Autonomy/Freedom
Dignity
Privacy
Fairness
Equity/Solidarity
Social wellbeing/Sustainability
Performance
Safety
Explainability/Accountability
A chatbot responds to questions about history, science and the arts instantly, and so delivers civilization’s accumulated knowledge with an efficiency that withers the ability to research and to discover for ourselves (Why exercise thinking when we have easy access to everything we want to know?) Is perfect knowledge worth intellectual stagnation?
Compared to deaths per car trip today, how great an increment would be acceptable to switch to all-driverless cars, ones prone to the occasional glitch and consequent, senseless wreck?
If an AI picks stocks, predicts satisfying career choices, or detects cancer, but only if no one can understand how the machine generates knowledge, should it be used?
What’s worth more, understanding or knowledge? (Knowing, or knowing why you know?)
Which is primary, making AI better, or knowing who to blame, and why, when it fails?
What, and how much will we risk for better accuracy and efficiency?
What counts as risk, and who takes it?
A driverless car AI system refines its algorithms by imitating driving habits of the human owner (driving distance between cars, accelerating, breaking, turning radiuses). The car later crashes. Who is to blame?
Compared to others, our principles lean toward human freedom/libertarianism, and are more streamlined. Small differences.
Ethics Guidelines for Trustworthy AI
AI High Level Expert Group, European Commission
https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai
European Ethical Charter on the Use of Artificial Intelligence in Judicial Systems and their Environment
European Commission for the Efficiency of Justice
https://rm.coe.int/ethical-charter-en-for-publication-4-december-2018/16808f699c
Ethical and Societal Implications of Data and AI
Nuffield Foundation
https://www.nuffieldfoundation.org/sites/default/files/files/Ethical-and-Societal-Implications-of-Data-and-AI-report-Sheffield-Foundat.pdf
The Five Principles Key to Any Ethical Framework for AI
New Statesman, Luciano Floridi and Lord Clement-Jones
https://tech.newstatesman.com/policy/ai-ethics-framework
Postscript on Societies of Control
October 1997, Deleuze
/Library: Deleuze, Foucault, Discipline, Control.pdf
A Declaration of the Independence of Cyberspace
John Perry Barlow
https://www.eff.org/cyberspace-independence
Deontological
Guiding value
Tradition
Rules for action
A set of moral directives seems to recur through historical times and places, and in diverse religious, social and political contexts. This endurability can be taken to legitimize the guidelines. While no single list perfectly contains the recurring imperatives, typically there are:
Duties to self:
• Preservation
• Develop my own talents
• Fidelity/Integrity (Be true to myself)
Duties to others:
• Honesty (Be true to others)
• Beneficence (Help others as reasonably possible)
• Reparation (Repair harm done to others)
• Gratitude
Advantages/Drawbacks
Because ethical legitimacy stands on widespread historical acceptance of the moral rules, the guidelines are familiar, commonly employed, and easily applied to experience. But, multiple duties may yield contradictory imperatives. For example, a student may have money to buy a new computer (develop own talents) or donate to a scholarship fund (beneficence), but not both. No formula has been discovered to reliably adjudicate these duty conflicts.
Deontological
Guiding value
Religious faith
Rule for action
Follow the command of God (or Gods in the case of polytheism, as in ancient Greece).
Advantages/Drawbacks
Divine sanction fortifies confidence in moral regulation, but difficulties remain in decoding how the regulation should be applied on the human level, as exemplified by conflicting interpretations of religious texts, and by the story of Job in the Bible.
Deontological
Guiding value
Equality
Rule for action (Aristotle version)
Treat people identically unless they differ in ways relevant to the situation. Differences between people that are relevant should yield proportionately unequal treatment. (Treat equals equally, and unequals unequally.)
Advantages/Drawbacks
Aristotelian fairness yields objectively correct responses to dilemmas. But, it can be difficult to define the “equal” and “unequal” in practice, especially in terms of what counts as relevant to a situation. For example, a five foot woman and a six foot man each pay the same price for an airplane ticket. Should they receive the same legroom?
Rule for action (John Rawls version)
Decide without regard for how your conclusion affects you personally. The theory can be presented as a thought experiment in which deciders know nothing about themselves (age, education, preferences, and so on) and after making a judgement, those qualities are assigned to them randomly. So, with respect to the airplane ticket and legroom, deciders must imagine that their height will be assigned by lottery after pronouncing their decision.
