A key part of this legislation is the requirements for tech companies to provide selected news organisations with advance notice about changes to ranking algorithms. This has been generally overlooked in the reporting and discussion but I believe it is the actually the most important part of the legislation. It will give the selected news organisations an enormous advantage over other companies not included and protect them from new competitors, basically entrenching the current media landscape for the foreseeable future.
Given the current Australian government's cosy relationship with a particular media company that currently dominates the media landscape here, I don't think it is coincidence.
It's even broader than that. The law applies to any "alterations to the ways in which a service distributes content". The law never actually defines what this means, but it gives a bunch of examples that go beyond ranking. For example, anything that affects a particular "class of content", such as deciding whether or not to make all videos auto-play, is an alteration.
Basically, this law would prevent Facebook from deploying just about any non-trivial change to its product without first doing a detailed analysis of how it would affect the Australian news business, in order to determine whether a notification is required.
Thanks for highlighting this. The initial reporting and FB’s own blog posts do not make this clear. Even running experiments at scale could be problematic with the way this is written (interpretation as a non-lawyer).
Google has been busy making major deals with news organisations. Facebook has chosen the opposite track. The problem Facebook has is government in Australia is wildly popular because of the pandemic. They can do what they want right now it won't lose them any votes. Ministers are already telling Facebook to leave the country completely!
I would not call the federal government “wildly popular”. Most people are (rightly, IMO) laying our pandemic success at the feet of state politicians and realising that the federal level had been ineffective at best.
Australia does have competitive federalism, and so many of the localised decisions have been from the states, but the major decisions for seeding - closing borders, acquiring vaccines, etc, along with fiscal backstopping - are federal.
The federal government (under ScoMo) has never polled so well as it has during covid, and for good reason.
(NB: I am no blind Coalition supporter, but they have made decisions which are very popular, and no amount of directing attention to the states would absolve them from blame if we had a situation more like Europe or the US.)
The United States has land borders with two countries: Canada and Mexico. However these are NOT considered to be major sources of Covid infection in the U.S.--instead it has been China and Europe. The U.S. could have set up controls eliminating people movement by air as easily as Australia did--but it failed to do so.
This is an artefact of bad testing in non-developed countries.
You should assume it's ten or twenty times worse than the figures show, AT LEAST, in any countries that aren't fully transparent and rich enough to test widely.
Facebook and Google have not taken opposite tracks; Google hasn't made deals to pay for linking, a major problem with the legislation. Google's deals are around news content, but FB lacks comparable products to negotiate deals around, leaving them with only a refusal to pay for linking.
Yup, and given that we have largely managed the pandemic extremely well as a society with a competent government that has a publicly funded health care system, I think it's unlikely that Facebook preventing us from reading the news via Facebook is much of a concern.
We were a large consumer of news before Facebook. We will be a large consumer of news after Facebook.
That’s not a fair characterisation of GDPR, nor one borne about by examples.
And 2020 put pause to any Australian brain drain and given how well we’ve handled the pandemic, is likely to be seen significant increases in net migration.
I actually suspect that new news carousel product they launched might be related. If they push news articles onto the carousel, they can apply a different algorithm to the carousel.
Honestly, I think that makes sense and it doesn't immediately strike me as a negative for either side. These are articles coming from trusted sources. There's no need to apply the anti-spam parts of the algorithm. News agencies get a more stable algorithm, Google gets to keep their secret sauce.
There is still an advantage to the incumbents. Those carousels are usually in prime real estate. Google would hold the keys to who is in the carousel though, so they could expand it without legislative changes. I like the flexibility, though I don't love handing Google the keys to more kingdoms.
The only problem I have with this requirement is that it requires giving only news organizations access to this information. Your concerns can adequately be addressed by ensuring everyone has even access to that information.
Google and Facebook's algorithms should be required to be publicly disclosed. As a society, we should demand that we are able to see the algorithms that every web property lives and dies based on, that lives are built and destroyed by.
