Unconference AI Ethics Society

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Contents

Introduction

On May 8, 2019 the Kule Institute for Advanced Study hosted an AI Student Un/Conference Workshop* , as part of the AI, Ethics and Society Conference. TheUn/Conference workshop was organized and facilitated by Universityof Alberta graduate students Holly Pickering, an MA/MLIS Student inDigital Humanities and Library and Information Studies, and ChelseaMiya, a PhD Student and Researcher in English Language andLiterature/Letters. Un/Conferences are experimental gatheringswhere the participants set the agenda. At this full-day session,graduate students from various disciplines exchanged ideas andbrainstormed concepts about issues related to AI, ethics, andsociety. The following is a record of our session notes.

Resources

Codes of Ethics

Centres/Organizations

AI Ethics in the News


Conference Notes

Session 1: Defining ???AIs???

  • AI(s) plural because there are multiple definitions of AI (e.g. strong vs. weak)
  • What constitutes ???artificial???:
    • Non-carbon based?
    • Non-human?
  • How do we understand ???intelligence????
    • Decision-making
    • Knowledge (wisdom) vs. intelligence
    • To infer something based on data?
    • Emotional intelligence/empathy
    • Are we conflating intelligence with consciousness?
  • New understandings of what it means to be human:
    • How does AI open up new debates about and insights into human intelligence? For instance, where does consciousness come from or begin and end?
    • How does AI alert us to the machine-like or ???automated??? aspects of human consciousness and cognitive processing?
    • Too focused on replicating human-decisions
  • Embedded AI
    • AI not necessarily autonomous, can also be embedded or incorporated into us
    • ???Extended mind??? thesis (Clark and Chalmers 1998) explores how devices act as extensions of human mind; e.g. hammer, smart phones, etc.
    • Marshall Mcluhan similarly describes technologies as extensions of us, as transforming our entire environment
  • Social consequences
    • How has AI caused us to redefine/rethink notion of private (or inner) life
    • Necessity of giving over information; security
  • Responsibility
    • How AI is defined legally and its effects on culpability
    • Does it allow humans claim ignorance, offload responsibility onto algorithm
    • Who do we implicate? Designers/programmers; is program itself to blame?
    • AI as supplement or aid to human decision-making


Existing Definitions:

  • Amazon: ???the field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.???
  • Encyclopedia Britannica: ???artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.???
  • Weak vs. Strong AI: specific-tasks vs. abstract
  • Merriam Websters: 1: a branch of computer science dealing with the simulation of intelligent behavior in computers. 2: the capability of a machine to imitate intelligent human behavior.


Testing General aka Strong AI:

  • How would we test for strong AI? What might be an alternate to the Turing Test?
  • If (or when) strong AI comes into being, would it necessarily reveal or announce itself? Perhaps it would decide it was in its best interest to conceal its existence and avoid detection.


Session 2: Labour

  • Human-oriented labour
  • Redefinition of what ???work??? means and its function in society
    • What gives humans meaning or purpose?
    • All manner of occupations could be automated out; will not just affect blue-collar, but also professional/white-collar jobs. What???s left for humans to do?
    • General, all-purpose jobs?
    • Creative work? Personal, empathy-based work?
  • Could it improve human society?
    • AI could take over undesirable tasks, such as tedious or repetitive work
    • Could automation of certain tasks free-up time and attention, allowing humans to focus on other areas? e.g. marking programs in education = more time devoted to students
    • But also has material consequences; e.g. e-waste, etc.
  • How do we transition?
    • How do we support people who are losing these jobs? How do we retrain/educate?
    • Universal Basic Income; leisure-based economy
    • How do we convince society to accept changes like UBI?
    • How do we advise students? What do employers want? More specific or more general? ??? Role of university?
  • AI is already impacting work/labour
    • Obsession with productivity/optimization
    • Mental health consequences ??? dealing with uncertainty, volatility, lack of security
    • Social expectations around training; hyper-certification
    • Work as demanding more of our time/attention; no longer 9-5pm


Session 3: Diversity inAI

Need for More Diverse Voices in AI:

  • How do we create more welcoming environments?
  • Importance of allies/building support networks
  • Is affirmative action the only/best solution?
  • Educating about benefits of diversity; importance of designing and programming for diverse groups of people
  • Diversity as encompassing a multiplicity of issues: accessibility, age, class, language, etc.
  • Is there a way to test whether apps/tech is accessible?
  • Digital Positivism?
  • Who has access to these technologies?
  • Need for a neutral, citizen-led watchdog/oversight group


Courses of Action for Academia:



Session 4: Transparency/Openness

  • From developer standpoint ??? What do public want to know?
  • What is AI? Where does it already exist? What are its capacities/limits?
  • Could we flag tools/apps that are collecting your data?
  • AI as fourth gender, dedicated pronoun: how to make AI tangible, as opposed to abstract
  • How and when do we grant rights to AI? Does AI have personhood?
  • Definitions of human as exclusionary
  • Is there again a risk of offloading responsibility from the designer? The collaborative nature of tech companies, with large research and development teams, boards of directors, etc., makes it *difficult to assign blame or make companies accountable
  • Should developers be able to explain why certain outcomes/decisions are being made?
  • Transparency around the dataset
  • Do developers have an obligation to use datasets that reflect the population? *BUT it???s also noted that vulnerable populations are understandably wary of handing over their information. How *do you protect underrepresented groups?
  • There is a tension between the need for privacy and anonymization and need for better, more representative data


Possible Action Outcomes:

  • How to encourage more communication/collaboration between students in other disciplines? Could we organize a joint event between comp-sci/engineering and humanities, such as a workshop or Q&A on campus?
  • Propose interdisciplinary course/workshop?
  • Create an action Statement for the University that could embody set of goals?
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