Databrokers and companies like this rarely sell raw data. They feed the raw data into algorithms to generate thousands of scores. For example, Cambridge Analytica created a psychological profile based on raw Facebook data.
In the USA these scores are protected as a form of corporate free speech. "they are just opinions".
As long as the public debate doesn't distinguish between these two types of data, then companies will continue to be able to make claims like this which don't address the real issue. What we really need to know is: do they generate and sell derived data?
That's the problem with companies: too often they only ask if something is legal, and not if something is ethical.
Luckily our laws are the result of the larger ethical debate we as a society have. If enough people feel that something is unethical, we change the laws. Look at the GDPR in Europe. So Canadians: get upset!
I'm not sure the GDPR bans trading in personal data.
- if users give permission (which not has to be more explicit), you can still do a lot.
- You can try to claim that it would be in the valid interest of the person to share the data, even if they haven't given permission.
We really need to start looking at the long term effects of these systems om societies. After all, here in the west the market is slowly piecing together a similar infrastructure (databrokers and their ilk).
While China covets these chilling effects, here in the west we might best frame when as an unwelcome side-effect, e.g. Social Cooling. https://www.socialcooling.com/
In my experience almost all these 'take control of your data' initiatives fail to differentiate between 'raw data' and 'derived data. It's not your raw data that is valuable, but the 'derived data' that they distill from it. By comparing your data to that of other people they claim to be able to deduce your interests, sexuality, political leaning, gullability, neuroticism, etc. That's what Cambridge Analytica did: creating a psychological profile by looking at patterns in your data.
In the EU this derived data is recognised as personal data, and thus protected a bit. But in the US the databrokers say this derived data is an 'opinion' they formed about you, and is thus protected as a form of corporate free speech.
We need the wider public to understand this important distinction, but I see very little sign of the experts themselves understanding it.
The main reason I like these 'edge computing' developments is that they give access to advanced functionality without constantly reporting to the cloud what you are doing.
Then again, this being Google, I suspect this opportunity has not been taken..
- In the USA some judges use sentencing software that analyses if a defendant would be likely to commit a crime again. This software turned out to be biased against black people. https://www.propublica.org/art...
- Women were less likely to be shown Google adds for high paying jobs, as the algorithm had perceived the existing bias (women less often have high paying jobs), and then concluded that showing these adds to women would result in fewer clicks. https://www.washingtonpost.com...
- An algorithm denied pregnant women medicare. "The scholar Danielle Keats Citron cites the example of Colorado, where coders placed more than 900 incorrect rules into its public benefits system in the mid-2000s, resulting in problems like pregnant women being denied Medicaid." https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy
The general assumption is: 'algorithms use math and data, thus they must be neutral and scientific'. But it's not that simple. This site explains it: https://www.mathwashing.com/ [mathwashing.com]
"The real danger, then, is not machines that are more intelligent than we are usurping our role as captains of our destinies. The real danger is basically clueless machines being ceded authority far beyond their competence." - Daniel Denett
Why always putting people in the correct categories is mathematically impossible:
https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de
You're making my point:
- Hyperbole
- Ad hominem attacks
I don't get it. You'd think the Slashdot community would be more forgiving to a man who is able to stick so closely to the geek mentality:
- Doesn't mind failing openly.
- Cares about and respects open source ideals.
- Is running three companies that are pushing the envelope (and yes, that means you invest a lot and don't always make deadlines).
Is not so much that they create profiles about you, it's that a larger market of databrokers is getting ahold of that data too. They when start using that data as a proxy for more salient things their clients would like to know about you.
For example, in the short term the music you select says a lot about your mood. In the long term this helps to update a score about your mental stability and health. And those scores, along with others, then influences your employability score.
Staying on Facebook does not mean you agree with how Facebook operates.
It's simply a sign that:
- there are no serious competitors available to switch to. The network effect means that the value these systems have to their user base depends largely on the size of the user base. This means that new arrivals can't compete by just having better features.
- it's difficult to switch. The new GDPR law in the EU is trying to tackle, for example, vendor lock-in by forcing data portability.
Whether you call it a monopoly or not, a market where people have very little real ability to switch is an issue we should address.
- In the USA some judges use sentencing software that analyses if a defendant would be likely to commit a crime again. This software turned out to be biased against black people. https://www.propublica.org/art...
- Women were less likely to be shown Google adds for high paying jobs, as the algorithm had perceived the existing bias (women less often have high paying jobs), and then concluded that showing these adds to women would result in fewer clicks. https://www.washingtonpost.com...
- An algorithm denied pregnant women medicare. "The scholar Danielle Keats Citron cites the example of Colorado, where coders placed more than 900 incorrect rules into its public benefits system in the mid-2000s, resulting in problems like pregnant women being denied Medicaid."
https://www.theverge.com/2018/...
- "Illinois ends risk prediction system that assigned hundreds of children a 100 percent chance of death or injury" https://www.theverge.com/2017/...
The list is endless.