These algorithms are not human readable code. They are massively complex interconnected systems of many black box ML models. I don't understand what clarity people think releasing the "algorithms" will bring. In fact, describing ranking as a single algorithm is pretty misleading.
As you say, explaining the intracicies of the algorithm is a fools errand. I guess it is more reasonable if you turn it around: these changes have drastic impact on businesses, so there is a duty to behave responsibly in administering them.
If Google really has no idea what the impact of a change will be then it is fairly irresponsible to make that change given the real world harm it can cause. But I suspect in general it does have at least a reasonable idea what the effect of changes will be - that is why it is making them.
So the more reasonable version of this is that they need to submit human interpretable descriptions of the effect of changes based on reasonable evidence and validation of their models.
Monitoring search engine and social network ranking and filtering updates should be more efficient than complaining about biased parrots (language models). This is a tip to certain ethics researchers who are raising scandals about search bias, but not in the right place - go in the field, check the fucking feeds, leave your abstract ethical tower and measure the reality.
Instead of trying to argue the gender bias in "doctor - man + woman == nurse" (abstract ethical argument) they should check the search results for bias (concrete, measured effect).
In many cases they (Google) don't know the impact of changes until they try deploying the changes, and there's ML in the picture, not just algorithms. As I understand it, they often run tests that expose the change to a limited subset of users first.
Yes, but they don't just do random stuff. They make changes with the intention to adjust the experience in certain ways, so making those intentions public is important.
I also believe any algorithm that isn't human-readable should be banned. If it can't be understood, nobody can validate that it isn't racist, sexist, or slanted towards encouraging violence and harm.
The fact that technology companies have been grossly negligent and irresponsible isn't a reason to not regulate them: It's proof regulation needs to be much, much stronger.
This is an incredibly naive perspective. I guess you want to ban search engines, self driving cars, automated filtering of lewd and abusive content (why do you think FB isn’t full of porn? It’s not a hand engineered algorithm), automatic speech recognition for the hearing impaired, and a vast swath of important technology I didn’t list. I don’t think you really understand the implications of what you’re asking for. Sorry - black boxes are here to stay. And they are immeasurably useful. I could spend hours listing important and crucial technologies that you want banned because you are scared of racism.
> I agree with you but I am still scared of racism.
My suspicion is that the concern with machine learning over racism is rooted in two things. The first is just the general modern trend of accusing anything you don't like of being racist, because everybody hates racism and wants to fight it. And the second is the fear on the part of people who make a living fighting racism that machine learning might actually put them out of a job.
Because machine learning is basically a paperclip optimizer. You tell it to maximize a thing, it maximizes the thing and minimizes everything else. Racism isn't paperclips, so the paperclip optimizer will optimize for smashing it in favor of making more paperclips. And then they're out of business.
Because when you look at the criticism of this stuff, it generally looks like this. ~12% of the population is black, only ~5% of the selected applicants are black, the algorithm is accused of racism.
But nothing is that simple, because all kinds of things like income and education level and so on correlate with race, so you have to take all of those things into account before you can tell what's going on. And taking into account all of the available data is how machine learning works.
Which isn't to say that you couldn't make an algorithm racist. Tell it to optimize for applicants with a particular skin color and it does. But then your problem isn't with the algorithm, it's with the jackasses who asked for that.
What to optimize for is a much more general and difficult question. (Hint: Not paperclips.)
> My suspicion is that the concern with machine learning over racism is rooted in two things. The first is just the general modern trend of accusing anything you don't like of being racist, because everybody hates racism and wants to fight it. And the second is the fear on the part of people who make a living fighting racism that machine learning might actually put them out of a job.
I don't get to how you go from this statement, to then again explaining exactly how racism is embedded in algorithms. By using the biased data we have in the real world...
It isn't the data that's biased. If you're hiring a computer scientist and disproportionately few black people have a degree in computer science, the data is not lying about who the qualified applicants are and the algorithm can't change that.
To fix that you have to cause more black high school students to go to college and study computer science and then wait two generations until their proportionality in the installed base of qualified computer scientists reaches parity. There is no magic wand that makes it happen overnight.