Advantages/Drawbacks
An effective strategy in some situations. For example, when sharing a cookie between two friends, one breaks it in half and the other chooses the side: the person breaking the cookie operates from behind the veil of ignorance in that they don’t know how they will be affected by their own portioning. But, in many situations it’s nearly impossible for deciders to blindfold themselves to their own reality within the decision being made.
Deontological
Guiding values
Rationality, Dignity
Rule for action (Rationality version)
Actions must be universalizable, meaning that it is possible to rationally conceive of everyone taking the action all the time. Lying, for example, cannot be universalized because if everyone lied all the time, no one would take anything seriously, so no one could successfully lie. Attempting to lie therefore contradicts itself. Restated, lying cannot make sense because universalizing the practice doesn’t make everything false, instead, it creates a reality like an adventure movie which is neither honest nor dishonest: it’s not true, but it’s also not misleading, just entertaining.
Advantages/Drawbacks
Powerfully objective, but practically torturous: imagine never lying about anything, ever.
Rule for action (Dignity version)
Treat others as ends in themselves, and never only as means. Because others’ independent life projects must be respected, treating them as tools or instruments serving my own projects becomes inadmissible. The difference can be understood in the distinction between collaboration and exploitation: the first treats others as ends in themselves, the second treats them as tools for use.
Advantages/Drawbacks
The ideal of universal dignity as inherent to human being is inspiring in the abstract, but does a remorseless murderer deserve to be treated as dignified? More practically, if humans may not be treated as mere instruments, what does that mean for our interactions with cashiers?
Deontological
Guiding value
Freedom
Rule for action
Do what you want, up to the point where you interfere with others doing the same. Libertarian models extend freedom expressions from our minds and bodies, to our possessions and the fruits of our labors. In every case, freedom means applying rules to yourself, and obeying them.
Advantages/Drawbacks
Freedom maximization empowers individual experience: we are liberated to choose our own identities and destinies. But, the theory does little to resolve conflicts between individuals or support collective wellbeing. Zoning laws, for example, conflict with libertarian thought.
Consequentialist
Guiding value
Happiness
Rule for action
Bring the greatest good and happiness to the greatest number. Total wellbeing is calculated by summing the condition of every member of society. Then, those actions raising the happiness count – or diminishing overall suffering – are implemented. Happiness can be defined hedonically (Bentham, physical pleasures), or idealistically (Mill, intellectual pleasures). In both cases, the happiness calculation must account for everyone, as far into the future as the effects of an action may be reasonably projected.
Advantages/Drawbacks
Overall wellbeing and the collective welfare is attractive in the abstract, but balances against injustices to flesh and blood individuals: if a fatal disease can be cured with a lethal experiment on a human, and there are no volunteers, a pure utilitarian will coerce participation. Another drawback is the difficulty in accurately calculating happiness in a world of diverse people with unpredictable futures.
Consequentialist
Guiding value
Happiness
Rule for action
Bring the greatest good and happiness to the greatest number, not including the actor. Total wellbeing is calculated by summing the condition of every member of society except the person doing the calculating. Then, those actions raising the happiness count – or diminishing overall suffering – are implemented. Happiness can be defined hedonically (Bentham, physical pleasures), or idealistically (Mill, intellectual pleasures). In both cases, the happiness calculation must account for everyone except the actor, and the calculating must stretch as far into the future as the action’s effects may be reasonably projected.
Advantages/Drawbacks
Selflessly seeking collective wellbeing sounds noble, but is altruism based on generosity, or is it disguised self-abnegation?
Consequentialist
Guiding value
Happiness
Rule for action
Bring the greatest good and happiness to me. Happiness can be defined hedonically (Bentham, physical pleasures), or idealistically (Mill, intellectual pleasures). Some egoists view the ethics as an inescapable psychological reality: we are all out for ourselves whether we admit it or not. Others support egoism as a rational choice, especially those promoting Enlightened Egoism, the view that acting to benefit others is desirable as an efficient strategy for self-service. More, the best way to bring happiness to others may be to seek it for oneself (Adam Smith, invisible hand).
Advantages/Drawbacks
No one knows my own happiness better than I do, so it makes sense that I hold the responsibility to seek it. Also, if the invisible hand idea is persuasive, then enlightened egoism becomes preferable to utilitarianism and altruism by default. But, egoism requires that others, even those closest to us, be categorized as unworthy of independent moral consideration.
Virtue
Guiding value
Good living (Eudaimonia)
Rule for action
As opposed to deontological and consequentialist theories which attempt to form good rules for action, virtue ethics attempts to form good people, and then trust that they will act civically in a complex world. Virtue is a skill, one that is acquired intellectually through study, and also practically as part of youthful development in social institutions: families, schools, churches, the military, workplaces, sports teams, civic associations. As an example of virtue happening, a college student may attend an ethics lecture in the afternoon, and in the evening apply the lessons while participating in a water polo competition where the virtue of winning with humility (or losing with dignity) is practiced.
