okay, thanks, Sara.
Let me get my screen shared.
Okay.
Thank you all for being here.
And a special thank you to Sara for chairing the panel and for organizing this conference
twice.
It's very impressive.
To start, I do want to pass on Fenwick's regret that he couldn't be here with us.
This presentation is something of a synthesis
of work we've been collaborating on for several years,
and it's serving as the ground for new research into the growing use of artificial intelligence
in media governance.
The shared project started with Fenwick
observing that the term, "discoverability" has become a key word for cultural policymakers
and cultural producers in Canada.
Discoverability was a way to talk about how digital platforms had changed the way cultural
productions find or don't find audiences.
Outside of policy circles,
discoverability was usually framed as a problem of marketing and publicity, of learning new
competencies to better compete in the online attention market.
But in our view, discoverability is better understood as a form of media power.
Platforms use this power to design and influence user experience, helping to shape distinctive
modes of discovery like binge watching and doom scrolling.
As we wrote in a paper on this topic in 2019, today's media consumers may have more choices
online, but their agency and the logics of
interactivity itself are constrained in important ways.
We started that paper with the story of rapper Rory Fresco. Fresco enjoyed a brief turn in
the limelight when SoundCloud queued the unknown musician's song Low Key to autoplay after a
brand new Kanye West track.
As a result of the platform classifying the two songs as related, millions of listeners
automatically discovered Low Key, and as a result, Fresco was signed to a major label.
A few months later, SoundCloud changed this
feature to serve users individualised recommendations
based on their past listening behaviour, which essentially ended the possibility of another
Rory Fresco.
We liked this story because it captured the way that algorithms and their optimizations
seem to be up ending the normal means of promotion and career advancement in the culture industries.
A platform optimized for relevance means Rory Fresco
ends up working with Timbaland.
But a platform optimized for individual
listener engagement leads to a very different outcome.
Last week, a different story about platforms in the music industry caught our eye. Bloomberg
released a story about the strong and surprisingly intimate ties between TikTok and the platform's
most popular creators, as well as the music industry.
The story showed that the platform works directly with the individual stars to help them reach
users, take advantage of trends, maximize views, and go viral. Among less successful users.
TikTok's algorithm is notoriously opaque and unpredictable.
Even relatively popular amateurs on TikTok struggle with figuring out how to get in the
recommendation system's good graces.
While they differ dramatically in ascending agency, both of these stories testified to
the power that content platforms have over the circulation of culture.
Power the puts has put them on the agenda of media and cultural policymakers.
As TikTok shows, this power doesn't always originate in algorithmic processes, but it
is often made to work through them.
In our view, the algorithms that filter, rank,
and recommend content on these platforms essentially
automate as well as customize media and cultural policy.
They also privatized.
Platform algorithms serve as a technical means of calculating complex matters of cultural
reception and interpretation, such as relevance, taste, enjoyment, and even one's personality.
By attempting to measure these qualities, and then automatically modify users' experiences
in response and at scale, algorithmic systems regulate their users' access to an
experience of culture.
How they respond depends on what they are optimized for.
In other ways, a way of thinking about this is that algorithms, algorithms are instruments
of regulation.
While they're optimizations act as policy, setting goals for the system and providing
benchmarks to judge effectiveness.
While we find this analogy suggestive, the line between
the two is blurry as it is in formal policymaking
and the two feed back into each other.
We also believe this analogy illuminates what's at stake in allowing the platform's to continue
to regulate themselves, exposing a profound lack of accountability for algorithmic or
artificially intelligent systems.
In the classic text, Doing Cultural Studies, Stuart Hall and his collaborators identify
five moments in the circuit of culture that allow researchers to carry
out a comprehensive analysis.
Inspired by this framework, we identified three moments that provide opportunities to
analyse the accountability of algorithmic systems, inputs, code and contexts.
These moments occur simultaneously.
But distinguishing them helps to clarify which agents make decisions, who or what decides
whether these decisions are high quality, and how these practices might be made more
accountable.
You deserve totality.
These moments make clear that development and deployment matter in terms when it comes
to algorithms. Policies that seek to hold algorithmic systems accountable for their
outcomes must address both.
In the following section,
I'll elaborate on this framework by considering some of the features of algorithms systems
that present challenges to public accountability.
So in inputs we have proprietary data, meaning that the data used in algorithmic decision
making is often confidential and not easily accessed by public institutions.
Then as a strategic
asset giving competitive advantage to companies with the access and ability to aggregate it.
There's also systemic inequity in data.
A growing body of important research has debunked the idea that data are politically neutral,
rather than a selected representation of an already inequitable world.
If used naively, or assumed to be objective, data will reflect, reinforce, and reproduce
injustice.
Personalization: algorithmic filters are usually personalized to some extent.
And so all perspectives on a given system are often just that one perspective.
No single outcome can be assumed to be indicative of the overall system.
And it's impossible for even an expert user to deduce how the algorithm functions in the
abstract based on their individual results.
Similar to that is localization,
where context-aware algorithms vary their outcomes according to geography,
sorting out the different ways it can be signalled by user preference, GPS location, or IP address.
Like personalization, it is also more or less impossible to comprehend an internationally
available platform's local characteristics.
Code, as I've just talked about. Algorithms are often referred to as black boxes, because
you just cannot observe how they arrive at their outcomes.
With the advent of machine learning, we face a situation where developers are in the same
boat. Blackbox signals are often deliberate. Knowledge
of the operation of Google search algorithm,
or Spotify Discover Weekly,
represents a competitive advantage for these companies, and they will not readily
disclose these important corporate secrets. As Jennifer Ellis pointed out, algorithmic
opacity can also stem from unintentional factors, such as the fact that reading code generally
requires special training, or that machine
learning algorithms by definition change during use.
And as Mike Ananny and Kate Crawford have also persuasively argued, really opening the
box does not necessarily bring about accountability.
Traceability means discovering the source of a systems failure, assigning responsibility
for it and punishing, or at least educating, those responsible. Algorithms function as
components in distributed systems where
responsibilities are shared.
Recommendations and other forms of filtering do not result from any one part of the system,
but from the interactions among many.
When algorithmic processes lead to harmful outcomes, traceability becomes
a key ethical concern.
Most platforms constantly develop and release updates to their code.
These changes can occur at scale and often without indication to the user.
As a result, all observations of algorithmic systems must be sensitive to the time and
state of the unstable object of research.
Instability necessitates continual monitoring to detect changes.