But concentrating on the places where it can't be solved instead of the places where it can will make it take even longer.
No, the racism is a real issue, though a lot of it is caused by limited training data. Having an image recognition algorithm identify Africans and South Asians as gorillas doesn't happen because the designers intended it, but because their training data had only light-skinned human faces and dark-skinned primates. But the effect is racist even though this wasn't the intent.
Likewise, if the system is trained to duplicate human decision-making (like who gets loans), interesting things can happen: if the decision-makers unconsciously favored whites over blacks, the algorithm could wind up weighing skin color or stereotypically Black or Latino names negatively, meaning that the final model is explicitly racist, just because there is a correlation in the training data. That doesn't mean we shouldn't use deep learning, it means that it's not responsible to just fit the training data and ship without testing for such problems.
> Having an image recognition algorithm identify Africans and South Asians as gorillas doesn't happen because the designers intended it, but because their training data had only light-skinned human faces and dark-skinned primates. But the effect is racist even though this wasn't the intent.
This isn't racism at all. It's just bad PR because humans take the implication that calling black people monkeys is calling them stupid, since that's the implication you would draw if a person did that.
An algorithm doing that is just recognizing that humans and gorillas are both primates:
And then it's a bug, in the same way that recognizing a black balloon as a balloon but a white balloon as a light bulb is a bug. It has nothing to do with race at all. The algorithm isn't racist against white balloons. The solution is a general increase in the amount of training data, which is what you want in all cases regardless.
> if the decision-makers unconsciously favored whites over blacks, the algorithm could wind up weighing skin color or stereotypically Black or Latino names negatively, meaning that the final model is explicitly racist, just because there is a correlation in the training data.
Except that this is exactly the thing that a paperclip optimizer will smash to bits because it interferes with the goal of making more paperclips.
I’m not an expert in this, but I think racists call black people apes, not just because they think they are stupid, but because they think they are sub-human.
Blacks don’t reach the intelligence and blah to be human. I think that’s what racists drive at when they call someone a monkey, and that’s why it’s so offensive.
It would also make your theoretical AI racist, as it identified blacks as not human.
Honestly, at the end of the day that is what is so difficult about much of this. It’s mostly subjective
> It would also make your theoretical AI racist, as it identified blacks as not human.
That isn't how racism works. It's like saying that an AI that misclassifies a bat as a bird is racist. It's not racism, it's just error.
And it's not a race-specific error, it's a general error for which someone cherry picked the instances that imply a racially motivated intent that doesn't actually exist.
Calling it racism is pointless and misleading because there is no race-specific cause or solution to the problem. The solution is completely identical to the one for the same error in the general case, i.e. get more training data.
This isn't theoretical: Google Photos was identifying Black people as gorillas, and they didn't fix it, they worked around the bug by removing "gorilla" as a possible label in 2018. Some here seem to be saying that we can't call this racist unless someone specifically intended to do this because of hate. It's not subjective when someone's own face is so flagged.
I absolutely want to ban self-driving cars that behave in ways no human can explain or understand! The mere idea that anyone would think that should be legal is borderline insane.
All you are doing here is convincing me that tech companies are just runaway trains with nobody at the controls!
Humans are held responsible if they cause harm to others.
If a driver hits a pedestrian on purpose he is charged with murder.
Who do you charge if a self-driving car behaves in this way?
the humans that designed the car? to be clear, computers don't intentionally do anything. if an engineer deliberately programs a car to hit pedestrians for no reason, they would be charged with murder. if the car hits a pedestrian as a result of an engineering mistake, the company would be liable for damages, and if particularly egregious, engineers might face manslaughter charges.
To be clear, that was my point. You can't punish the computer and good luck finding the one to punish for an accident ten years later.
But maybe if it's free software...
Punishing bad driving is - I assume - intended to incentivise better driving.
