CIRCA:Unconference AI Ethics Society

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(Courses of Action for Academia:)
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**Could we organize an AI-centric meeting group or space for students to discussion these issues? UAlberta’s new digital centre might be one option.
**Could we organize an AI-centric meeting group or space for students to discussion these issues? UAlberta’s new digital centre might be one option.
**But need to keep in mind the time investment/emotional labour, esp. for students
**But need to keep in mind the time investment/emotional labour, esp. for students
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Revision as of 20:23, 29 May 2019

Contents

Introduction

On May 8, 2019 the Kule Institute for Advanced Study hosted an AI Student Un/Conference Workshop . The session was organized and facilitated by University of Alberta graduate students Holly Pickering, MA/MLIS Student, Digital Humanities and Library and Information Studies, and Chelsea Miya, PhD Student and Researcher, English Language and Literature/Letters. The workshop was part of the AI, Ethics and Society Conference, which took place May 8-10, 2019. Un/Conferences are experimental gatherings where the participants set the agenda. At this full-day session, graduate students from various disciplines exchanged ideas and brainstormed concepts about issues related to AI, ethics, and society. The following is a record of our session notes.

Resources

Codes of Ethics

Centres/Organizations

AI Ethics in the News

  • Open letter calling for Google to stop selling AI tech to law enforcement [1]
  • Google Employees protest AI weapons program [2]
  • AI in the Courts [3]


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 in AI

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:

  • Interdisciplinarity:
    • Importance of breaking down and deconstructing disciplinary boundaries; e.g. through restructuring/renaming
  • How can universities better connect with the public/greater community?
    • Commit to making resources more public/accessible
    • Ensure that academics are publishing open-source; not publish in journals that have a paywall
  • Public Organizations:
    • Alberta Machine Intelligence (AMI): Alberta research group; also leads educational seminars on AI skills and literacy (https://www.amii.ca/amii-educates/)
    • StartUp Edmonton
  • Curriculum/Training:
    • Could university provide more AI-centric interdisciplinary courses? E.g. interdisciplinary AI ethics course?
    • UAlberta is also developing intro to machine-learning courses that explain how these algorithms work; what’s under the hood *Are these courses open to students from all **departments/faculties?
    • Technology and the Future of Medicine; open to all graduate students
    • The need for better ethics courses in STEM (and humanities); student consultation in developing courses
    • Seminars that centre, not just on learning, but conversation/discussion could also be helpful.
  • Student Organization
    • Could we organize an AI-centric meeting group or space for students to discussion these issues? UAlberta’s new digital centre might be one option.
    • But need to keep in mind the time investment/emotional labour, esp. for students



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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