The general assumption is: 'algorithms use math and data, thus they must be neutral and scientific'. But it's not that simple. This site explains it: https://www.mathwashing.com/
"The real danger, then, is not machines that are more intelligent than we are usurping our role as captains of our destinies. The real danger is basically clueless machines being ceded authority far beyond their competence." - Daniel Denett
I created a device that disconnects my home network from the internet..
- during sleeping hours
- when it detects that there are no phones and laptops on the network.
It's part of an Ethical Smart Home experiment where we are designing a privacy friendly smart home. Some details:
- It has a hardware switch to reconnect at any time.
- It's fail safe. In case of power failure the internet is reconnected.
Cambridge Analytica went a step further by creating psychological profiles on all citizens, and using those to manipulate people, in some cases nudging people not to vote at all.
If the American people that Obama's level of activity was illegal, you should act on that too.
First, thanks to machine learning you only need a sample of the population to be able to semi-accurately create psychological profiles for everyone. That's why yesterday's leak of 'only' 3 million profiles is still significant.
Second, your argument amounts to 'whataboutism'. If Facebook is acting unscrupulously on an even larger scale, we should fight that too. You don't get away with murder by saying "but that guy killed 10 people!".
Everyday didn't have all the info needed to properly weigh the pro's and cons of these 'free' services. They thought they get free services in return for watching advertisements. But what they are slowly waking up to is that they also also getting these free services in return for becoming transparant to future employers, banks, insurers, and governments.
That may not be a bargain they are willing to make.
A second change is that alternatives are popping up. There are lots of companies now offer encrypted email, and among my friends more and more of them (lawyers first, consultants next, etc) are signing up for this.
We'll see similar awakenings in IOT, etc. Our job is to have alternatives ready when the scandals grow so big that society wants to switch.
Slashdot Mirror
That's like saying nobody is forced to use a mobile phone.
Social pressure, and the need to belong, is a real force in society.
Also, whether 'compulsory' or not, billions of people use Facebook. That makes it something we all should care about understanding.
Also, no, there are no real alternatives to Facebook, as the most important feature of any social network is "are my friends there".
There is great Kurtzgesagt video that explains how bacteriophages are our next best hope:
https://www.youtube.com/watch?...
Databrokers and companies like this rarely sell raw data. They feed the raw data into algorithms to generate thousands of scores. For example, Cambridge Analytica created a psychological profile based on raw Facebook data.
In the USA these scores are protected as a form of corporate free speech. "they are just opinions".
As long as the public debate doesn't distinguish between these two types of data, then companies will continue to be able to make claims like this which don't address the real issue. What we really need to know is: do they generate and sell derived data?
That's the problem with companies: too often they only ask if something is legal, and not if something is ethical.
Luckily our laws are the result of the larger ethical debate we as a society have. If enough people feel that something is unethical, we change the laws. Look at the GDPR in Europe. So Canadians: get upset!
I'm not sure the GDPR bans trading in personal data.
- if users give permission (which not has to be more explicit), you can still do a lot.
- You can try to claim that it would be in the valid interest of the person to share the data, even if they haven't given permission.
We really need to start looking at the long term effects of these systems om societies. After all, here in the west the market is slowly piecing together a similar infrastructure (databrokers and their ilk).
While China covets these chilling effects, here in the west we might best frame when as an unwelcome side-effect, e.g. Social Cooling. https://www.socialcooling.com/
You mean that a story about a massive digital measuring and judgement system that we are slowly starting to see the extent of is not 'news for nerds'?
The social layer is a vital force multiplier. There is no force quite like social pressure.
In my experience almost all these 'take control of your data' initiatives fail to differentiate between 'raw data' and 'derived data. It's not your raw data that is valuable, but the 'derived data' that they distill from it. By comparing your data to that of other people they claim to be able to deduce your interests, sexuality, political leaning, gullability, neuroticism, etc. That's what Cambridge Analytica did: creating a psychological profile by looking at patterns in your data.
In the EU this derived data is recognised as personal data, and thus protected a bit. But in the US the databrokers say this derived data is an 'opinion' they formed about you, and is thus protected as a form of corporate free speech.
We need the wider public to understand this important distinction, but I see very little sign of the experts themselves understanding it.
Doesn't the same go for all money since we went off the gold standard?
The main reason I like these 'edge computing' developments is that they give access to advanced functionality without constantly reporting to the cloud what you are doing.
Then again, this being Google, I suspect this opportunity has not been taken..
Here are some examples:
- In the USA some judges use sentencing software that analyses if a defendant would be likely to commit a crime again. This software turned out to be biased against black people.
https://www.propublica.org/art...
- Women were less likely to be shown Google adds for high paying jobs, as the algorithm had perceived the existing bias (women less often have high paying jobs), and then concluded that showing these adds to women would result in fewer clicks.
https://www.washingtonpost.com...
- An algorithm denied pregnant women medicare. "The scholar Danielle Keats Citron cites the example of Colorado, where coders placed more than 900 incorrect rules into its public benefits system in the mid-2000s, resulting in problems like pregnant women being denied Medicaid."
https://www.theverge.com/2018/3/21/17144260/healthcare-medicaid-algorithm-arkansas-cerebral-palsy
- Google's sentiment analysis algorithms gave gay related words a low score.
https://tech.slashdot.org/stor...