If a company makes a self-driving car and that car then drives badly, surely the response needs to be to incentivise the company to improve their engineering practices, eg, spend more on testing, or require more levels of review of changes, or whatever other organisational changes they need to make safer cars. You don't need to find an individual person responsible to create that incentive. And if you really do want to find an individual responsible it can easily just be the executives of the company (and the executives are probably pretty easy to find even a decade later).
What about all the other examples he listed. What about cancer detection? Or viral spread prediction? Drug discovery or medical imaging diagnosis? Physics research?
Machine learning is very widely used in the sciences and extremely beneficial to humanity in uncountably many ways and assuredly countless more to come. Of course technologies can be used for evil but so can nearly everything that exists. I believe your proposal comes from a desire to help or better the world, but to ban all non-human-readable algorithms is frankly ridiculous and demonstrates a naive understanding of the issue. It sounds a lot like the calls by the U.S. Congress to ban encryption.
- In medical: your doctor should be responsible for your diagnosis and drug company is responsible for defective drugs, except when they get away with lobbying and hiring good lawyers.
- In physics: I'm not sure if it's as big of a problem as in social networks. But consider this case: If you cannot reproduce the result of an experiment due to a ML model being cryptic, that would lead to huge credibility issue in science.
At best, you may be able to justify black boxes providing secondary indicators: Maybe using AI to study cancer detection might lead you to a new solid discovery, but "we use AI to determine if you have cancer" should never be the mission, as it fails to generate useful information about how it is detected.
We can't even explain all physical phenomena, so good luck with banning anything that depends on the gravity of earth to function, because we don't know what gravity is.
But gravitational laws stay unchanged for millenials isn't it ? If I toss an apple, it will falls down. If I throw it fast enough, it goes into orbital mode.
> I also believe any algorithm that isn't human-readable should be banned. If it can't be understood, nobody can validate that it isn't racist, sexist, or slanted towards encouraging violence and harm.
I'm not sure a human-readable algorithm exists for ranking all the web pages in the world based on natural language input. In fact, I'm pretty sure such an algorithm does not, and potentially cannot, exist given the absolute failure of all approaches towards NLP that weren't based on absolute masses of text data and complex models.
Are you willing to make Google 10% as effective to achieve your goal of a human-readable algorithm?
you don't need any NLP to rank webpages (in fact the entire innovation of Google was that they figured out a way to rank pages completely ignoring that fact). Pagerank works fundamentally by treating the web as a graph and prioritising results based on their connections, that is to say it ranks based on popularity and is agnostic about the content of the actual page.
This generally has worked well. On the other hand, actually attempting to manipulate search results based on automated handling of content is what has given us countless of censorship debates or simply failure where even uncontroversial content is removed or downranked because it violated some sort of strange rule because it had a 'bad word' in it. On Facebook recently clothing ads for the disabled people were banned[1], because turns out the ML system only cared about the wheelchair, not the person in it.
It's actually fairly straight-forward to build recommender systems on transparent, graph-based algorithms and it gives you the added advantage of not discriminating in strange ways.
You've just skipped over the early days of Google where they relied primarily on PageRank and bad actors manipulated it to death.
It's trivial to generate webs of fake, inter-related content and use that specifically to feed incoming links to valuable pages. Or to comment-spam websites so aggressively it ruins them. Or all of the secret deals between high-ranking sites to feed links even though the sites weren't related. There are countless examples of black-hat techniques to break PageRank.
I am sorry but you simply can't build a sustainable search engine without deeply understanding the user intent and the meaning behind the indexed pages.
>There are countless examples of black-hat techniques to break PageRank
there are also countless of adversarial examples to trick ML algorithms. In fact this is in many ways worse because of the 'idiot savant' character of ML systems, which are almost always oblivious to context and can be tricked in ways that aren't apparent from the design of the system.
In contrast to systems that are legible or even formally verifiable ML systems are entirely unable to provide any guarantees. When someone breaks pagerank at least it's apparent how they broke it. When an ML system mistakes a turtle with a fractal pattern on its shell for a gun nobody knows how to fix the system in any reliable way, other than feed it more data and pray.