Advantages/Drawbacks
Because virtue is a skill, the attainment of mastery provides satisfaction, meaning virtue is its own reward: doing good feels good, and together they define a good life. However, exactly what counts as being virtuous is hard to define since different societies teach their youth different lessons and embody distinct practices for managing crime and punishment, vows of marriage and family responsibilities, the treatment of the vanquished in war and sporting competitions, and so on.
Post-Nietzschean (Nietzsche/Heidegger)
Guiding value
Authenticity
Rule for action
As opposed to the traditional ethical obligation for individuals to aspire to an ideal identity as defined by their society, the ethics of authenticity asks you to be true only to yourself, whoever you may be. The precedent requirement is to determine who, exactly, you are. Nietzsche proposed the Eternal Return thought experiment, Heidegger proposed anxiety in the face of death. In both cases, the result is an understanding one’s unique life projects as distinct from broader social expectations. The subsequent ethical imperative is to engage those projects.
Advantages/Drawbacks
In a world without objective right and wrong, being true to myself provides a direction and use for my freedom. When the authentic person is an artist, the theory works well, but when the authentic person is a natural born murderer, the theory is less felicitous.
Post-Nietzschean
Guiding value
Nativity
Rule for action
Traditionally, ethicists have worked to escape the idiosyncrasies of particular times and places by developing theories sufficiently abstract to apply universally. Culturalism reverses the tradition by embracing the idiosyncrasies: a community’s native beliefs are accepted as their legitimate moral rules, and the task of ethics is to learn the local practices, customs, and traditions, and then fit into them.
Advantages/Drawbacks
Respect for distinct cultures and traditions is maximized, but hope for ethical progress recedes because respecting another culture’s moral rules goes equally whether those rules seem noble, or barbaric.
Post-Nietzschean (Habermas)
Guiding value
Consensus
Rule for action
Gather those involved in a conflict and discuss until reaching a shared resolution. The discourse must be rational and peaceful: participants comprehend their own agreements, and arrive without coercion. There is a partial analogy to American courthouse jury decisions here in that agreement is by informed consent, and the fact of agreement is the decision’s judicial/ethical legitimacy.
Advantages/Drawbacks
Provides a broad range of initially possible solutions since everything is on the table for discussion. But, everything on the table means a lot of talking since every conflict must be addressed and resolved from scratch.
Post-Nietzschean (Gilligan)
Guiding value
Care
Rule for action
As opposed to concentrating on individuals, care ethics focuses on the links uniting people in their social networks. The aim is to strengthen the web of bonds, especially with those who are nearest. Families are a commonly cited example. For instance, a relative suffering drug addiction may receive a disproportionately large share of resources and concern. Or, if the addict becomes dangerously toxic, links to the family may be severed. In both scenarios, fortifying the web of familial care is the guiding concern, not any particular member.
Advantages/Drawbacks
Fortifying our intimate social networks conforms to intuitive feelings: many of us would rescue a sister before a stranger if only one could be saved. But, the theory can lead to tribalism, a mafia-family approach to civil co-existence.
Post-Nietzschean (Deleuze)
Guiding value
Originality
Rule for action
The traditional ethical split between wrong and right is replaced by stagnation and creation. Creativity as an ethics repurposes customary elements of experience for original uses. A common example is slang: redirecting a language’s standard words for divergent meanings. Another example is the reorienting of web platforms, including the exploitation of LinkedIn as a dating site. As an ethics, creativity works within its native reality instead of coming from outside, its twists the elements of experience away from orthodox uses as opposed to destroying them, and it escapes conventions as opposed to overthrowing them.
Advantages/Drawbacks
Originality as the highest value can be individually invigorating. But, if the driving reason we innovate is to go on and create something else, the interminability is daunting, as in the endless “Yes, and…” of improvisation theater.
Updated 2021. Mostly curated from Casey Fiesler’s collection here.