The list is endless.
The general assumption is: 'algorithms use math and data, thus they must be neutral and scientific'. But it's not that simple. This site explains it: https://www.mathwashing.com/ [mathwashing.com]
"The real danger, then, is not machines that are more intelligent than we are usurping our role as captains of our destinies. The real danger is basically clueless machines being ceded authority far beyond their competence." - Daniel Denett
Why always putting people in the correct categories is mathematically impossible:
https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de
Books on the subject:
https://nyupress.org/books/978...
https://weaponsofmathdestructi...
http://www.hup.harvard.edu/cat...
You're making my point:
- Hyperbole
- Ad hominem attacks
I don't get it. You'd think the Slashdot community would be more forgiving to a man who is able to stick so closely to the geek mentality:
- Doesn't mind failing openly.
- Cares about and respects open source ideals.
- Is running three companies that are pushing the envelope (and yes, that means you invest a lot and don't always make deadlines).
Is not so much that they create profiles about you, it's that a larger market of databrokers is getting ahold of that data too. They when start using that data as a proxy for more salient things their clients would like to know about you.
For example, in the short term the music you select says a lot about your mood. In the long term this helps to update a score about your mental stability and health. And those scores, along with others, then influences your employability score.
Spotify has been sharing data on your mood since 2016:
https://betanews.com/2016/07/2...
In the coming years the scandals will increasingly involve databrokers. The general populace has no idea what is going on in that market.
There is a steady stream of negative stories about Tesla popping up recently. So many that it is starting to feel a bit artificial.
Staying on Facebook does not mean you agree with how Facebook operates.
It's simply a sign that:
- there are no serious competitors available to switch to. The network effect means that the value these systems have to their user base depends largely on the size of the user base. This means that new arrivals can't compete by just having better features.
- it's difficult to switch. The new GDPR law in the EU is trying to tackle, for example, vendor lock-in by forcing data portability.
Whether you call it a monopoly or not, a market where people have very little real ability to switch is an issue we should address.
Here are some examples:
- In the USA some judges use sentencing software that analyses if a defendant would be likely to commit a crime again. This software turned out to be biased against black people. https://www.propublica.org/art...
- Women were less likely to be shown Google adds for high paying jobs, as the algorithm had perceived the existing bias (women less often have high paying jobs), and then concluded that showing these adds to women would result in fewer clicks.
https://www.washingtonpost.com...
- An algorithm denied pregnant women medicare. "The scholar Danielle Keats Citron cites the example of Colorado, where coders placed more than 900 incorrect rules into its public benefits system in the mid-2000s, resulting in problems like pregnant women being denied Medicaid." https://www.theverge.com/2018/...
- "Illinois ends risk prediction system that assigned hundreds of children a 100 percent chance of death or injury"
https://www.theverge.com/2017/...
The list is endless.
The general assumption is: 'algorithms use math and data, thus they must be neutral and scientific'. But it's not that simple. This site explains it: https://www.mathwashing.com/
"The real danger, then, is not machines that are more intelligent than we are usurping our role as captains of our destinies. The real danger is basically clueless machines being ceded authority far beyond their competence." - Daniel Denett
When you take a model of reality, and then start fetishizing it as a template.
This is why I love Slashdot. Thanks for this!
I created a device that disconnects my home network from the internet..
- during sleeping hours
- when it detects that there are no phones and laptops on the network.
It's part of an Ethical Smart Home experiment where we are designing a privacy friendly smart home. Some details:
- It has a hardware switch to reconnect at any time.
- It's fail safe. In case of power failure the internet is reconnected.
They just announced they are building a Giga-factory in China that will produce both batteries and cars: https://electrek.co/2018/05/14...
Cambridge Analytica went a step further by creating psychological profiles on all citizens, and using those to manipulate people, in some cases nudging people not to vote at all.
If the American people that Obama's level of activity was illegal, you should act on that too.
First, thanks to machine learning you only need a sample of the population to be able to semi-accurately create psychological profiles for everyone. That's why yesterday's leak of 'only' 3 million profiles is still significant.
Second, your argument amounts to 'whataboutism'. If Facebook is acting unscrupulously on an even larger scale, we should fight that too. You don't get away with murder by saying "but that guy killed 10 people!".
Everyday didn't have all the info needed to properly weigh the pro's and cons of these 'free' services. They thought they get free services in return for watching advertisements. But what they are slowly waking up to is that they also also getting these free services in return for becoming transparant to future employers, banks, insurers, and governments.
That may not be a bargain they are willing to make.
A second change is that alternatives are popping up. There are lots of companies now offer encrypted email, and among my friends more and more of them (lawyers first, consultants next, etc) are signing up for this.
We'll see similar awakenings in IOT, etc. Our job is to have alternatives ready when the scandals grow so big that society wants to switch.
That's because, historically speaking, men are not a repressed group.
We're trying to head to towards a general large scale equilibrium. That means sometimes there will be suboptimal local situations.
I recommend you deal with it. Women have for centuries.