Pagerank worked fine when it was invented. It's a very elegant algorithm. But in a perfect illustration of Goodhart's law, it fell apart once people realized that they could game it to increase their traffic. Google has been in a constant arms race against unscrupulous SEO practices ever since.
>Google has been in a constant arms race against unscrupulous SEO practices ever since.
One company controls 80% of what is found on the internet. They set rules, restrictions, penalties that are not public. They do not pass any sort of regulatory muster. They rip and tear through businesses standing in their way. They crush out a person's online existence through never explained reasons. They use every advantage they can to tweak a human's emotions, drive and needs to feed more and more advertisements.
You suggest those trying to use every advantage they can to rank higher unscrupulous?
Google's fight to keep search results crisp ended soon after they began selling advertising. Google long ago quit innovating search to be better for people, they've made it better for advertisers.
I agree that you don't need NLP to rank webpages (though it certainly helps), but you do need it to parse the kinds of queries given to search engines these days. The days of logical OR and NOT are long gone I'm afraid.
> It's actually fairly straight-forward to build recommender systems on transparent, graph-based algorithms and it gives you the added advantage of not discriminating in strange ways.
I think other commenters have addressed the PageRank issue, but I'd be super interested in papers doing the work you note above.
To me, their response didn't seem to indicate that it should be directly decided by people. This is a consumer protection matter, and to stretch an analogy, like a list of ingredients on a consumable. Here we have these black boxes, and no list of ingredients, yet they drive and shape our world. A Person can't EVEN directly decide if they wanted to.
If you look at the actual data, you will find that black box models are in fact responsible for preventing the majority of abusive content including hate speech and porn on social media platforms. Ban these models and you’d find your favorite social media platform is more abusive. Most of the racism and sexism you are concerned about comes from other humans.
Tell me, how did your brain come up with what you wrote? How do I validate that it isn't racist, sexist, or slanted towards encouraging violence and harm?
Seems to me that’s a viable answer. How can we test an algorithm like Google’s ranking though? We can’t feed it consistent data like in a software test. It relies on too much information, and what we know about it indicates we can’t extract it out to test against it—except for results in the real world.
Not to mention Facebook’s are even more difficult. Tangentially related, remember when you could use “View As” on your profile page to see what your profile looked like to others? It doesn’t work anymore, only works for Public and Yourself; you can no longer choose the person to view as.
It’d be great to test these algorithms. We can’t. They need to be designed and instrumented so this is possible.
lol. sorry, but that reminds me of a skit by an Australian comedian:
male guest: "now first of all, let me just start by saying I'm not racist..."
female guest: "pfft..."
host: "ah see you made a noise there, but a lot of people accuse him of being a racist, so I think it's very helpful to know that he actually isn't one..."
Right, like I said, credibility is a different problem. But at this point, we don't even get a lie from them, we get nothing. At least a lie can be checked and examined. There's nothing available at all currently.
We actually have a reasonable way to test for human biases in AI - perturb the input a bit and see how the AI responds - For e.g., change the name, change the gender etc. and we use them to measure if AI is fair. It is different question whether all AI can be subject to such tests. For e.g., how will you detect a human bias in a page ranking algorithm? but for where it matters, you can test them and we do test them.
Yes. True and fair. But how can we test the page rank algorithm “ourselves"? Who is "we" in the "we do test them"? Is the public even asking for 3rd party examinations by transparent/public organizations (or at least, publicly funded)? Seems like we only get to "test" against the live system, and third party examination seems relatively impossible. It seems like something with such far reaching and invasive results should be more accessible, at the very least.
Very few people have the ability to influence the success or failure of every business on the planet. Those that do are heavily scrutinized for racist or sexist behavior. (Sometimes they also don't get convicted anyways, but that's another matter.)
Yeah, they can give you the architecture drawn as a nice mind map, list the hyper-parameters, but that's like knowing the algorithm of the compiler, it doesn't help detect a bad program. The question is what the model is learning, not how. What are the inputs and what is it learning to output.
