Ethical considerations

Lecture 23: Ethical considerations

-appreciate that there exist ethical considerations in the context of a data wrangling/data science/data analytics project

Data extraction, not data collection

• Google Streetview ("single greatest breach in the history of privacy”)

• 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 ..”

-be able to explain who are the stakeholders in big data analytics and what are their perspectives? Understand what the tensions are between their motivations and what is the degree of power balance between them.

explain who are the stakeholders in big data analytics

what are their perspectives?

Data is contributed, collected, extracted, exchanged, sold, shared, and processed for the purpose of predicting and modifying human behaviour in the production of economic or social value.

what the tensions are between their motivations?

Process 1: Data Extraction

•Data extraction, not data collection : Google Streetview ("single greatest breach in the history of privacy”)

•Our everydayness quantified

•Incursions into legally and socially undefended territory

Process 2: Data commodification: secondary markets and hidden value chains

•Sell personal data until it turns into waste

•Big data as a new industry (secondary markets)

Process 3: Decision Making

•Big Data Quality (Veracity)

–Data accuracy for aggregated data

–Completeness of our digital identity

–Mosaic effect

–Meaning dependent on the context

•Data Analysis

–Predictions based on the past

–How can I redefine myself?

–In what context is is legitimate to make a prediction about someone?

–Predictions often based on correlations (not causations)

–What about outliers? (what if I don’t fit into a predefined category??)

•Data Visualization

–Decision making and presentation biases

Process 4: Control and monitoring

•Pervasive monitoring now possible using sensors, Internet of Things technology

•Everyone is observed, organisations make money of observing others, collect data, sell data, make offers, induce dependence

•What happens to social trust?

•Surveillance is the precise opposite of the trust-based relationships

•Free market economy versus Surveillance Economy

Process 5: Experiments

Rewards and punishments

what is the degree of power balance between them?

•EU General Data Protection Regulation (enforced since May 2018)

•Aims to regulate and protect data privacy for all EU citizens.

–Penalty 4% of annual global turnover of the organizations.

•The consent

–should be clear, concise, not too long and intelligibly written—should attach the reasons of data collection 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 information regarding the processing and purpose of data collection 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.

-appreciate the difference between the following two perspectives on the definition of (big) data analytics

Perspective 1: The ability to collect, store, and process increasingly large and complex data sets from a variety of sources, into competitive advantage.(more technology)

Perspective 2: Data is contributed, collected, extracted, exchanged, sold, shared, and processed for the purpose of predicting and modifying human behaviour in the production of economic or social value.(more social)

• p1 is a organization perspective, p2 is a social perspective
• P1 is the basic explanation, P2 is BDA from social perspective

-appreciate the motivation for each of the 10 simple rules for responsible big data research. (you need not memorise this list, but should be able to comment on a rule if it is mentioned)

Rule 1

•Acknowledge that data are people and can do harm

–All data are people until proven otherwise

•Social media

•Heart rates from Youtube videos

•Ocean measurements that change property risk profiles

Rule 2

•Recognize that privacy is more than a binary value

–Privacy is contextual and situational

–Single Instagram photo versus entire history of social media posts

–Privacy preferences differ across individuals and societies

Rule 3

•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

Rule 4

•Practice ethical data sharing

–Seeking consent from participants to share data

Rule 5

•Consider the strengths and limitations of your data; big does not automatically mean better

–Document the provenance and evolution of your data. Do not overstate clarity; acknowledge messiness and multiple meanings.

•is a Facebook post or an Instagram photo best interpreted as an approval/disapproval of a phenomenon, a simple observation, or an effort to improve status within a friend network?

Rule 6

•Debate the tough, ethical choices/issues

–importance of debating the issues within groups of peers

•Examples mentioned earlier

–Facebook emotional contagion

–Exposing teen girl’s pregnancy

•More recently, Google Duplex

Rule 7

•Develop a code of conduct for your organization, research community, or industry

–Are we abiding by the terms of service or users’ expectations?

–Does the general public consider our research “creepy”?

Rule 8

•Design your data and systems for auditability

–Plan for and welcome audits of your big data practices.

–Systems of auditability clarify how different datasets (and the subsequent analysis) differ from each other, aiding understanding and creating better research.

•“For example, many types of social media and other trace data are unstructured, and answers to even basic questions such as network links depend on the steps taken to collect and collate data.”

Rule 9

•Engage with the broader consequences of data and analysis practices

–Recognize that doing big data research has societal-wide effects

Rule10

•Know when to break these rules

–Natural disaster

–Public health emergency

–Hostile enemy

It may be important to temporarily put aside questions of individual privacy in order to serve a larger public good.