Social and Ethical Implica0ons of Big Data Analy0cs
School of Compu2ng and Informa2on Systems Pauline Lin
©University of Melbourne 2022
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Big data analytics
– Stakeholders
– Processes & implica5ons
– 10 simple rules for responsible and ethical big data research
COMP20008 Elements of Data Processing
Question- what is the story?
Top 3 silicon Top 3 auto (car) valley (2014) makers (1990)
$247 billion
$250 billion
Number of employees
1.2 million employee
Market capitalisation
$1.09 trillion
$36 billion
(Zuboff 2015)
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What is Big Data Analytics? What’s different?
• The ability to collect, store, and process increasingly large and complex data sets from a variety of sources, into compe55ve advantage (LaValle and Lesser 2013)
• Bigdatamanagementcapabili5es
– Volume,VarietyandVelocity(3Vs)+Veracity(4Vs)
• Algorithmstoprocessbigdata
– Advanced sta2s2cal and computa2onal techniques to process large,
unstructured and fast data
Is this a sufficient defini/on?
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Creepy uses of data in the media
Target exposing a teen girl’s pregnancy
– Father: “My daughter got this in the mail!” he said. “She’s still in high school, and
you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her
to get pregnant?”
• https://www.businessinsider.com.au/the-incredible-story-of-how-target-exposed-a-teen-girls-pregnancy-2012-2 https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=1&_r=1&hp
Facebook’s 2012 secret mood experiment
– howpeoplereacttoanemotionalcontagionprocess
– filtered users’ news feeds – the flow of comments, videos, pictures and web links
posted by other people
– 689,000usersaffected
– https://www.theguardian.com/technology/2014/jun/29/facebook-users-emotions-news- feeds Cambridge Analytica (we have discussed previously ..)
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Consequences of Big Data Analy=cs
• Posi5veconsequences
– Tracking criminals, higher product margins, new business models,
improved healthcare and …
• Nega5veconsequences:
– Misuse of personal informa2on, breaching privacy, profiling of
individuals, discrimina2on and …
• Whereistheboundary?Whatisthebalance?
– There is no agreement on what is ethical and what is not!
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BDA is Not only about technology
• Technological view (3Vs) does not help to understand unethical use
– Technology is neutral in nature
– It does not consider the underlying processes that are enabled
– It does not consider the stakeholders that are involved and influenced – 3Vs do not consider either people or process
– A new social phenomenon, a new market economy
• We need further (non technical) perspec:ves on big data analy:cs – It is not OK to exclude social from the defini2on
COMP20008 Elements of Data Processing
COMP20008 Elements of Data Processing
Social and Ethical Implica0ons of Big Data Analy0cs
School of Compu2ng and Informa2on Systems Pauline Lin
©University of Melbourne 2022
BDA is Not only about technology
• Technological view (3Vs) does not help to understand unethical use
– Technology is neutral in nature
– It does not consider the underlying processes that are enabled
– It does not consider the stakeholders that are involved and influenced – 3Vs do not consider either people or process
– A new social phenomenon, a new market economy
• We need further (non technical) perspec:ves on big data analy:cs – It is not OK to exclude social from the defini2on
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Stakeholder View on BDA
The exchange of data is characterised by its 4Vs.
Own and benefit from data
Individual
Create big data
Organizations
Guide and regulate
Unequal exchanges between stakeholders
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BDA from Social Perspec>ve
• Interac)ons among stakeholders
• Data is contributed, collected, extracted, exchanged, sold, shared, and processed for the purpose of predic)ng and modifying human behaviour in the produc)on of economic or social value.
• BDA involves several processes, discussed next
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Process 1: Data Extraction
Process 1: Data Extrac>on
• Data extraction, not data collection
• Google Streetview (“single greatest breach in the history of privacy”)
– https://www.theguardian.com/technology/2010/may/15/google-admits-storing-private-data • Our everydayness quantified
• Incursions into legally and socially undefended territory
• Google has the largest unpaid number of employees • “You’re not the customer, you’re the product ..”
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Process 2: Data commodifica>on
• Secondary markets and hidden value chains
• Sell personal data un)l it turns into waste
• Big data as a new industry (secondary markets)
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(Martin 2015)
• hKps://www.atdata.com
• hKp://www.iriworldwide.com/en-us/ • hKp://www.intelius.com/
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Process 3: Decision Making
• Big Data Quality (Veracity)
– Data accuracy for aggregated data
– What’s the quality criteria for a social media post?
– Completeness of our digital iden)ty
– Mosaic effect
– “When is a car not a car?”