Human Centered Data Science | University of Washington | eScience Institute | Jonathan T. Morgan |
Information Ethics and Privacy in the Age of Big Data | Montana State University | Library/Honors College | Scott W. H. Young and Sara Mannheimer |
Ethical and Policy Dimensions of Information Technology and New Media | University of Colorado Boulder | Information Science | Casey Fiesler |
Ethical Issues in Technology Design | Columbia University - Teachers College | Mathematics Science and Technology | Joey J. Lee |
Data Stories | Carnegie Mellon University | English | Christopher Warren |
The Ethics and Governance of Artificial Intelligence | Harvard University | Harvard Law School | Jonathan Zittrain |
The Ethics and Governance of Artificial Intelligence | Massachusetts Institute of Technology, Harvard University | MIT Media Lab | Joi Ito, Jonathan Zittrain |
Big Data,Big Responsibilities | University of Pennsylvania - The Wharton School | Legal Studies & Business Ethics | Kevin Werbach |
Algorithms and Society | Northwestern University | Computer Science and Communication Studies | Brent Hecht |
Ethics of Data Science | New York University | Steinhardt (Mike Cottrell College of Business) | Laura Norén |
Introduction to Data Ethics | University of Milan | ICT | Marco Cremonini |
Algorithms and Big Data | University of Southern California | Comm | Mike Ananny |
Fairness in Machine Learning | UC Berkeley | Computer Science | Moritz Hardt |
Shannon Vallor, Ph.D.
William J. Rewak, S.J. Professor of Philosophy, Santa Clara University
Solon Barocas, Moritz Hardt, Arvind Narayanan
From: Oxford
Luciano Floridi
From: Oxford
Quinn, Michael J.
From: Wiley
Herman T. Tavani
From: The Business Ethics Workshop, 3rd Edition. Boston, USA: Boston Academic Publishing / Flatworld Knowledge. pp. 349-376 (2018)
James Brusseau
Pace University, New York City
Patrick Lin
The COMPAS-ProPublica debate about bias in algorithmic recidivism assessments. What happens when AI evaluates who stays in jail, and who gets out on bail, or on parole?
Ability to message (text/image/voice/+?) to human interlocuters with full/partial/no disclosure.
What should be done on the road? When decisions go wrong, who shoulders responsibility?
Video and artificial intelligence monitoring of drivers promises safety, but what do we sacrifice when the machine stops us from ever breaking the law?
The explainability/interpretability distinction, and the question of how we relate to our environment when we can’t understand why decisions are made.
As opposed to 20th century marketing which sought to create needs and then satisfy them with goods and services, it is now more cost-effective to discern consumers trues desires (even when they don’t realize them themselves). Predictive analytics combines with big data and market forces to satisfy consumers as they are. Is that satisfying?
The ethics of big data and privacy in the Tinder Live comedy show.
A woman walks into a bar wearing Google Glass and begins filming. Ethical issues follow.
What happens when AI evaluates job candidates?
What happens when your sexual orientation can be decoded from pictures of your face?
Hidden camera by Forbrugerrådet Tænk w/ English captions
In Seattle, Surveillance Camera Man sticks his camera in peoples’ faces to find out what they will do. He ends up shedding light on surveillance society. (30 Mins)
Ethics, the Trolley Problem, Driverless Cars
Personal Information Tradeoffs
James Brusseau
Bruno Gransche
Burton, E., Goldsmith, J., Koenig, S., Kuipers, B., Mattei, N., & Walsh, T
Accenture & Northeastern University
Data Ethics EU
Open Data Institute
Open Roboethics Institute
Benno Keller, Geneva Association
Capgemini Research Institute
Ray Perrault and Saurabh Mishra
VDE Association for Electrical, Electronic & Information Technologies and Bertelsmann Stiftung
Berkman Klein Center, Harvard University
IBM
IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems
Jess Whittlestone, Rune Nyrup, Anna Alexandrova, Kanta Dihal, Stephen Cave, Leverhulme Centre for the Future of Intelligence, University of Cambridge.