Unpredictability: again, this is reflecting what Alex was saying about algorithmic systems,
especially machine learning and multi agent ones exhibiting emergent behaviour that cannot
always be anticipated.
Software bugs and complexity introduce further unpredictability and then compounds uncertainty
about intent.
Devising an algorithm to solve one problem may lead to it inadvertently creating new
ones, such as copyright enforcement or anti- piracy algorithms that block users or
content they weren't intended to police.
And as we all know, I think at this point very clearly, there are structural barriers
within the tech industry.
Algorithmic processes may fail or further marginalize people and communities if they
perpetuate the unequal status quo.
A significant obstacle to ameliorating this
problem is the lack of diversity in the tech industry
But the majority of technical jobs are held by men and the overall workforce is
overwhelmingly white,
meaning that underrepresented voices go unheard and important perspectives
are excluded.
What goals do algorithmic systems hold?
What optimal behaviours
are they trying to nudge users toward?
How do these automated systems determine whether they have succeeded?
These questions concerning optimization and these principles are usually neither public
nor knowable through digital methods.
Algorithmic processes can seem enigmatic or uncanny to users because they don't
know what optimal condition the algorithm is trying to achieve or maintain.
Furthermore, ensuring that algorithmic systems are functioning optimally requires testing,
including gauging the effects of delivering sub optimal services, which can further undermine
users' and creators' trust as well as reinforce inequities.
Assessing the strength of the effects of new technologies like algorithmic filters and
recommendations requires asking fundamental questions about media use. What media do people
consume? When, how, and why? These traditional media studies concerns remain relevant as
consumption and reception occur in new contexts.
They may also help to cut through some of the conceptual noise around admittedly baggy
terms, like platform and content.
When platform companies produce or sell their own content, their recommendation algorithm's
goals may not be in harmony with their users' interests.
On platforms that host a mix of in-house and user-generated content, creators may struggle
to reach an audience.
Vertical integration encourages the optimization of algorithms for corporate rather than user-
or creator-advantage.
As in the TikTok story, classic problems of media concentration and collusion represent
an especially problematic instance of the lack of transparency around
an automated system's goals.
Numerous examples demonstrate that automated systems are susceptible to adversarial users
and coordinated manipulation.
The value of data diminishes quickly if poisoned by trolls, manipulated by fans, or influenced
by new covert forms of marketing.
As new methods of manipulation emerge, algorithmic accountability must be capable of addressing
systems in adverse information environments.
I can sum up the preceding issues in the form of three questions with some sub questions.
Number one, our inputs to algorithmic systems justified, who decides what does and
does not count?
How might data reproduce systemic inequity and historical prejudices?
Who owns or has privileged access to data?
Two: How do algorithms function as forms of regulation?
To what extent should automation even be involved in cultural or journalistic recommendations?
Does private algorithmic regulation enhance or obfuscate public policy objectives?
Three: Are algorithms an appropriate instrument in the known situation or cultural context?
Is the situation understood well enough to estimate the impact of using
algorithmic regulation?
What happens when users behave in unexpected ways?
How to resolve conflicting user expectations? Which users take precedence when deciding
optimal performance?
Okay, so just some fairly brief concluding thoughts to sum up a bit.
The first one is that we're not suffering from a lack of regulation.
As we've shown, algorithms govern the spread and promotion of culture by technical means.
Content platforms are largely left to self regulate their own inputs and code.
While their algorithms following the dictates of their optimizations automatically regulate
each user's access to culture, essentially generating individually tailored, constantly
changing cultural policies.
As governments and corporations embraced artificial
intelligence as a solution to automatically
regulate media access and moderate content, the state of affairs becomes more entrenched
and acute.
These bespoke policies are based not on publicly debatable goals like cultural protection or
the desire to subsidize regional economic independence.
But on data sets of inferred user preferences and the unknowable optimizations
chosen by firms or development teams.
The outcomes of these policies are as inscrutable as their intentions.
Under the current system of platform governance, it's beyond our reach to know whether
algorithmic regulation is discriminatory, radicalizing, or otherwise undermines the values
that guide public policy.
If there's to be a future of democratically accountable algorithms, then it will be through
acknowledging the consequences of them for media regulation, policy, and governance.
And then, finally, government policy is vitally important and requires the kind of intense
scrutiny that the recent privacy bill has been subjected to.
Consumer focused issues like data privacy are also important.
But we believe that creators deserve a greater representation in these debates, particularly
those working outside the traditional confines of the culture industries.
Like other precarious workers whose labour is mediated by platforms, online creators
should be supported in efforts to act collectively,
whether that's in the formation of unions
or less formal projects to keep platform power in check.
And that's it for me. There's just a few other things that Fenwick and I are working on together.
Thanks.
Hey,
you're actually a little over time, Robert.
So thank you so much.
That was an interest of mine as well,
relating to algorithmic recommendation systems. Super, super interesting.
And I'm a fan of your, your past work as well with Dr. McKelvey. Super interesting.
I want to introduce our next presenter.
James Meese is a senior lecturer at RMIT University
in Melbourne in Australia, and he holds an
early career research fellowship with the Australian Research Council.
He also has two books published one is called
Authors, Users, Pirates, Subjectivity, and Copyright
Law, and the other is called Death and Digital Media.
So looking forward to, and thank you for coming so early.
James for this presentation.
Hi everyone, I'm James Mays from RMIT University, and today
I'm going to talk about recent trends around
the algorithmic governance of news.
I'm going to start by discussing case studies
in legal reform from Europe, Australia, and Germany.
Then I'm going to offer a brief working topology to map those reforms before offering some
thoughts on the relationship between these algorithmic systems, and on the business of
news media.
And just before I start, I just want to note that this talk comes out of a project funded
by the Australian Research Council.
So it's become increasingly evident that platforms have an inordinate amount of power to control
the distribution of news. So this became clear to Australians on February 17 2021, when Facebook
stopped Australian users from sharing local or international news.
Facebook had been arguing with the Australian government over a controversial new law and
used media bargaining code that would force platforms to pay for news content.
They regularly threatened to withdraw services from the country,
but it was still something of a surprise when the ban happened.
Overnight, Facebook stopped serving news to all of Australia, and no one really could
stop them.
They only reinstated news after the Australian government made some last minute concessions.
This example points to a fact that scholars have realized for some time now: that tech
companies aren't just intermediaries, but they become critical gatekeepers.
I don't have time to go through the literature, but I can speak to it in our discussion.
And thankfully, governments are not only becoming aware of extensions, but starting to do something
about it.
I guess the question I want to raise in this presentation is whether these interventions
lead to what Hunt and McKelvey have called algorithmic accountability.