Explainable models do not preclude the systemic problems you highlight. Plenty of systems before the advent of non-explanatory ML models had those defects. One option is to define test and validation sets and encourage 3P validation, somewhat like how accreditation works in other contexts.
Publicly disclosing the algorithms would drastically increase the pace of gaming them and resulting in pay to play system where the fanciest SEO wins.
Google and Facebook partially relies on the obscurity to keep the fighting the spam battle. IMO we don't have the technology yet to have fully open ranking algorithms that are not quickly broken.
Google's best asset for ranking is their user data. Even if you had the exact algorithm, you couldn't game it without massive amounts of user traffic. (At least not for popular searches.)
delegating the war against spam rather then being picked up by the user doesn't seem right. To give Big Tech such power to relieve ourselves of a mild annoyance is destructive. This is understood in other aspects of life Hence we have local governments which are inefficient and inconvenience people greatly. Yet it is found that selling all our problems is counterproductive. It ends with monopolies. The answer to this isn't to charge tech companies for the privilege of dictating our lives, rather, it's greater accountability on behalf of big tech and more responsibility on the part of consumers. The only cure for google domination is for the transfer of information online to become more democratised.
This excuse has been used to protect Google and Facebook for decades, but considering disinformation campaigns, civil unrest, and outright genocide has been the cost... I think the price of using obscurity to prevent SEO tactics is way too high.
The root cause isn't algorithms, it's a lack of accountability (both of companies and of users). The problem with 8chan wasn't some inadvertently harmful AI, it's that the site and its users damaged the world for several years without facing consequences.
I'm curious how this advance update thing is supposed to work. What does disclosing those details look like, actually?
The reason I'm asking is that as these things grow in complexity, it's quite possible that even if you join the team that works on these systems it will probably take you a pretty long time to understand how they really work. Their actual behaviour is likely to still be mysterious a lot of the time because they're driven by data.
Is a high-level description in english OK? Do we need to see pseudocode? The source code code? Do they have to open source it? What parts, if it's tied to internal frameworks? If there is ML, do they have to disclose all their sauce there? The trained network / weights? The training data, if the alg alone is useless without a data set?
Any human-initiated change to search algorithms is presumably human-understandable. Someone writes a rule to downrank some terms or traits of a website, they presumably document it somewhere.
That documentation will need to be shared, and the implementation of the rule change will need to be delayed until the disclosure window has passed.
Honestly, first and foremost, I expect a firehose of documentation, if Google isn't lying about making dozens of changes to it's algorithms every day. News companies might need a full-time guy (or team) just to sit there and read through them all.
But on the other hand, a bunch of journalists will have a ton of never-before-seen information about how the world's most powerful companies affect every other company on the planet. That alone is going to be worth some major exclusives.
Also, by the mere nature of being forced to share it, Google and Facebook will have to clean up their acts, they'll have to assume any change they make that could open them up to legal scrutiny will be found.
You underestimate the complexity here by orders of magnitudes. You also overestimate the usefulness to news companies. You underestimate the harm that bad actors can take.
The search algorithm tells you the order of search results for a particular set of terms. Except that as input you need to feed it a graph of the entire indexed internet, which is re-indexed periodically as the content on the index changes. How does knowing that benefit new companies? What, exactly would your hypothetical full-time guy/team, equipped with that index at huge cost, tell their company that would justify the time and expense? That they should write interesting content that lots of people consume?
Second, the general approach has been published and is well documented [1], as are its susceptibilities to attack [2]. So there's your algorithm, what does it tell you?
Third, general SEO isn't the problem, it's coordinated attacks that can poison all search results / ads markets if enough detail is known. Google invests [3] heavily to address these areas [4].
Finally, you underestimate how much of a firehose you'd have to drink from. It describes all of the internet.