– Game: hLp://celebrityguesswho.com/#2
– Meaning dependent on the context
– Is how I act on social media a true representa2on of who I am? COMP20008 Elements of Data Processing
• hLps://www.theguardian.com/technology/2016/nov/02/admiral-to-price- car-insurance-based-on-facebook-posts
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Process 3: Decision Making
Process 3: Decision Making – cont.
• Data Analysis
– Predic2ons based on the past
– How can I redefine myself?
– In what context is it legi2mate to make a predic2on about someone?
– Predic2ons oYen based on correla2ons (not causa2ons)
– What about outliers? (what if I don’t fit into a predefined category??)
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• Do you want to stop global warming? BECOME A PIRATE!
See also: h/ps://www.tylervigen.com/spurious-correla
(Zuboff 2015 and Mar
Data is contributed, collected, extracted, exchanged, sold, shared, and processed for the purpose of predic)ng and modifying human behaviour in the produc)on of economic or social value.
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Oversight and regulate by societal actors
• EU General Data Protec)on Regula)on (enforced since May 2018) • Aims to regulate and protect data privacy for all EU ci)zens.
– Penalty 4% of annual global turnover of the organiza6ons.
• The consent
– should be clear, concise, not too long and intelligibly wri5en— should a5ach the reasons of data collec8on and analyses.
– individuals have the right to withdraw the consent with the same easiness that they have previously agreed with.
• Accessing individual’s data
– Individuals have the right to ask for a copy of their personal data together with informa6on regarding the processing and purpose of data collec6on and analyses from a controller
– Individuals have the right of data portability, which means that they can transfer their data from one controller to another.
h#p://www.eugdpr.org/eugdpr.org.html
COMP20008 Elements of Data Processing
COMP20008 Elements of Data Processing
Social and Ethical Implications of Big Data Analytics
School of Compu2ng and Informa2on Systems Pauline Lin
©University of Melbourne 2022
Responsible big data research
• From Zook et al (Plos Comp Bio 2017)
– http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005399
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• Acknowledge that data are people and can do harm
– All data are people un2l proven otherwise
• Social media
• Heart rates from Youtube videos
• Ocean measurements that change property risk profiles
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• Recognize that privacy is more than a binary value
– Privacy is contextual and situa2onal
– Single Instagram photo versus en2re history of social media posts – Privacy preferences differ across individuals and socie2es
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• Guard against the reidentification of your data
– Metadata associated with photos
– Reverse image search – connect dating and professional profiles
– Difficult to recognize the vulnerable points a-priori!
• Battery usage on a phone – can reveal a person’s location
– Unintended consequence of 3rd party access to phone sensors
– When datasets thought to be anonymized are combined with other variables, it may result in unexpected reidentification
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• Practice ethical data sharing
– Seeking consent from participants to share data
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• Consider the strengths and limitaHons of your data; big does not automaHcally mean beJer
– Document the provenance and evolu2on of your data. Do not overstate clarity; acknowledge messiness and mul2ple meanings.
• is a Facebook post or an Instagram photo best interpreted as an approval/disapproval of a phenomenon, a simple observa2on, or an effort to improve status within a friend network?
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• Debate the tough, ethical choices/issues
– importance of deba2ng the issues within groups of peers
• Examples men2oned earlier
– Facebook emo2onal contagion – Exposing teen girl’s pregnancy
• More recently, Google Duplex
– hYps://www.youtube.com/watch?v=D5VN56jQMWM
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• Develop a code of conduct for your organizaHon, research community, or industry
– Are we abiding by the terms of service or users’ expecta2ons? – Does the general public consider our research “creepy”?
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• Design your data and systems for auditability
– Plan for and welcome audits of your big data prac2ces.
– Systems of auditability clarify how different datasets (and the subsequent analysis) differ from each other, aiding understanding and crea2ng beYer research.
• “For example, many types of social media and other trace data are unstructured, and answers to even basic ques2ons such as network links depend on the steps taken to collect and collate data.”
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• Engage with the broader consequences of data and analysis pracHces
– Recognize that doing big data research has societal-wide effects
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• Know when to break these rules
– Natural disaster
– Public health emergency
– Hos2le enemy –…
– It may be important to temporarily put aside ques2ons of individual privacy in order to serve a larger public good.
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Summary: What can we do?
• We need to empower individuals
– Educate individuals, raise social awareness
– Provide data access and control (e.g. Google ac2vity)
• hYp://www.abc.net.au/4corners/stories/2017/04/10/4649443.htm • Define and develop a culture of acceptable data use
– Organiza2ons should internalize the costs
– Genuine consent from individuals
– Be transparent and clearly communicate intent of data collec2on and analy2cs/ai
– Adopt rules for responsible big data research
– Provide data control to individuals
COMP20008 Elements of Data Processing
COMP20008 Elements of Data Processing
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