So the European Union's reform emerges out of the Digital Services Act, which is an omnibus
kind of reform proposal.
And one of the articles in the act, Article 29, requires
specified platforms to explain what the main
parameters of their recommender systems are, and explain and provide options for users to
modify or influence these parameters.
So in some respects, this is a promising reform.
Individuals have some agencies and some choice, but this choice is necessarily circumscribed.
Users won't have control over a handful of levers that feed the recommender system
they're entitled. This isn't very helpful considering that these systems capture data
from multiple sources.
It was a replicate of previous issues saying privacy, self management, is the assumption
that users can take charge of complex information environments.
Finally, this approach only offers limited accountability. It doesn't set standards or
baseline for algorithmic decision making.
It just forces platforms to make some of their workings public.
Australia has also embraced transparency through its news media about and encouraged.
Reform introduced a standard that required
platforms to give news outlets advanced notification
of algorithm changes, if this was to affect the referral traffic to news content.
This is the corporate standard as Dwayne Winseck has noted about that the board
of reform that I've just given you as privileged information about the workings
of algorithmic systems over and above the general public.
It also presumes an algorithmic dependency that has not yet been proven.
And you know, I would argue that if it's functional,
it may well exacerbate the problem than solve
any algorithmic reliance that does exist.
There's no real public interest logic driving
this intervention. Perhaps more hilariously though,
the entire form is reliant on the minister or the treasurer, designating a platform under the code.
However, the government was only able to break a kind of standoff with Google and Facebook
by agreeing not to make them subject to the code, as long as they handed over money
to news companies.
So currently, this kind of provision about
how it makes transparency sits on the books unused.
Other interventions address algorithmic distribution
from the perspective of media diversity.
Both Germany and Australia have introduced non discrimination requirements or platforms
that are deemed large enough to, should not
be able to unfairly discriminate between news outlets.
Again, the Australian reform requires designated platforms not to choose between news outlets
based on the outcome of commercial negotiations, the idea that, in bargaining and making commercial
deals, platforms can't preference, commercially beneficial publishers over another.
But of course, once again, this just sits on the books and it's not operational.
Because of the designation issue I pointed to earlier, Germany offers a similar reform
but it's focused on scale.
Platforms that are deemed large enough are not allowed to unfairly discriminate.
So in contrast to the above efforts, based on discrimination requirements are useful
reforms, they start to check platforms as public private infrastructures, and regulates algorithms
in a different way.
Rather than focusing on transparency, it focuses on inputs, to ensure that all forms of news at
least get a chance to be read by publics online.
But there are forms that take a passive approach to regulation.
The aim is to ensure that no outlet is unfairly excluded, rather than generally coming to terms
and understand what a diverse media environment and social media could look like.
So we can map these developments and sort of identify rough typology of approaches from
different regions with a focus on transparency, as well as an attempt to produce some rules
on the road for platforms, as [...] the point--- that following standard regulatory
purchase rate infrastructure. However, in other areas,
these reforms vary wildly. Europe
adopts a top down state based approach.
Australia, as prefaced, really on this corporatist transfer of money.
Attempts to regulate recommender systems have really fallen by the wayside here.
What's the most valuable, I think, is on that positive publicly oriented interventions have
right now that in many of these cases, scholars have made some very compelling
calls for this, to ask regulators to implement concepts
like maybe diversity, but they're still under
debate within the field itself.
More critically, in my eyes, is actually interrogate
the presumptions that have inspired this regulatory moment.
One major worry has been the emergence of filter bubbles and echo chambers.
However, these have been proven to be if not false,
then at least on shaky ground. My work on...
and others kind of speaks to this.
In research with my team, we try to explore the
assumption that news companies are dependent on platforms.
Today, [...] ran some traffic to site data that we collected from different Australian
news genres.
During one of the most consequential order and changes in January 2018.
We've seen online only sites print, moving to online public service media, so the Australian
Broadcasting Corporation, BBC equivalent, and SBS, multicultural public broadcaster,
and social newspapers, right in Australia as BuzzFeed equivalents.
We want to focus on the blue, which is Facebook.
And we see that generally, most of these outlets only have a very small amount of
traffic coming from Facebook, a bit higher in public service media, obviously much higher
in social news.
Obviously, there's some traffic from Google as well.
But focusing on Facebook, we can't really see evidence of a sustained traffic dependency
on Facebook here, there's obviously some but it's not substantial.
Google, in many cases is more compelling as a state of dependency.
Overall, though, we obviously, this green area here is direct traffic, and we see a lot of
direct traffic, they will type into the web browser footprints.
For digital natives, I bet less public service media.
There's reasons for that I can talk about in the discussion, really the only complete
state of dependency is social news.
There are some extra categories which are set up a bit, I can talk about that in discussion.
And the point is not to deny that platforms play an important role in the news ecosystem.
But I think to get more accurate about which platforms are important, and when. The Facebook
algorithm plays a role in its distribution, but it doesn't shape the news agenda or news
distribution more generally.
Indeed, from this data, at least around the 2017-2018 mark,
presence on Google Search might be just more important.
I think it's important also to piece together these alternative narratives and not just
rely on accounts for news companies.
These accounts are necessarily contextualized by companies dealing with a declining revenue,
and historical institutional expectations
about their own importance in the wider information
ecosystem.
So as part of this project I'm considering working with media system dependency theory.
While this is focused on the relationship, historically different individuals and systems,
I think it could help us understand this complex web of dependencies that emerge around meso
and macro level actors.
What I really wanted to point out today is that these relationships are not simple.
They're not one way, and they really need to be accounted for when thinking about reform.
I suspect that closer attendances for these sort of dependencies, can help reveal the
systemic processes, and potentially support more nuanced regulatory interventions.
Thank you.
Wonderful.
Thank you so much, James.
That was fascinating.
I'm looking forward to being able to discuss that some more in a few minutes.
Our next presenter is Charlotte Panneton.
Charlotte is a PhD.
student in Media Studies at the Faculty of Information and Media Studies at Western
University.
Her research involves critical approaches to gaming oriented digital media, focusing
on the video game industry and live streaming platforms.
Charlotte is also interested in the cultural history of arcade gaming, and practices of
speed running in emulation.
And Charlotte, your presentation is live.
Live, that's right.
About 12 minutes, then.
Perfect. So, let me just get my screen shared here.
Okay, I think we've got it.
Can everyone see my screen?
Not yet.
Okay.
I think I got it.