Alan Winfield
High-Level Expert Group on AI, European Commission
Germany’s Federal Government
Postscript on Societies of Control
October, 1997, Deleuze
/Library: Deleuze, Foucault, Discipline, Control.pdf
A Declaration of the Independence of Cyberspace
John Perry Barlow
https://www.eff.org/cyberspace-independence
Ethics For The New Surveillance,
The Information Society, Vol. 14, No. 3, 1998. Gary T. Marx
http://web.mit.edu/gtmarx/www/ncolin5.html
What’s New About the New Surveillance? Classifying for Change and Continuity
Surveillance & Society 1(1):9-29 2002. Gary T. Marx
http://www.surveillance-and-society.org/articles1/whatsnew.pdf
Machine Ethics – Association Advancement AI Papers from the 2005 Symposia
http://www.aaai.org/Papers/Symposia/Fall/2005/FS-05-06/FS05-06-020.pdf
Ethical Issues in Advanced Artificial Intelligence (Paper clip thought experiment)
Cognitive, Emotive and Ethical Aspects of Decision Making in Humans and in Artificial Intelligence, Vol. 2, 2003 Nick Bostrom
https://www.nickbostrom.com/ethics/ai.html
Responsible AI—Two Frameworks for Ethical Design Practice
IEEE, 2020 by
Dorian Peters ; Karina Vold ; Diana Robinson ; Rafael A. Calvo
https://ieeexplore.ieee.org/document/9001063
The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions
ACM AIES, 2019 by
Jess Whittlestone, Rune Nyrup, Anna Alexandrova and Stephen Cave
https://www.aies-conference.com/2019/wp-content/papers/main/AIES-19_paper_188.pdf
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
UC-Berkeley, arXiv e-print by
Roel Dobbe, Sarah Dean, Thomas Gilbert, Nitin Kohli
https://arxiv.org/pdf/1807.00553.pdf
A socio-technical framework for digital contact tracing
Cornell University, arXiv e-print by
Ricardo Vinuesa, Andreas Theodorou, Manuela Battaglini, Virginia Dignum
https://arxiv.org/abs/2005.08370
The AI Black Box: Failure of Intent & Causation
Harvard Journal of Law & Technology 31,2: 2018, by Yavar Bathaee
https://jolt.law.harvard.edu/assets/articlePDFs/v31/The-Artificial-Intelligence-Black-Box-and-the-Failure-of-Intent-and-Causation-Yavar-Bathaee.pdf
How Netflix uses AI to Predict Your Next Series Binge - 2020
Anoop Deoras, ML Research Scientist, Netflix
https://blog.re-work.co/how-does-netflix-use-ai-to-predict-your-next-series-binge/
Configuring the Networked Self
Julie Cohen
http://juliecohen.com/configuring-the-networked-self/
Data Aggregators, Consumer Data, and Responsibility Online: Who is Tracking Consumers Online and Should They Stop?
Prepublication
/Library/LibContentAcademic/Martin-Tracking-Users-Online-Proof.pdf
You have one identity: performing the self on Facebook and LinkedIn
Media, Culture & Society, José van Dijck
/Library/LibContentAcademic/Dijk.pdf
When data is capital: Datafication, accumulation, and extraction
https://journals.sagepub.com/doi/full/10.1177/2053951718820549
How do data come to matter? Living and becoming with personal data
https://journals.sagepub.com/doi/full/10.1177/2053951718786314
Owning Ethics: CorporateLogics, Silicon Valley, and the Institutionalization of Ethics
https://datasociety.net/wp-content/uploads/2019/09/Owning-Ethics
Northpointe (COMPAS) versus ProPublica Recidivism Debate
No nonsense version of the "racial algorithm bias, Yuxi Liu
https://www.lesswrong.com/posts/
A computer program used for bail and sentencing decisions, Washington Post/Stanford University
https://www.washingtonpost.com/
Foucault, Deleuze, and the Ethics of Digital Networks
The Quest for Intercultural Information Ethics, Bernd Frohmann
/Library/LibContentAcademic/ICIE IV Foucault Deleuze.pdf ,
The surveillant assemblage
British Journal of Sociology, 2000, Haggerty & Ericson
/Library/LibContentAcademic/The surveillant assemblage.pdf
Algorithmic paranoia and the convivial alternative
https://journals.sagepub.com/doi/full/10.1177/2053951716671340
Data cultures of mobile dating and hook-up apps: Emerging issues for social science research
Big Data & Society, 2017
/Library/LibContentAcademic/Data cultures of mobile dating.pdf ,
Romance Gets Georgia Tech Treatment With Machine Learning Algorithm
Georgia Tech, 2020
https://news.gatech.edu/2020/02/13/romance-gets-georgia-tech-treatment-machine-learning-algorithm,
Cracking the Tinder Code: An Experience Sampling Approach to the Dynamics and Impact of Platform Governing Algorithms
Journal of Computer-Mediated Communication, 2018
https://academic.oup.com/jcmc/article/23/1/1/4832995
Algorithms & Agency
Colorado Tech LJ, 2018
Collection
Interpretable Machine Learning, A Guide for Making Black Box Models Explainable
2019, Christoph Molnar
https://christophm.github.io/interpretable-ml-book/index.html
Big Data Ethics,
Wake Forest Law Review, 2014, Richards, King
https://papers.ssrn.com/sol3/papers.cfm
Database of Ruin
Harvard Business Review
https://hbr.org/2012/08/dont-build-a-database-of-ruin
Iron Cagebook
Counter Punch
http://www.counterpunch.org/2013/12/03/iron-cagebook
Big Other: Surveillance Capitalism and the Prospects of an Information Civilization
Journal of Information Technology (2015) 30, 75–89. (Surveillance Capitalism)
https://poseidon01.ssrn.com/delivery.php
A Politics of Intensity: Some Aspects of Acceleration in Simondon and Deleuze
Deleuze Studies 11.4 (2017)
/Library/LibContentAcademic/AccelerationInSimondonAndDeleuze.pdf
Control Societies and Platform Logic
New Formations, 2015, Alex Williams
/Library/LibContentAcademic/ControlandPlatform.pdf
What is Privacy?
