More Like this

1. Work in Progress: STEMtelling as a Method towards Ethical Awareness in Machine Learning

   Tilsen, J; Nickel, R; Thomas, S; Appelhans, S; Cheville, RA (June 2025, American Society for Engineering Education)

   The recent surge in artificial intelligence (AI) developments has been met with an increase in attention towards incorporating ethical engagement in machine learning discourse and development. This attention is noticeable within engineering education, where comprehensive ethics curricula are typically absent in engineering programs that train future engineers to develop AI technologies [1]. Artificial intelligence technologies operate as black boxes, presenting both developers and users with a certain level of obscurity concerning their decision-making processes and a diminished potential for negotiating with its outputs [2]. The implementation of collaborative and reflective learning has the potential to engage students with facets of ethical awareness that go along with algorithmic decision making – such as bias, security, transparency and other ethical and moral dilemmas. However, there are few studies that examine how students learn AI ethics in electrical and computer engineering courses. This paper explores the integration of STEMtelling, a pedagogical storytelling method/sensibility, into an undergraduate machine learning course. STEMtelling is a novel approach that invites participants (STEMtellers) to center their own interests and experiences through writing and sharing engineering stories (STEMtells) that are connected to course objectives. Employing a case study approach grounded in activity theory, we explore how students learn ethical awareness that is intrinsic to being an engineer. During the STEMtelling process, STEMtellers blur the boundaries between social and technical knowledge to place themselves at the center of knowledge production. In this WIP, we discuss algorithmic awareness, as one of the themes identified as a practice in developing ethical awareness of AI through STEMtelling. Findings from this study will be incorporated into the development of STEMtelling and address challenges of integrating ethics and the social perception of AI and machine learning courses. 

   more » « less
2. Conceptualizing a Theory of Ethical Behavior in Engineering

   Nguyen, L. M.; Poleacovschi, C.; Faust, K. M.; Padgett Walsh, K.; Feinstein, S. G.; Rutherford, C. (January 2020, 2020 ASEE Virtual Annual Conference Content Access)

   Traditional engineering courses typically approach teaching and problem solving by focusing on the physical dimensions of those problems without consideration of dynamic social and ethical dimensions. As such, projects can fail to consider community questions and concerns, broader impacts upon society, or otherwise result in inequitable outcomes. And, despite the fact that students in engineering receive training on the Professional Code of Ethics for Engineers, to which they are expected to adhere in practice, many students are unable to recognize and analyze real-life ethical challenges as they arise. Indeed, research has found that students are typically less engaged with ethics—defined as the awareness and judgment of microethics and macroethics, sensitivity to diversity, and interest in promoting organizational ethical culture—at the end of their engineering studies than they were at the beginning. As such, many studies have focused on developing and improving the curriculum surrounding ethics through, for instance, exposing students to ethics case studies. However, such ethics courses often present a narrow and simplified view of ethics that students may struggle to integrate with their broader experience as engineers. Thus, there is a critical need to unpack the complexity of ethical behavior amongst engineering students in order to determine how to better foster ethical judgment and behavior. Promoting ethical behavior among engineering students and developing a culture of ethical behavior within institutions have become goals of many engineering programs. Towards this goal, we present an overview of the current scholarship of engineering ethics and propose a theoretical framework of ethical behavior using a review of articles related to engineering ethics from 1990-2020. These articles were selected based upon their diversity of scope and methods until saturation was reached. A thematic analysis of articles was then performed using Nvivo. The review engages in theories across disciplines including philosophy, education and psychology. Preliminary results identify two major kinds of drivers of ethical behavior, namely individual level ethical behavior drivers (awareness of microethics, awareness of macroethics, implicit understanding, and explicit understanding) and institutional drivers (diversity and institutional ethical culture). In this paper, we present an overview and discussion of two drivers of ethical behavior at the individual level, namely awareness of microethics and awareness of macroethics, based on a review of 50 articles. Our results indicate that an awareness of both microethics and macroethics is essential in promoting ethical behavior amongst students. The review also points to a need to focus on increasing students’ awareness of macroethics. This research thus addresses the need, driven by existing scholarship, to identify a conceptual framework for explaining how ethical judgment and behavior in engineering can be further promoted. 

