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Data ethics

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Guest lecture for the Data and Analytics summer school of the Accounting and Finance Master in NUI Galway

Publicado en: Datos y análisis
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Data ethics

  1. 1. Data Ethics Mathieu d’Aquin - @mdaquin Data Science Institute National University of Ireland Galway Insight Centre for Data Analytics
  2. 2. Data Ethics
  3. 3. Data Ethics The set of principles and processes that guide the ethical collection, processing, analysis, use and application of data having an effect on human lives and society d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  4. 4. Data Ethics The set of principles and processes that guide the ethical collection, processing, analysis, use and application of data having an effect on human lives and society d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  5. 5. Data Ethics The set of principles and processes that guide the ethical collection, processing, analysis, use and application of data having an effect on human lives and society d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  6. 6. Data Ethics The set of principles and processes that guide the ethical collection, processing, analysis, use and application of data having an effect on human lives and society d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  7. 7. Ethics What is right, what is fair, what is just. Hosmer, L. T. (1995). "Trust: The Connecting Link between Organizational Theory and Philosophical Ethics". The Academy of Management Review. 20 (2)
  8. 8. In an ideal world What is ethical. (right, fair, just) What is legal.
  9. 9. In the real world What is ethical. (right, fair, just) What is legal.
  10. 10. What does this have to do with data?
  11. 11. What is ethical. (right, fair, just) What is legal. What does this have to do with data? Data protection Privacy Statistical bias Black box decisions Uneven access self-governance ...
  12. 12. Machine ethics (https://www.smbc-comics.com/comic/machine-ethics)
  13. 13. Example related to privacy/data protection In 2014, New York City released data about 173m taxi trips in the city, where the licence plates and identifier of the taxi had been obfuscated for anonymisation purposes. It was de-anonymised within hours of being released… … and later cross-referenced with timestamped pictures of celebrities entering taxis in New York to figure out their personal address, and how much they tipped. See e.g. http://gawker.com/the-public-nyc-taxicab-database-that-accidentally-track-1646724546
  14. 14. Example related to privacy/data protection In this case, it is useful to note that: - Replacing identifiers with a hash is not anonymisation, it is at best bad pseudonymisation - Current data protection regulation in Europe regulates against this sort of cases - The upcoming GDPR will make the consequences of this sort of mistakes stronger - It defines its scope as “any information relating to an identified or identifiable natural person ('data subject'); an identifiable natural person is one who can be identified, directly or indirectly”. Arguably, the unanticipated case of the celebrities fall under this scope… and should therefore have been anticipated.
  15. 15. But, should also be asking: What is my impact on society? How can I minimise the risk of negative implications? (drawing upon critical social science, and regulation as guidelines) How do I make what I’m doing compliant with regulation? In addition to:
  16. 16. Examples related to bias Google search “unprofessional hair for work” and “professional hair for work”
  17. 17. Example related to black-box decision The US justice system relies on a tool to predict, when judging for an offence, what is the likeliness an individual has to re-offend. It is based on many variables, including address, type of offence, past history of offences, and ethnicity. It has been demonstrated to make significant mistakes, especially through being prone to give overly negative scores to black people. See https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  18. 18. Notes on those cases - The algorithm is not biased, the data is. Garbage in, garbage out. - Human decisions are not gold standards, and therefore should not be treated as such in training machine learning models - Sometimes, unrelated things just happen to correlate (see http://www.tylervigen.com/spurious-correlations) - a machine learning model will rely on those correlations to make decisions.
  19. 19. Should we ban cheese?
  20. 20. Example related to uneven access and under-represented cases Researchers at Georgia Institute of Technology developed and used a chatbot to act as a TA for computer science courses (without the students’ knowledge). It worked very well in most cases… … but failed dramatically in uncommon, delicate situation. Bobbie Eicher et al., Jill Watson Doesn’t Care if You’re Pregnant: Grounding AI Ethics in Empirical Studies, AIES 2018
