Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for “Good”. This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. But what is the Common Good, and is it enough to want to be good? Via four lead questions, the concept of Ethics Pen-Testing identifies challenges (...) and pitfalls when determining, from an AI point of view, what the Common Good is and how it can be enhanced by AI. The current paper reports on a first evaluation of EPT. EPT is applicable to various artefacts that have ethical impact, including designs for or implementations of specific AI technology, and requirements engineering methods for eliciting which ethical settings to build into AI. The current study focused on the latter type of artefact. In four independent sessions, participants with close but varying involvements in “AI and ethics” were asked to deconstruct a method that has been proposed for eliciting ethical values and choices in autonomous car technology, an online experiment modelled on the Trolley Problem. The results suggest that EPT is well-suited to this task: the remarks made by participants lent themselves well to being structured by the four lead questions of EPT, in particular, regarding the question what the problem is and about which stakeholders define it. As part of the problem definition, the need became apparent for thorough technical domain knowledge in discussions of AI and ethics. Thus, participants questioned the framing and the presuppositions inherent in the experiment and the discourse on autonomous cars that underlies the experiment. They transitioned from discussing a specific AI artefact to discussing its role in wider socio-technical systems. Results also illustrate to what extent and how the requirements engineering method forces us to not only have a discussion about which values to “build into” AI systems, the substantive building blocks of the Common Good, but also about how we want to have this discussion at all. Thus, it forces us to become explicit about how we conceive of democracy and the constitutional state and the procedural building blocks of the Common Good. (shrink)

The following paper presents both a historical and personal account of the societal and ethical issues arising in the development of artificial intelligence, tracking, where I was involved, the issues from the nineteen seventies onward. My own involvement in the AI narrative begins with the early discussions around whether machines can think. These first discussions, in time, evolved secondly, with the rise of the internet in the nineties, into perceptions of AI as distributed intelligence, addressing its impact on social structures (...) including basic ethical issues arising in daily life. Thirdly, in the sweeping application of AI to all kinds of societal goals and contexts, the awareness that all natural and artificial things might be digitally connected with each other and to human agents led my further involvement in the AI narrative. Tracing this evolution from start to finish, I conclude my own narrative in the history of AI by presenting some of the future challenges for the development and use of artificial intelligences. Through the application of recent research in academia, scientific associations and political bodies, I address the possibilities for the good life, both with and without artificial intelligences. (shrink)

For years, privacy regulators have said that privacy is good for business. Strong privacy management programs and accountability mechanisms build trust with consumers. In the public sector, privacy regulators have seen massive information sharing projects fail when public input or consultation, or independent oversight is not considered. After a sequence of events in 2018, society as a whole began asking questions about what is being done with personal information and questioned whether it is in our best interests. This presentation made (...) at the University of Alberta’s Kule Institute’s event on “AI, Ethics and Society” in May 2019 provides an overview of the shifts that have taken place and how privacy regulators internationally have incorporated discussions about ethical assessments, in addition to traditional privacy impact assessments, as a way to guide current and future tech developments involving personal information in a way that is legal, fair and just. (shrink)

The following essay explores the intersection of care with ethical reflections on artificial intelligence. The current debate around AI ethics focuses on questions of moral AI judgment and the general criteria for maximizing the fairness, accountability, and transparency of these judgments. While this discussion is important, it all too often obfuscates the actual purpose and intention behind the use of the algorithmic or AI technology. Where the rationale for developing these technologies focuses on increasing optimization and innovation, concern must be (...) shifted to ensure that AI will be used primarily to address current inequities and harms, from exacerbating climate change to manipulating voters via social media to creating “better” weapons. (shrink)

The sharing economy and peer-to-peer business relationships using information technology has become moreimportant in today’s world. For the sharing economy to work, however, trust and reputation are cruciallyimportant. I argue that the gathering of personal data needs to be accompanied by safeguards providing aguarantee of privacy rights. This argument will be based on a sketch of a theory called ‘peer-to-peer ethics.’Basically, the idea is that what constitutes the ground for normativity is something that is agreed upon byeveryone involved. In short, (...) what is considered to be ‘good’ is whatever contributes to bringing about thedesired goal of the community. This is a very familiar and ancient view on normative concepts, but, as I argue,one that deserves to be taken seriously especially as we enter into an intricately globalized world of ethicswhere worldviews clash with one another. (shrink)

Artificial Intelligence is playing an increasingly prevalent role in our lives. Whether its landing a job interview, getting a bank loan or accessing a government program, organizations are using automated systems informed by AI enabled technologies in ways that have significant consequences for people. At the same time, there is a lack of transparency around how AI technologies work and whether they are ethical, fair or accurate. This paper examines a body of literature related to the ethical considerations surrounding the (...) use of artificial intelligence and the role of ethical codes. It identifies and explores core issues including bias, fairness and transparency and looks at who is setting the agenda for AI ethics in Canada and globally. Lastly, it offers some suggestions for next steps towards a more inclusive discussion. (shrink)

This article explores how a focus on ‘student centeredness’ can lead to ‘innovation’ and how innovation can enhance student centeredness. Putting students at the centre of all considerations can unleash their creative and innovative potential. And recent innovations have made it easier to make students the focal point of service delivery. After a description of what we understand under these two guiding concepts, a case study is presented in which an AI-powered Student Virtual Assistant was developed at York University in (...) Toronto, Canada. All steps of the product creation, including envisioning, designing, prototyping, and evaluating are described, as well as the following steps involving maintenance and expansion. (shrink)

Many economists have argued convincingly that automated systems employing present-day artificial intelligence have already caused massive technological displacement, which has led to stagnant real wages, fewer middle- income jobs, and increased economic inequality in developed countries like Canada and the United States. To address this problem various individuals have proposed measures to increase workers’ living standards, including the adoption of a universal basic income, increased public investment in education, increased minimum wages, increased worker control of firms, and investment in a (...) Green New Deal that will provide substantial employment in transitioning to green energy, buildings, and agriculture. In this paper I argue that both left-wing and right-wing positions in political philosophy, such as John Rawls’s Justice as Fairness and Robert Nozick’s Entitlement Theory, are committed to the conclusion that we should take political action to counteract the effects of technological displacement by undertaking such measures to increase workers’ living standards. (shrink)

Education at all levels is increasingly augmented and enhanced by data mining and analytics, catalysed by the growing prevalence of automated distance learning. With an unprecedented capacity to scale both horizontally and vertically, data mining and analytics are set to be a transformative part of the future of education. We reflect on the assumptions behind data mining and the potential consequences of learning analytics, with reference to an issue brief prepared for the U.S. Department of Education entitled Enhancing Teaching and (...) Learning Through Educational Data Mining and Learning Analytics. We argue that the associated gains conceal subtle, but important risks. Data-ism, an underpinning paradigm, assigns unjustified veracity to data-driven science and the application of personalised analytics may compromise individual privacy, agency and inventiveness. This holds serious ethical implications, particularly when considering the impact on minors, rendering wholesale adoption premature. (shrink)

The introduction of Artificial Intelligence systems has demonstrated impeccable potential and benefits to enhance the decision-making processes in our society. However, despite the successful performance of AI systems to date, skepticism and concern remain regarding whether AI systems could form a trusting relationship with human users. Developing trusted AI systems requires careful consideration and evaluation of its reproducibility, interpretability, and fairness, which in in turn, poses increased expectations and responsibilities for data scientists. Therefore, the current study focused on understanding Canadian (...) data scientists’ self-confidence in creating trusted AI systems, while relying on their current AI system development practices. (shrink)