PowerPoint Presentation
https://eportfolio.pace.edu
Spinoza, Feminism and Privacy: Exploring an Immanent Ethics of Privacy
Feminist Legal Studies, 2014
/Library/LibContentAcademic/Spinoza,_Feminism_and_Privacy.pdf
“You Social Scientists Love Mind Games”: Experimenting in the “divide” between data science and critical algorithm studies
https://journals.sagepub.com/doi/full/10.1177/2053951719833404
Social media and the social sciences: How researchers employ Big Data analytics
https://journals.sagepub.com/doi/abs/10.1177/2053951716645828
Doing data differently? Developing personal data tactics and strategies amongst young mobile media users
https://journals.sagepub.com/doi/full/10.1177/2053951718765021
Algorithms as culture: Some tactics for the ethnography of algorithmic systems
https://journals.sagepub.com/doi/full/10.1177/2053951717738104
What makes Big Data, Big Data? Exploring the ontological characteristics of 26 datasets
https://journals.sagepub.com/doi/full/10.1177/2053951716631130
Big Data Ethics
Big Data & Society, Zwitter, 2014
https://journals.sagepub.com/
Data cultures of mobile dating and hook-up apps: Emerging issues for critical social science research https://journals.sagepub.com/doi/full/10.1177/2053951717720950
Critical data studies: An introduction
https://journals.sagepub.com/doi/full/10.1177/2053951716674238
Corporate Surveillance in Everyday Life: How Companies Collect, Combine, Analyze, Trade, and Use Personal Data on Billions
A Report by Cracked Labs, Vienna, June 2017
https://crackedlabs.org/dl/CrackedLabs_Christl_CorporateSurveillance.pdf
Automatic Society
Journal of Visual Art Practice, 2016, Bernard Stiegler
/Library/LibContentAcademic/AutomaticSociety.pdf
Datacrocy
Journal of Visual Art Practice, 2016
/Library/LibContentAcademic/Datacrocy.pdf
Ars and Organological Inventions in Societies of Hyper-Control
Leonardo, 2016
/Library/LibContentAcademic/HyperControl.pdf
Modulation after Control
New Formations, 2015, Hui
/Library/LibContentAcademic/ModulationAfterControl.pdf
Organized Autonomous Networks,
Cultural Politics, 2010, Milani
/Library/LibContentAcademic/O-A-N.pdf
Towards a Rhizomatic Technical History of Control
New Formations, 2015, Goffey
/Library/LibContentAcademic/RhizomeControl.pdf
The Production of Subjectivity: From Transindividuality to the Commons
New Formations, 2010
/Library/LibContentAcademic/SubectivityTransindividualityCommons.pdf
System Error: Complicity with Surveillance in Contemporary Workplace Documentaries
Seminar, 2016
/Library/LibContentAcademic/SurveillanceWorkplace.pdf
TransIndividualizations,
2006, Stiegler, Rogoff
/Library/LibContentAcademic/TransIndi.pdf
“Plausible Cause”: Explanatory Standards in the Age of Powerful Machines,
Vanderbilt Law Review, 2017
/Library/LibContentAcademic/“Plausible_Cause”_Explanatory.pdf
Exploring Information Ethics
Journal of Information Ethics, 2016
/Library/LibContentAcademic/Exploring_Information_Ethics_.pdf
Data barns, ambient intelligence and cloud computing: the tacit epistemology and linguistic representation of Big Data
Ethics of Information Technology, 2015
/Library/LibContentAcademic/Data_barns,
University in the Epoch of Digital Reason
Analysis and Metaphysics, 2015, Peters
/Library/LibContentAcademic/THE_UNIVERSITY
Everything counts in large amounts: a critical realist case study on data-based production
Journal of Information Technology, 2014
/Library/LibContentAcademic/Everything_counts
Big Data and the End of Theory
Wired Magazine, 2006, Anderson
https://www.wired.com/2008/06/pb-theory
Big Data and the End of Theory? No.