Thank you so much. I actually would also like to thank the organizing committee of this conference
for allowing me the opportunity to present on Twitch as a video game oriented live
streaming platform. Twitch often falls to the wayside of discussions about streaming
new media and platform governance, often overshadowed by certain platform behemoths
such as YouTube, Facebook, Netflix, and Spotify.
But what I hope to do with this brief presentation is to use Twitch as a case study,
one that embodies emerging complexities associated with digital cultural production
algorithmic governance, and larger platform regulation.
So Twitch TV is a free gaming oriented social live streaming website that enables users to
stream themselves playing video games and interact with viewers in real time.
Founded in 2011 and purchased by Amazon in 2014, Twitch has since become the
preeminent platform for streaming casual and competitive gameplay with upwards of
140 million monthly users and 9.5 million active streamers.
Game scholar T.L. Taylor has aptly characterized Twitch as a regulatory Wild West.
She notes that, and I quote, live streaming is a fast moving space,
full of change and iteration and evolves at a pace, you can actually keep up with. End quote.
And so, what I want to do here is to briefly trudge into this Wild West
and provide a high level overview of the platform.
And, specifically, I want to address the ways in which Twitch's technological affordances,
content type monetization practices, and game industry interests, as well as its
currently implemented policy frameworks and tools, highlight the emerging difficulties
of adequate and equitable regulation and governance on new media platforms.
So I'm going to start with a kind of stuff that's being broadcast on Twitch.
The most notable and appealing technological affordance on Twitch right now is that of live
streaming itself, which enables a user to publicly broadcast live video and audio
on their own dedicated channel page. So, typically, streamers will
share a live video feed of their gameplay with a composite face cam overlay.
They may integrate various on- screen graphics and features.
as the Stream is broadcast in real time, viewers of the stream can communicate with the streamer
and with each other, via a text based chat function.
This chat function is open to any registered user and is the primary
site of socialization and communication on Twitch.
When a broadcast ends, the Stream is saved and archived via the
site's video-on-demand function, and enables viewers to watch recent broadcasts asynchronously.
And finally, viewers have the ability to clip or record short excerpts of a given stream,
which can be circulated as a short form video across different social media.
And so, by articulating the various audio visual and text based affordances on Twitch,
what I'm trying to do is to highlight the issue of definition and what
affordances should be considered when we approached the question of regulation.
Does a real time public facing live streaming video constitute
a broadcast akin to traditional broadcast media?
Does an archived recording of a stream access via channels' video-on-demand function
constitute an online video much like something on YouTube?
How do we characterize the synchronous chat? Is it
an active comments section, or an old school chat room?
And finally, how do we qualify clips when they circulate as
short form videos like their viral Instagram or TikTok counterparts?
And so the question that emerges here is that of definition and media type.
I also want to address the particularity of Twitch's gaming and video gameplay content. Purchase history,
mission statement, aesthetics, and platform functions all explicitly privilege gaming content.
Most streams on Twitch features some form of video game play to
varying degrees of prominence on stream.
As such, users must contend with personal use rates so that is broadcasting their own gameplay
and external rates, including rates to licenses in game music
branding, asset use, or publisher and post embargoes just to name a few.
And while Twitch is dedicated to game oriented live
streaming, the platform is also used for streaming non gaming content.
The just chatting category figures prominently on Twitch, but there are
also dedicated categories from music production and performance,
podcasting, fitness, and special events, such as trade shows and sport strats.
And although Twitch operates on the premise of fair use,
streamers are still subject to DMCA takedown and copyright claims made by game publishers,
music companies, stock media owners, and other corporate entities. The question, perhaps,
is how do we handle the overlapping types of content and use context found on stream?
I briefly want to consider the dynamics of monetization. So the initial draw of
Twitch is that anyone can stream their gameplay and engage with others for free.
As such, the majority of streamers on Twitch use
the platform affordances to the ends of casual engagement and leisurely activity.
However, there are a subset of streamers via Twitch's affiliate and partner programs
that have monetization options enabled and make
revenue from their activity based on viewer subscriptions, donations, and displayed ads.
The question raised here, then, is how policy differentiates between these non remunerated
versus remunerated tiers of streamers, and if it discerns the difference between
casual or amateur use of the platform versus professional quality or broadcast style streams.
And finally, we should consider how stakeholder interests figure into platform use.
Of course game publishers and developers are actively engaged with Twitch, as their
products make up the bulk of the activity on the platform.
Game companies were quick to identify the
promotional potential and marketing opportunities afforded by Twitch,
as game streams can be used to generate interest for upcoming releases, help sustain interest
for recently released games, and help revitalize interest in older games, as Twitch also acts
as a fully developed, ready-made infrastructure for esports or competitive video gaming.
There is, then, an obvious symbiotic relationship between Twitch and the larger video game industry
that sustains the site to the ends of marketing, promotion, and esports development.
However, of interest here is the question of industry versus user interest:
How do we ensure that the platform remains accountable to its users,
especially if it comes into conflict with the interests of the video game industry?
And, as these complexities emerge, I think we can start to see the policy implications
when it comes to regulation and moderation.
We need to be able to contend with various affordances, content types, monetization and
stream quality, stakeholder interests, and ensuring equity and accountability towards Twitch users.
As it stands, Twitch's overlapping policy frameworks and internal regulatory tools
attempt to handle all this, but to varying degrees of success.
And so, Twitch's first regulatory line of defence is its platform based affordances, including native
algorithmic moderation tools, third party channel bots, and automatic copyright detection tools.
Twitch's framework also relies on channel specific moderation teams and reporting tools.
This is largely due to the difficulty of automatically regulating live streamed content.
Twitch users are always subject to the platform's terms of service and community guidelines,
which are constantly updated to accommodate the dynamics of gaming and streaming culture.
And, of course, Twitch users are subject to any external policy, most notably of which
is the California Privacy Disclosure Act and the Digital Millennium Copyright Act,
the latter of which has been at the heart of Twitch's recent moderation issues.
And while these overlapping moderation tools and policies have been implemented
to contend with Twitch's increasingly complex ecosystem of cultural production,
issues, of course, continue to crop up.
To give a few examples, while streamers on Twitch operate on the premise of fair use and can
freely stream gameplay, their activity can still be flagged
by Twitch's automated copyright detection tools.
These tools have issued warnings for banned users for third party claims on in-game sound effects
and soundtracks, such as clock chimes, police sirens, and in-game radio station music.
Some rules outlined in Twitch's Community guidelines have been inequitably enforced, with
women or people of color often bearing the brunt of unjustified content bans,
trolling, harassment, and false flagging, with little avenue for recourse.