  21. 21. Example related to uneven access and under-represented cases Notes on this case: - Another form of bias, not related to spurious or inaccurate correlations, but to under-representation of specific parts of the population. - Raise issues with the uneven access to the benefit of the technology, and therefore unfairness. - “The future is already here — it's just not very evenly distributed” -- William Gibson Bobbie Eicher et al., Jill Watson Doesn’t Care if You’re Pregnant: Grounding AI Ethics in Empirical Studies, AIES 2018
  22. 22. Principles for designing ethics data science projects ‘Ethics in Design’ for Data Science Dialectic The process is based on a conversational approach between data and critical social scientists throughout the project’s life-cycle. Reflective Ethical concerns are not pre-fixed; they may emanate from any stage of the project; thus, constant reflexivity on activities and researchers is needed. Creative, not disruptive The objective of this process is to achieve a positive impact on the research, increase its value addressing ethics throughout the project’s life-cycle. All- encompassing Ethical concerns appear as much in the research activities as in their outcomes, their use and exploitation; the process needs to expand on all stages. d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  23. 23. Principles for designing ethics data science projects ‘Ethics in Design’ for Data Science Dialectic The process is based on a conversational approach between data and critical social scientists throughout the project’s life-cycle. Reflective Ethical concerns are not pre-fixed; they may emanate from any stage of the project; thus, constant reflexivity on activities and researchers is needed. Creative, not disruptive The objective of this process is to achieve a positive impact on the research, increase its value addressing ethics throughout the project’s life-cycle. All- encompassing Ethical concerns appear as much in the research activities as in their outcomes, their use and exploitation; the process needs to expand on all stages. d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018 Methodology borrowed from design fiction: the use of speculative and often provocative scenarios involving the artifact to be design (a data process), as a way to explore its possible implications and reflect on their consequences. Pragmatically, it consist in telling stories asking and answering what if questions (e.g. “what if the student is pregnant? What would happen then?”) and building mockups of the final product to reflect on its behaviour. See Anthony Dunne and Fiona Raby, Speculative Everything, MIT Press, 2013 and Joseph Lindley and Paul Coulton, "Back to the Future: 10 Years of Design Fiction". British HCI 2015.
  24. 24. Principles for designing ethics data science projects ‘Ethics in Design’ for Data Science Dialectic The process is based on a conversational approach between data and critical social scientists throughout the project’s life-cycle. Reflective Ethical concerns are not pre-fixed; they may emanate from any stage of the project; thus, constant reflexivity on activities and researchers is needed. Creative, not disruptive The objective of this process is to achieve a positive impact on the research, increase its value addressing ethics throughout the project’s life-cycle. All- encompassing Ethical concerns appear as much in the research activities as in their outcomes, their use and exploitation; the process needs to expand on all stages. d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018
  25. 25. Principles for designing ethics data science projects ‘Ethics in Design’ for Data Science Dialectic The process is based on a conversational approach between data and critical social scientists throughout the project’s life-cycle. Reflective Ethical concerns are not pre-fixed; they may emanate from any stage of the project; thus, constant reflexivity on activities and researchers is needed. Creative, not disruptive The objective of this process is to achieve a positive impact on the research, increase its value addressing ethics throughout the project’s life-cycle. All- encompassing Ethical concerns appear as much in the research activities as in their outcomes, their use and exploitation; the process needs to expand on all stages. d’Aquin et al, Towards an “Ethics in Design” methodology for AI research projects, in AIES 2018 i.e. don’t do that:
  26. 26. Some conclusions Following regulation is insufficient for data ethics. Ethical issues often appear after the development phase, in scenarios that have not been anticipated. Need to uncover those scenarios to integrate in the process ways of mitigating ethical implications, and balance social, economic and ethical values. This cannot be done (currently) by the technologists alone!
  27. 27. Shameless self-promotion Check Towards an “Ethics by Design” methodology for AI research projects at the first conference on AI, Ethics and Society, AIES 2018 The Re-Coding Black Mirror worksop at The Web Conference (WWW 2018) - https://kmitd.github.io/recoding-black-mirror/ MagnaCartaForData.org Contacts: mathieu.daquin@insight-centre.ie, mdaquin.net, @mdaquin

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