(Could Big Data be the end of theory in science? A few remarks on the epistemology of data‐driven science, Science & Society
Fulvio Mazzocchi)
http://embor.embopress.org/content/16/10/1250
Big Data, New Epistemologies and Paradigm Shifts
Big Data & Society, 2014, Kitchin
/Library/LibContentAcademic/BigDataEpist.pdf
Small Data in the Era of Big Data,
GeoJournal, 2015, Kitchin
/Library/LibContentAcademic/Small_data
Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS
Big Data & Society, 2016, Canali
/Library/LibContentAcademic/BigDataEC.pdf
A World without Causation: Big Data and the Coming of Age of Posthumanism
Millennium: Journal of International Studies, 2015
/Library/LibContentAcademic/A World without Causation
The ethics of algorithms: Mapping the debate
Big Data & Society, 2016
/Library/LibContentAcademic/The ethics of algorithms
Beyond the Quantified Self: Thematic exploration of a dataistic paradigm
New Media and Society, 2015
/Library/LibContentAcademic/Beyond the Quantified Self
Can we trust Big Data? Applying philosophy of science to software
Big Data & Society, 2016
/Library/LibContentAcademic/Can we trust Big Data
Predictive Analytics and Incarceration, Prepublication
Strategic opportunities (and challenges) of algorithmic decision-making: A call
for action on the long-term societal effects of ‘datification,’
Journal of Strategic Information Systems, 2016
/Library/LibContentAcademic/STRATEGIC
Consumption as biopower: Governing bodies with loyalty cards
Journal of Consumer Culture, 2013
/Library/LibContentAcademic/Consumption as biopower
Antisocial media and algorithmic deviancy amplification: Analysing the id of Facebook’s technological unconscious
Theoretical Criminology, 2017
/Library/LibContentAcademic/Antisocial media
Bias in Algorithmic Filtering and Personalization
Ethics of Information Technology, 2013
/Library/LibContentAcademic/etin-final.pdf
Corporate Surveillance in Everyday Life: How Companies Collect, Combine, Analyze, Trade, and Use Personal Data on Billions
A Report by Cracked Labs, Vienna, June 2017
https://crackedlabs.org/dl/CrackedLabs_Christl_CorporateSurveillance.pdf
What's Up With Big Data Ethics?
By Jonathan H. King and Neil M. Richards
http://www.forbes.com/sites/oreillymedia/2014/03/28/whats-up-with-big-data-ethics/
What Big Data Needs: A Code of Ethical Practices
By Jeffrey F. Rayport
http://www.technologyreview.com/news/424104/what-big-data-needs-a-code-of-ethical-practices/
Big Data Is Our Generation’s Civil Rights Issue, and We Don’t Know It
By Alistair Croll
http://solveforinteresting.com/big-data-is-our-generations-civil-rights-issue-and-we-dont-know-it/
Injustice In, Injustice Out: Social Privilege in the Creation of Data
By Jeffrey Alan Johnson
http://the-other-jeff.blogspot.com/2013/03/injustice-in-injustice-out-social.html
Big Data and Its Exclusions
By Jonas Lerman
http://www.stanfordlawreview.org/online/privacy-and-big-data/big-data-and-its-exclusions
Big Data Are Made by (And Not Just a Resource for) Social Science and Policy-Making
By Solon Barocas
http://blogs.oii.ox.ac.uk/ipp-conference/2012/programme-2012/track-c-data-methods/panel-1c-what-is-big-data/solon-barocas-big-data-are-made-by-and.html
Big Data, Big Questions: Metaphors of Big Data
By Cornelius Puschmann and Jean Burgess
http://ijoc.org/index.php/ijoc/article/view/2169
View from Nowhere: On the cultural ideology of big data
By Nathan Jurgenson
http://thenewinquiry.com/essays/view-from-nowhere/
The Hidden Biases in Big Data
By Kate Crawford
https://hbr.org/2013/04/the-hidden-biases-in-big-data
The Anxieties of Big Data
By Kate Crawford
http://thenewinquiry.com/essays/the-anxieties-of-big-data/
How Big Data Is Unfair
By Moritz Hardt
https://medium.com/@mrtz/how-big-data-is-unfair-9aa544d739de
Unfair! Or Is It? Big Data and the FTC’s Unfairness Jurisdiction
By Dennis Hirsch
https://privacyassociation.org/news/a/unfair-or-is-it-big-data-and-the-ftcs-unfairness-jurisdiction/
How Big Data Can be Used to Fight Unfair Hiring Practices
By Dustin Volz