On more than one occasion, cosplayers fitness streamers, and women playing games in tank
tops have had their activity tagged as sexual content, only to be issued extended bans.
Some game companies have gone so far as to dictate how much of their games can be streamed by players
issuing copyright claims if players dream beyond certain in-game events or story points.
Game publisher Atlas issued an embargo on the ending of its game Persona Five
threatening to take action, as streamers revealed the ending in their gameplay streams.
And Twitch's tools and policies often clash, especially in circumstances where its tools
are not able to contextualize the content being streamed. I offer, to this point, this
amazing headline about Metallica's automatically censored performance on Twitch's official channel.
So basically what I've tried to do with this presentation is to
briefly demonstrate the emerging issues and complexities of live streaming
and platform-based cultural production as it manifests on a platform like Twitch.
I think that these complexities demonstrate that
before we can talk about how to regulate these types of platforms,
we first have to talk about what it is exactly that we need or intend to regulate.
Can we first define what a Twitch broadcast is?
Can we account for its different temporal forums, it's amateur versus professional quality,
is overlapping gaming, music, visual, and personal material, and its commercial dimensions?
Should a streamer be held accountable for the content in their stream chat?
Should we audit the algorithms and reporting tools used on the site? Should
we regulate the terms of service for community guidelines that frame the platforms activities?
I'm sorry to propose more questions than answers,
but I find myself thinking about regulating platforms like Twitch as a sort of wicked problem
resistant to clear definition and uniform solutions.
I understand that there's an ongoing discussion taking place in Canada about
proposed amendments to the Broadcasting Act that would allow for the regulation
of user generated or social media based content.
And it seems to me that this type of amendment is a sort of blanket solution to a wicked problem
which, by definition, isn't tractable.
And as we've seen with platforms like Twitch, regulation and governance must account for
the inherent complexities and constantly changing dynamics of platform-mediated cultural production.
And so, addressing questions of platform regulation here will require in depth examination
flexibility, adaptability, nuance, and interdisciplinary cooperation. Thank you.
Thank you so much, Charlotte, that was super fascinating.
Our last presentation is a presentation of a team that I'm working with
Manny Appiah, who is a graduate of McMaster's masters program in Communication and New Media
and who's now working in the area of copyright.
Fizza Kulvi is a current PhD candidate in McMaster's Communication, New Media and Cultural Studies
program and Charnjot Shokar is also a graduate of McMaster's
Communications and New Media program, and we've been working together in the
Communications Governance Observatory to put together the paper that
that we've pre recorded our presentation for, and we'll be in the chat to discuss
as the recording is playing. I did want to note as well that this is a work in
progress and I've actually realized in the last few days, partly as a result of a tweet stream
by Dwayne Winseck, that we have some things to look at in terms of how we've characterized the
lobbying so maybe that's something we can come back to after the presentation.
I'm Sarah Bannerman, Canada Research Chair in Communication Policy and Governance, and our
presentation is called Platform Lobbying and Relational Sovereignty in Canada.
Hi, my name is Emmanuel Appiah, I completed McMaster's Master program in Communication and New Media, where
I researched and studied in privacy, copyright, television, and philosophy.
Now that I work in the copyright compliance space with the storytelling platform
I applied the theoretical components that we've kind of explored in my Master's,
to the copyright compliance space.
I'm Fizza Kulvi, PhD candidate working on the governance of online speech,
and I am a research assistant for the Communication Governance Observatory.
I'm Charnjot Shokar, research coordinator for the Communications Governance Observatory.
This paper is a work in progress. Our goals were to explore the concept of relational sovereignty
and establish a view to the relationship between the Canadian government and platforms.
This includes Amazon, Facebook, Google, and it sister company Sidewalk Labs, Netflix, and Twitter.
This paper examines the relationship between digital platforms and the Canadian government.
Drawing on the concepts of relationality and relational sovereignty outlined in Part One,
it examines the complex interactions between the Canadian government and digital platforms,
highlighting the ways that digital platforms are simultaneously the objects of regulation,
stakeholders and lobbyist shaping regulation,
and tools used in regulation and government service provision.
Our examination is based on records contained in the Canadian lobbying registry
and the large corpus of government documents obtained
via access to information requests. Our method is outlined in part two.
In part three, we present an overview of the interactions between the Canadian government and
digital platforms between 2008 to the present, drawing on the records of the lobbying registry
and our corpus of access to information, documents. We find that many areas of
federal regulation and governance are increasingly mediated by digital platforms.
We know growing numbers and meetings and involvements between
foreign digital platforms and government officials, we see that platforms
provide knowledge to government that is required to regulate or govern,
and we find that platforms influence regulations or play a role in regulation beyond simple lobbying.
The concept of relationality developed by feminist relational autonomists has been applied,
both at the level of individual people and to a lesser degree, at the level of states.
The world is not made up of individual agents for self made men, feminist relational autonomists note.
Instead, feminist theorists of relationality challenge notions of liberal individualism,
by acknowledging that people are fundamentally in relation.
Individuals' characteristics, their capacity for language,
and their modes of understanding the world are all developed by relationships with others.
States, similarly, are not unitary agents, but part of a network of relations.
The concept of relational sovereignty is, to date,
less developed, and it is the concept that we seek to explore in our paper.
A relational concept of sovereignty emphasizes that sovereignty comes from relationships.
The traditional concept of sovereignty in states is independent and self governing.
Sovereignty has always been, and to some extent, fiction.
A relational concept of sovereignty suggests that neither individuals nor states are atoms
independent of all that surrounds them, either as an ideal nor as a reality. Rather, states
must be conceived of as relational as setting up, recognizing, and configuring sets of relationships.
If relational sovereignty is nothing but the growing integration and influence
of digital platforms on state governance, is it still sovereignty?
Or is the decline of state sovereignty better termed as the rise of the platform sovereign?
For the literature review, we looked at relationality, platform sovereignty, platform regulation, and
lobbying. There has been little academic study of platform lobbying in Canada.
One of the few studies, Megan Beretta, examines the ways in which
lobbying shapes Canadian policy actors' perceptions of digital rights in Canada,
drawing on lead interviews with lobbyists, policymakers, and Members of Parliament.
In the United States Pawel Popiel has examined the power of the Tech Lobby,
while Profitt and Susca have examined the video game lobby.
Beretta and others have noted the growing intimacies between tech companies and government,
both in general and Canada in particular.