   more » « less
3. Implications of Identity in AI: Creators, Creations, and Consequences

   https://doi.org/10.1609/aaaiss.v3i1.31268  

   Tadimalla, Sri Yash; Maher, Mary Lou (May 2024, Proceedings of the AAAI Symposium Series)

   The field of Artificial Intelligence (AI) is rapidly advancing, with significant potential to transform society. However, it faces a notable challenge: lack of diversity, a longstanding issue in STEM fields. In this context, this position paper examines the intersection of AI and identity as a pathway to understanding biases, inequalities, and ethical considerations in AI development and deployment. We present a multifaceted definition of AI identity, which encompasses its creators, applications, and their broader impacts. Understanding AI's identity involves analyzing the diverse individuals involved in AI's development, the technologies produced, and the social, ethical, and psychological implications. After exploring the AI identity ecosystem and its societal dynamics, We propose a framework that highlights the need for diversity in AI across three dimensions: Creators, Creations, and Consequences through the lens of identity. This paper presents a research framework for examining the implications and changes needed to foster a more inclusive and responsible AI ecosystem through the lens of identity. 

   more » « less
4. Left on their Own: Confronting Absences of AI Ethics Training among Engineering Master’s Students

   Goldenkoff, Elana; Cech, Erin A (June 2024, American Society for Engineering Education)

   Although development of Artificial Intelligence (AI) technologies has been underway for decades, the acceleration of AI capabilities and rapid expansion of user access in the past few years has elicited public excitement as well as alarm. Leaders in government and academia, as well as members of the public, are recognizing the critical need for the ethical production and management of AI. As a result, society is placing immense trust in engineering undergraduate and graduate programs to train future developers of AI in their ethical and public welfare responsibilities. In this paper, we investigate whether engineering master’s students believe they receive the training they need from their educational curricula to negotiate this complex ethical landscape. The goal of the broader project is to understand how engineering students become public welfare “watchdogs”; i.e., how they learn to recognize and respond to their public welfare responsibilities. As part of this project, we conducted in-depth interviews with 62 electrical and computer engineering master’s students at a large public university about their educational experiences and understanding of engineers’ professional responsibilities, including those related specifically to AI technologies. This paper asks, (1) do engineering master’s students see potential dangers of AI related to how the technologies are developed, used, or possibly misused? (2) Do they feel equipped to handle the challenges of these technologies and respond ethically when faced with difficult situations? (3) Do they hold their engineering educators accountable for training them in ethical concerns around AI? We find that although some engineering master’s students see exciting possibilities of AI, most are deeply concerned about the ethical and public welfare issues that accompany its advancement and deployment. While some students feel equipped to handle these challenges, the majority feel unprepared to manage these complex situations in their professional work. Additionally, students reported that the ethical development and application of technologies like AI is often not included in curricula or are viewed as “soft skills” that are not as important as “technical” knowledge. Although some students we interviewed shared the sense of apathy toward these topics that they see from their engineering program, most were eager to receive more training in AI ethics. These results underscore the pressing need for engineering education programs, including graduate programs, to integrate comprehensive ethics, public responsibility, and whistleblower training within their curricula to ensure that the engineers of tomorrow are well-equipped to address the novel ethical dilemmas of AI that are likely to arise in the coming years. 

   more » « less
5. Ethical Considerations in Artificial Intelligence Courses

   Burton, Emanuelle; Goldsmith, Judy; Koenig, Sven; Kuipers, Benjami; Mattei, Nicholas; Walsh, Toby (January 2017, AI magazine)

   The recent surge in interest in ethics in artificial intelligence may leave many educators wondering how to address moral, ethical, and philosophical issues in their AI courses. As instructors we want to develop curriculum that not only prepares students to be artificial intelligence practitioners, but also to understand the moral, ethical, and philosophical impacts that artificial intelligence will have on society. In this article we provide practical case studies and links to resources for use by AI educators. We also provide concrete suggestions on how to integrate AI ethics into a general artificial intelligence course and how to teach a stand-alone artificial intelligence ethics course. 

   more » « less