You might want to note some very important parts of your first-listed source:
> Furthermore, advertising income often provides an incentive to provide poor quality search results. For example, we noticed a major search engine would not return a large airline's homepage when the airline's name was given as a query. It so happened that the airline had placed an expensive ad, linked to the query that was its name. A better search engine would not have required this ad, and possibly resulted in the loss of the revenue from the airline to the search engine. In general, it could be argued from the consumer point of view that the better the search engine is, the fewer advertisements will be needed for the consumer to find what they want. This of course erodes the advertising supported business model of the existing search engines. However, there will always be money from advertisers who want a customer to switch products, or have something that is genuinely new. But we believe the issue of advertising causes enough mixed incentives that it is crucial to have a competitive search engine that is transparent and in the academic realm.
Larry and Sergey themselves both believed that ad-funded search was problematic, and that a transparent search engine in the academic realm was "crucial".
Unfortunately, Larry and Sergey's price was clearly billions of dollars.
Instead of relying on ignorance to push your agenda, you could frame that paragraph against what search engines were doing at the time, how Google was doing something different, and what Google still is transparent about today [1][2].
Publishing "the algorithm" doesn't have value for most of the internet, it is impractical to use even if it was, and bad actors would use it to destroy search quality to the detriment of e-commerce everywhere.
No, I've lived in the SEO world my entire life and I guarantee you that if the ranking changes were published spam would be so bad is to make search engines or social media totally unusable.
While I share your hesitation here, I think two points are important to keep in mind:
1. There is quite a difference between compulsory auditing (what the post you reply to refers to) and the government directly controlling industry.
2. In other industries this is quite commonplace and hasn't led to government takeover of industries (banking comes to mind. In their regulatory implementation on the Basel III accords developed in response to the 2008 financial crisis, both the UK and EU mandate government audits to ensure compliance with stress-testing and and leverage requirements; the US is also a signatory to these accords, but I am less familiar with their implementation into US law).
I'm not personally a huge fan of this approach, but I don't find the argument that government oversight is a slippery slope to totalitarianism that persuasive. In my opinion, a much a stronger critique of mandatory government audits is that they are often not that effective at preventing the negative outcomes they set out to prevent but still massively increase the legal complexity of operating in (or entering) a given industry without falling afoul of the law.
I wonder why Josh Frysenberg didn’t mention this at his presser today. Contrast this to him repeatedly saying “digital giants must pay traditional news”... I was waiting for him to slip once and say “tradition news must pay my boss Murdoch”
No, it's newscorp. They have a 55%ish(?) readership share for print media in Australia and are very cosy with the incumbent government. Afaik it's one of the most concentrated markets in the world. (Behind Egypt and China I believe).
As others have started, when looking at events in Australia, you can consider the currently-governing Liberal party (economically liberal, socially conservative) and News Corp as effectively the same organisation.
On the other hand, the Liberal party is very hostile to the public service in general and the ABC in particular.
They should be hostile to the ABC, because their journalistic standards are terrible. I've reported 3 factual inaccuracies to them and all of them took more than a month to retract. In one case, the inaccuracy affected the entire premise of the article.
ABC's standards are that it's okay to lie as long as you retract it a month later in a tiny 10pt foot note.
On the other hand, I've personally reported a similar article inaccuracy to a News Corp writer and he replied in 10 minutes, issuing a retraction.
Similarly, I reported an article inaccuracy in a Fairfax website and they retracted in less than 2 days. No reply but as long as it's corrected I don't mind.
SBS is even worse, they actually have zero accountability for online operations.
I've had retractions printed in nearly every Australian news website, and among the 3 times I've contacted the ABC about inaccurate reporting the average response time is 1.5 months. 100% of my reports resulted in retractions (extremely delayed ones).
This is not an organisation that cares about journalistic integrity. In fact they actively eschew ethics while their private sector counterparts reply in 1/50th the time or less.
Also, you're using an ABC-produced show as evidence that the ABC isn't ethically compromised? "We investigated ourselves and found we we did nothing wrong"?
Before you accuse me of being a shill, remember I've had retractions printed in News Corp outlets, too.
Given the current Australian government's cosy relationship with a particular media company that currently dominates the media landscape here, I don't think it is coincidence.