http://www.nextgov.com/big-data/2014/09/how-big-data-can-be-used-fight-unfair-hiring-practices/95195/
Big Data’s Disparate Impact
By Solon Barocas and Andrew D. Selbst
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2477899
Big Data and the Underground Railroad
By Alvaro M. Bedoya
http://www.slate.com/articles/technology/future_tense/2014/11/big_data_underground_railroad_history_says_unfettered_collection_of_data.single.html
Punished for Being Poor: The Problem with Using Big Data in the Justice System
By Jessica Pishko
http://www.psmag.com/navigation/politics-and-law/punished-poor-problem-using-big-data-justice-system-88651/
The Ethics of Big Data in Higher Education
By Jeffrey Alan Johnson
http://www.i-r-i-e.net/inhalt/021/IRIE-021-Johnson.pdf
The Chilling Implications of Democratizing Big Data
By Woodrow Harzog and Evan Selinger
http://www.forbes.com/sites/privacynotice/2013/10/16/the-chilling-implications-of-democratizing-big-data-facebook-graph-search-is-only-the-beginning/
Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights
https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/2016_0504_data_discrimination.pdf
Data and Discrimination: Collected Essays
Edited by Seeta Pena Gangadharan with Virginia Eubanks and Solon Barocas
http://www.ftc.gov/system/files/documents/public_comments/2014/10/00078-92938.pdf
Emerging Responsible Data Questions for Human Rights and Human Security
By Mark Latonero
https://responsibledata.io/emerging-responsible-data-questions-for-human-rights-and-human-security/
The Ethical Risks of Detecting Disease Outbreaks With Big Data
By Michael White
http://www.psmag.com/health-and-behavior/ethical-risks-of-detecting-disease-outbreaks-with-big-data
Framing the Big Data Ethics Debate for the Military
By Karl F. Schneider, David S. Lyle, and Francis X. Murphy
http://ndupress.ndu.edu/Media/News/NewsArticleView/tabid/7849/Article/581865/jfq-77-framing-the-big-data-ethics-debate-for-the-military.aspx
Ten Simple Rules for Responsible Big Data Research
By Matthew Zook, Solon Barocas, danah boyd, Kate Crawford, Emily Keller, Seeta Peña Gangadharan, Alyssa Goodman, Rachelle Hollander, Barbara A. Koenig, Jacob Metcalf, Arvind Narayanan, Alondra Nelson, and Frank Pasquale
http://journals.plos.org/
Data Scientist Cathy O'Neil on the Cold Destructiveness of Big Data
By Nikhil Sonnad
https://qz.com/819245/data-
The Affirmative Action of Vocabulary
By Alistair Croll
https://medium.com/@acroll/
'For These Times': Dickens on Big Data
By Irina Raicu
https://www.recode.net/2014/5/
Metaphors of Big Data
By Irina Raicu
https://www.recode.net/2015/
Immersive Theater, Big Data, Identity
CHI, 2018
Collection
Theme, 4 Articles: Algorithmic Normativities (Including: Algorithms as Folds)
Big Data and Society, 2019
Collection
Algorithmic anxiety: Masks and camouflage in artistic imaginaries of facial recognition algorithms
By Patricia de Vries and Willem Schinkel
https://journals.sagepub.com/doi/pdf/10.1177/2053951719851532
Human Rights in the Age of Platforms -
Essay collection from MIT Press
https://mitpress.mit.edu/books/human-rights-age-platforms
Good Isn’t Good Enough
by Ben Green (CS)
https://www.benzevgreen.com/wp-content/uploads/2019/11/19-ai4sg.pdf
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
by Erico Tjoa and Cuntai Guan IEEE
https://arxiv.org/ftp/arxiv/papers/1907/1907.07374.pdf
Bottom-up data Trusts: disturbing the ‘one size fits all’ approach to data governance
International Data Privacy Law, 2019, Sylvie Delacroix, Neil D Lawrence
https://academic.oup.com/idpl/advance-article/doi/10.1093/idpl/ipz014/5579842
Privacy Law's False Promise
Washington University Law Review, Vol. 97, No. 2, 2020, Ari Ezra Waldman
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3499913
A Framework for Understanding Unintended Consequences of Machine Learning
Harini Suresh MIT hsuresh@mit.edu John V. Guttag MIT guttag@mit.edu
https://arxiv.org/pdf/1901.10002.pdf
Please Stop Explaining Black Box Models for High-Stakes Decisions
Cynthia Rudin, Duke University
https://www.arxiv-vanity.com/papers/1811.10154/