This is due to revolving doors between tech, political parties in government, of growing
tech lobby and growing range of policy issues and industries that platforms are involved with,
and dependency and government and political parties and platforms.
Tech companies have expanded their lobbying government relations staff
and spending dramatically.
And lobbying is important in Canada for its agenda setting function. Individual
parliamentarians, government, departments, and political parties use and depend on platforms.
So with our method, first we looked to gain an overview of the formal
lobbying efforts of digital platforms in Canada.
We examine the records contained in the Canadian federal lobbying registry,
including the number of meetings on record between digital platforms and the Canadian
Federal Government, and the topics of these meetings.
Second, we read and analyzed approximately 290 sets of government documents, or over 17 Gbs
of files released under the Canadian Access to Information Act covering the period from 2018-2019.
We used the content analysis software program MaxQDA to identify topics and themes
in the documents and to organize our reading and the creation of document timelines and summaries.
So here are our findings.
Platforms differ in the amount of lobbying they have done over time. As you can see from the
graph, Amazon lobbying in Canada has far outpaced those of other digital platforms since 2016.
Google has also lobbied the federal government extensively since 2008
the first year for which records are kept. Twitter registered lobbyists only since 2017
and Facebook has lobbied sporadically mainly in 2011 and 2019,
where Netflix has lobbied relatively consistently since 2010.
Where some platforms lobby mostly on a few topics, other platforms lobby on a broad range of issues.
So, for instance, Facebook's lobbying in 2019
largely focused on election issues and Netflix on broadcasts,
while Amazon and Google lobbied on a huge range of issues. Google, by far, is the is the biggest
spender and lobbying and the United States, followed by Amazon, Facebook, Netflix, and Twitter.
We look at this graph where we see that the House of Commons is the most significant
focus of lobbying efforts by platforms,
followed by the Department of Industry, Science and Economic Development, and Canadian heritage.
Amazon, in orange, lobbies a broad range of government institutions,
as does Google in red. Facebook in blue focused on the Privacy Council Office
and then, when we look at netflix,
which is in green, it focused largely on Canadian heritage and the CRTC.
When examining the access to information requests related to platform lobbying
and other platform related government activities, we can see three key things.
First, growing numbers of meetings between digital platforms and government officials. The
second, platforms increasingly provide knowledge to government that is required to regulate or govern.
And finally, platforms influence governance or play a role in governance beyond simple lobbying,
such as providing growth and jobs or providing services used in governance.
For example, on the topic of privacy and access to information,
international platform lobbying, indicated by the blue line on the left graph,
significantly exceeds the lobbying of domestic platforms. The same is true
in terms of taxation and finance, where the lobbying of international platforms,
indicated by the yellow line on the right graph, has grown to exceed that of domestic platforms.
In regards to our second finding, we saw that the meetings that took place between ministers
Joly and platform executives during the minister's visit to Silicon Valley in 2017
show just how crucial platforms' knowledge and know-how is to governance.
The meetings were used by the Minister as an opportunity to gain information
on the existing measures adopted by digital platforms to address policy challenges, such
as harmful content, misinformation, fake news, content diversity, and creator remuneration.
Another example is the Amazon Web Services Institute, where Amazon offers
events that allow government officials to learn
and connect with Amazon web services experts and others in the cloud computing community.
The Amazon Web Services Institute, a public private forum to engage with technological solutions to
common problems, connects public, private, and nonprofit sector experts and decision makers.
And finally, if we define governance as influencing behaviour, whether through
regulation or other measures, then governments turn to platforms to help them do this.
For example, at the meeting with Facebook Minister Joly asked representatives
to speak about the success rate of Facebook's fact checking tool
and when the platform intends on launching something similar in Canada.
At the time of the meeting, no fact checking program was offered on Facebook Canada
Joly expressed her interest in the program and asked if the program
entailed partnering with local fact checking organizations.
Shortly after, Facebook came to be enrolled
in the regulation of news and misinformation for Canada. In 2018,
following Joly's trip,
Facebook Canada announced a fact checking program in collaboration with the Agence France-Presse,
AFP. This program is connected to Facebook Canada's election integrity initiative.
AFP fact checkers are now responsible for reviewing
and raiding the accuracy of Canadian news stories on Facebook.
In addition to the fact checking tool,
Facebook Canada also announced new tactics, including disrupting financial incentives
taking action against fake accounts, applying machine learning to help diminish spam,
reducing the posts people see that link to low quality web pages,
and providing people with easier access to additional perspectives and information.
So, effectively, Facebook was enrolled by the Canadian Government
to govern news and disinformation.
So to summarize: through our research, we can see
a growing number of meetings between digital platforms and government officials.
We see that platforms increasingly provide knowledge to government
that is required to regulate or govern, and finally
that platforms influence governance, or play a role in governance beyond simple lobbying.
Okay, so we've come to the end of our different presentations, and I wanted to see if there's any
questions and comments, I know that we've had some discussion started in the chat already.
Is there anyone who wants to start off with any comments or questions?
I'll just go back to your work, Alex, I thought that was quite interesting
and, as I said before, I had never actually heard about explainable Ai.
So it's very interesting to hear you talk about that and to talk about the kind of possible
Darwinian direction that that could go and I had put the
note in the chat that it seems as though the kind of things that you're talking about
would be important in the realm of algorithm audits and
trying to detect discrimination that algorithms may be doing, would you talk about that a bit?
Well, I can talk somewhere generally. The European Union, for example, recently passed a law for like
a right to an explanation, and companies that make decisions, organizations that make decisions of
legal system, justice too, have limited time and money and they don't want to hire a bunch of people to
explain their decisions to people, so they are looking to things like explainable AI to automate
the whole process. Now explainable AI is still very much in its infancy, but a lot of people
are putting a lot of attention on it, because this requirement for the right to an explanation.
Well, when the EU moves, the rest of the world pays attention,
especially on these digital ladders. So.
So there's a lot of focus on that and I keep on hearing conversations about
what people are making these sorts of decisions in these companies,
who decides how the algorithm is made,
what people, what companies should we pay attention to?
And those are two very important levels of abstraction to pay attention to.
There absolutely are things we can do, and things we should pay attention at
those levels of abstraction. My concern is those aren't the only two levels.
There is like an ecosystem level, and there's others that I
haven't thought of, and that haven't been thought of
otherwise, but just like we can talk about a natural ecosystem, where there's
a particular animal or a particular planet, or we can have a particular.
forest, let's say--there's also the global climate, and you need to think about
that in a different way than you would think about individuals or regional conglomerations,
so this is my analogy to people and to companies, and there have been works on
"the science of the artificial" to think about...
think about the things we create in these sorts of holistic ways.
