Unconference AI Ethics Society

Introduction

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

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?