The thing is, the solutions that we might offer for problems, incomplete as they might be, that work at
one level of abstraction won't necessarily work at the other levels of abstraction.
So if we're having these sorts of selection pressures over time where there aren't any
malicious actors, where everyone is trying their best to do the right thing, we can still have
bad results, and that's a thing that I don't think,
I haven't seen personally a whole lot of attention
being given to. Now, that's not the situation we're in; we're in a situation
where there are a bunch of actors who are trying to look out for number one and.
And some who even have malicious intent, which just
confounds the whole situation even more, but my concern is,
even in the optimal situation where all the individuals were saints, and all the companies
were looking out for the social good, we would still have this other level of abstraction layer
where bad things will still emerge, and I don't see that being talked about too much,
and this indicates to me that if we can think of this other level of abstraction,
there's probably an infinite number more that we haven't thought of, and that's a challenge too.
Anyway.
It's a fascinating whole topic that you've raised. Thank you so much.
There's a few more questions in the chat. Tanner has a question first and then Dwayne.
Thanks so much for that incredible set of presentations.
Sarah I have a question for you and your co presenters particularly related to one,
the concept of sovereignty, and two, the sort of starting point of relational sovereignty
for, I think the conceptual framing, so
I'm really interested in the concept of sovereignty, because I remember the 90s
there's a whole bunch of work written, basically the end of sovereignty,
the end of communication and media cultural sovereignty.
And I think what we're seeing today is an attempt to re-vivify some kind of normative concept of
communication media and cultural sovereignty, so i'm just sort of wondering if if sort of
you or your co panelists might sort of reflect upon the implications of that, and where things
might be going with the sovereignty concept. Secondly, as per relational sovereignty
as a framing device, I'm curious as to why you might have selected that one, as opposed to,
for example, maybe like a political economy sort of approach to the relationship between
the state and capital just generally, or state theory or critical theories of policy
making processes that might be drawn from anyone from C. Right Mills to liberal pluralist
tradition to the Chicago school regulatory capture tradition, or even to a neo Marxist approach that
looks at the relative autonomy of the State vis a vis blocks of capital that are trying to influence
policy making processes, so I'm just sort of curious about your thinking about the policy
making process itself as related to sort of bigger questions about the relationship between the state
and capital and then sort of what's the future of sovereignty in a so called post sovereign world.
One thing to say, to start off with is that the concept of relational sovereignty
is not meant as a normative concept, it's not meant as
something that's supposed to be a rule stick
in the way that we've described it right here, or a measuring stick.
And I think that is something, that it's a very good question that i'm also asking myself:
Is the concept of relational sovereignty helpful or not
if you were to kind of try to figure out what would a normative concept of relational
sovereignty look like, and I think if you're going to start to use that idea in a normative
sense, you do have to bring into the idea all of those learnings that we
do receive from the critical political economy tradition and other traditions that talk about
relationships in terms of power, and so I think, to turn it into a normative concept, which I think
is extremely good challenge, and I also think that that is a weakness of the relational autonomy
literature in general; it doesn't always bring that in successfully, I think, and so that's
something we have to continue to grapple with assuming that we continue to use this concept.
I feel like I may have dropped off part of your question, but that was the main answer
that I had for that.
And dwayne.
For James, you know, quick question for you, two quick questions:
One: Australian news bargaining code, should Canada adopt it?
Two, I'm intrigued by your reference to the German and Australian non-discrimination requirements
that require platforms that are deemed large enough to not unfairly discriminate between
news outlets and you site Nelson and [...]. So could you please
give me those full citations later, but you know.
Because a lot of people worry about that I like the idea, you know this kind of
must carry notion non discrimination, but you can get into some troubles there, so.
You know how's that working out for Australia and Germany,
and can you kind of shed light in because many people see a must carry mandate basically as
oh my God, then the social media will just be overrun with garbage.
So how do you prevent that problem?
To answer the question Dwayne might take a second question first.
In one way, one might think that even with all of the work being done around content moderation
and etc., social media is still overrun with garbage to some extent,
I think the most important thing, so in one sense, you know it's.
that's kind of there. I think the most important thing in terms of the Australian legislation though,
and I can speak to that with more rigor, is that the must carry is mainly established within
the context of a bargaining negotiation, so the idea being that if Facebook or Google
had a bad bargaining ground with a news organization they couldn't strike them from the services and
say okay, we're not going to feature you, and even though we're going to hand over
X million dollars, we're not going to feature you on our services. I think, in that
kind of way, unlike the kind of transparency mechanisms that
Robert and others have spoken to in this panel, what I like, at least in the context of news
is that this must carry
reforms actually provide a baseline standard for what must be on social media. We can't say
what can't be, but at least an extreme example, there's a baseline that saying well,
you have to save all news that wants to be on your platform, and I think that's kind of a good
principle, that these failed legislation doesn't really speak to a more general.
baseline to carry everything.
Which yeah I think is a kind of useful proposition there to kind of provide
at least one category of content that should be on there.
And don't even ask me about media bargaining,
I'm not sure how long I've got.
I think what I, to keep it quite short,
I don't think it's a very good legislation, I think it ties
news companies to platforms and their revenue models, and it doesn't actually force news
companies to actually think through how they might generate different sources of revenue.
You know a lot of this, as yourself and others have pointed out, a lot of the crosses has come
about because of collapse in advertising revenue from the mid 2000s onwards.
I don't think the media companies have spent too much time generally
coming to grips with what that means, it also excludes public
government from funding
a range of different options, obviously not corporate media, but thinking about
cooperative community media. There's other ways to do this, as Victor Pickard has pointed out,
and I think the most worrying thing from the Australian example is that
there's not a lot of clarity about what these fields mean.
There's no transparency about the deals being made between
companies and platforms, because they're all commercial and confidence.
And you essentially start tying the media companies to the viability of platforms.
Yeah, so I think there's kind of,
you know, I know candidates considering this quite strongly,
I don't think it's an ideal approach, and I think most of all,
the fact that the legislation is not actually in operation at the moment, as I pointed out.
It simply used as a sword of Damocles to threaten platforms with I think...
It's kind of also telling that, it's very much a real politics situation here rather than
carefully worded and implemented legislation that's been put through and actually used.
Thank you very much, such an important message I think because,
I think so many people are enamoured with the idea that, wow somebody brought the, you know,
the vampire squid to heel, let's get that over here ASAP, but once you start to look it's not
not all that pretty inside but anyway, thank you.
Thank you, both we have another question or comment to Robert.
Sharon Jeanette has said I was intrigued by Robert's statement that algorithms are
regulation and optimization is policy and view of the complexity of situation, as described
by any other presenters, how do you think we'll get to optimization.
Okay, yeah, thanks. Yeah, I'm glad someone liked that
It's kind of a provocative analogy; I'm not sure what actual policy people would think of it.
We were drawing on desk treatments work there in distinguishing between
policy and regulation, and I'm just going to read this real fast.
Media regulation refers to tools that are deployed on the media to achieve established policy goals,
so, in our view, algorithms can be considered forms of regulation
that calculate how to process cultural content.
And then the goals that the algorithms have or the optimizations,
in a way I'm not totally sure what you mean by how how we will get to optimization,
it's more just that we can kind of see the effects of algorithms
often, but we cannot ever know kind of why they're doing the things they're doing.
Other than sort of vague, you know Mark Zuckerberg
saying he wants to optimize for meaningful social interactions.
Or, you know, YouTube just saying it's for engagement, and we don't really know
kind of how they calculate engagement,
how they judge whether their algorithm has successively increased engagement.
And we can guess you know time on site and kind of
deduce from basic web analytics what might people be optimizing their algorithms for.
But I mean I was really excited by Charlotte's presentation just showing
how complicated just one platform is that isn't even one of the ones we talked about.
A lot, and like I mean what would you say Twitch is optimized
for, I mean there could be so many different answers to that,
and so that's kind of what we were trying to get out there with that analogy is that,
in order to actually hold any of these platforms accountable in any
way we don't often really know what they're even trying to achieve.
Other than just kind of basic capitalist ideas, that they want to make money,
increase viewers, or increase subscribers, but how they go about doing that, like how they actually
design and put their platform into action is opaque.
So, yeah, if you wanted to talk more about what you meant by how we will get to it,
maybe i'm not quite getting that but that's just a bit more on
what we were talking about there. Thanks.
Did you want me to respond to that question?
Please do, yeah.
Yeah, I guess, I was thinking of optimization in terms of policy
optimization at the national level, I mean we're talking a lot about that now.
With Bill C-10 and it's a fairly limited number of platforms that we're looking at.
What you've described in this panel is a whole lot of other issues that we haven't even started to
address, so how would we start to address them, I'm talking from a national kind of policy lens.
I mean, yeah, I don't know, is the short and honest answer. I think,
returning to this work, after a year away, I think one of my main takeaways was that
the level of abstraction that we sometimes have to get
to in order to talk about these things at a kind of national policy level
really stands over like lots of differences between,
You know, what's the difference between Netflix and YouTube, like they're both streaming video,
but you know there's so many different ways of kind of describing what they do. I don't know
if anyone else was in the panel that Dwayne was hosting previous to this where we were
kind of debating which policy framework was best for regulating. Some people think broadcasting
Dwayne was advocating telecoms was a better model to think was an interesting argument.
But I think it's also a testament to the fact that again like
that Charlotte really demonstrated, one platform is really hard to
sum up under this kind of pre- existing, often 20th or even earlier
century models of how we think these industries function, and often platforms,
I mean almost as by definition according to some platform theorists sprawl and try to develop
into new areas, so yeah my answer to how do I think we'll get there is I have no idea.
Maybe what we need is some sort of platform policy,
but again, I mean, that would just be so many different, such a grab bag of different problems.
That yeah, I don't know, it's maybe not possible,
I do think looking at use and maybe looking at the different actors might be,
in some ways, better way of kind of getting perspectives on it, I don't know if it would
lead to actual policy solutions, but like how do users use the platform, how do creators use it,
what does a company get out of it, these kind of might need to be differentiated more clearly.
Thanks so much Robert. Charlotte I see there's a discussion in the chat about hot tub Meta and
I noticed you kind of put your hand up earlier, so do you want to comment on that discussion, or
about the conversation here.
I think I just want to go back to this idea of discoverability,
especially because one of the things I,
you know, there's only so much I can do in 12 minutes about Twitch given how complex it is,
but a lot of the discussion on Twitch has been about discoverability hierarchies and algorithms
that make it so that certain streamers--you know, the ones with the most amount of viewers--
are the ones that get kind of boosted to the top of the page, and Twitch will also
work to select those that best represent the brand and are best in the interest of game
companies who will work with esports or promote certain games or promote certain aesthetics.
During Black History Month they'll promote black content creators,
they'll try to promote people with LGBT interests,
and so, anyone who isn't either being privileged by Twitch staff,
or someone who isn't naturally, you know, like you know people playing call of duty to 15,000 people.
Everyone else is kind of left at the bottom, and so you know there's kind of this issue about,
like, what is the policy in place, and so the algorithms kind of follow what
is both Twitch's community guidelines in terms of promoting a certain brand image,
and also what is going to make the most money for the platform. So if a guy like Ninja is streaming
to 50,000 people, and he has 50,000 subscribers to which he gets 50% cut of all those subscriptions
which are renewable every month, so there's like different interests right
in terms of the bottom line for the platform to stay profitable
the branding and imaging, and then you know, whatever the issue du jour is
in order to prevent backlash. And the fact that Twitch is owned by Amazon,
you know, has its own implications as well, so I just think that,
to the question of, you know, optimization via policy I think is really interesting because
it might not be like optimizing and policy in the best interest
but, rather, you know as Robert was saying, in the interest of the company itself.
Terrific. Thanks so much.
A question by Robert for James.
How does enforcement work with the laws that
you looked at, even if just thinking about it hypothetically.
Yeah great question. I forgot to mention that,
and it kind of passed my mind while I was constructing the presentation, that the German transparency
law is entirely optional, right, which is fantastic, so
it's kind of a, if you want to do it, right, which i'm sure platforms will be rushing to do.
So, in that sense, enforcement's not so much of an issue in terms of
the news media bargaining code. I must confess, I've, you know, I completely have,
despite having read the legislation like hundreds of times, enforcement
provisions completely slipped my mind.
I guess we stopped thinking about them in Australia, because
none of these standards are being used now because no one's been designated so.
Enforcement hasn't really come to the table, I think, from you know, a distant memory
we're talking about fines.
Maybe not quite on the quantum of the EU, but not far behind in terms of recognizing that the
greater revenue coming from platforms and trying to get a piece of that, but
you know, don't quote me on that. I'll get back to you, though.
Wonderful! Well, unfortunately, here we are at the end of the session already it's been
a really fascinating session, and thank you for all of these terrific presentations.