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Computing Consequences: A Framework for Teaching Ethical Computing

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How to prepare tomorrow's professionals for questions that can't always be answered with faster, better, or more technology.
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... In recognition of the need for ethical awareness and subsequent ethical competence in software development, there has been a notable shift in computing curriculum design to recognize social, political and environmental implications of technology [58]. These changes are captured in the evolution of computing curriculum that has seen the inclusion of social, ethical and professional issues [59], observable in the pioneering CS1991 and subsequent curriculum guidelines for undergraduate degree programmes including CS2014, SE2004, SE2014 and IT2107. These curriculum guideline volumes (see Table 1) provide guidelines for the inclusion of the relevant learning outcomes on software engineering ethics by higher learning institutions. ...
... It has further translated into the inclusion of ethics as part of the professional practice knowledge area in the Software Engineering Body of Knowledge (SWEBOK) [63] which also guides the curriculum guideline volumes. In support, earlier researches, such as [48], [59], [64], [65] demonstrate various advances in promoting professional practice and ethics, including the accreditation of qualifications. ...
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Computing graduates working as software engineers are expected to demonstrate competencies in various categories of software engineering ethics as a component of non-technical skills that complement technical skills. Therefore, university programme offerings should provide opportunities for students to develop software engineering ethical competence. This study analyses curriculum documents to determine the extent to which entry-level undergraduate computing qualifications of Universities of Technology (UoTs) in South Africa provide opportunities to empower students with software engineering ethical competence. We used summative content analysis to analyze texts within the UoT computing undergraduate qualifications related to software development as retrieved from the South African Qualifications Authority database. ATLAS.ti text analysis tool was used to classify texts according to predetermined software engineering ethics categories to determine the extent to which the qualifications under study expose students to software engineering ethics. The results show that the coverage of the various categories of software engineering ethics by UoT computing qualifications for software development is insufficient, incomplete and superficial, providing only limited opportunities for prospective software engineers to develop software engineering ethical competence. Lack of adequate inclusion of software engineering ethics by UoT qualifications in South Africa deprives prospective software engineers an opportunity to develop ethical competence required to become ethically successful software engineers. Such limited exposure by software development graduates risks the development of potentially unethical software products in the software industry.
... At the professional level, there is established cross-disciplinary research on ethical reasoning (Boyd, 2010;Knapp, 2016;Leonelli, 2016), with specific recommendations for pedagogy and training that emphasize case studies, field practice, and sensemaking frameworks (Mumford et al., 2008;Sternberg, 2010). In their discussion on teaching ethical computing, Huff and Martin (Huff & Martin, 1995) summarize a framework for introducing ethical analysis that incorporates levels of social analysis (individual through global), responsibility, privacy, reliability, equity, and other related topics. In terms of pedagogy, they make an important recommendation of "incorporation of ethical and social issues in the lab work associated with such standard computer science subjects as database design, human-computer interaction, operating systems, and algorithms" (Huff & Martin, 1995). ...
... In their discussion on teaching ethical computing, Huff and Martin (Huff & Martin, 1995) summarize a framework for introducing ethical analysis that incorporates levels of social analysis (individual through global), responsibility, privacy, reliability, equity, and other related topics. In terms of pedagogy, they make an important recommendation of "incorporation of ethical and social issues in the lab work associated with such standard computer science subjects as database design, human-computer interaction, operating systems, and algorithms" (Huff & Martin, 1995). Tractenberg and colleagues (Tractenberg et al., 2015) offer guidelines on introducing ethical reasoning into data science and AI training, and detail two syllabi that have students reflecting on ethical misconduct, societal impacts, privacy and confidentiality considerations, and responsible research practices, though evaluation hinges on written assignments and class discussion only. ...
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Although an increasing number of ethical data science and AI courses is available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on algorithmic development or data analysis. In this paper we recount a recent experience in developing and teaching a technical course focused on responsible data science, which tackles the issues of ethics in AI, legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Interpretability of machine-assisted decision-making is an important component of responsible data science that gives a good lens through which to see other responsible data science topics, including privacy and fairness. We provide emerging pedagogical best practices for teaching technical data science and AI courses that focus on interpretability, and tie responsible data science to current learning science and learning analytics research. We focus on a novel methodological notion of the object-to-interpret-with, a representation that helps students target metacognition involving interpretation and representation. In the context of interpreting machine learning models, we highlight the suitability of “nutritional labels”—a family of interpretability tools that are gaining popularity in responsible data science research and practice.
... The importance of well-integrating ethical aspects into computing programmes and modules/courses, as highlighted by Grosz et al. (2019) is well-established; and we are inspired by the research of Chuck Huff and C Dianne Martin (1995) which places emphasis on empathy, and students imagining the consequences of their own work and actions. Furthermore, we wish to encourage a more multi-disciplinary approach to teaching digital ethics as discussed in A.H. McGowan (2012). ...
... Both discourses underline the importance of knowledge, learning and education as conditions of successfully navigating ethical questions [20]. Both ask the question which help can be provided to people working in the design and development of technology and aim to develop suitable methodologies [68]. This is the basis of various "by design" approaches [33,64,86] that are based on the principles of value-sensitive design [58], [85]. ...
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Ethical, social and human rights aspects of computing technologies have been discussed since the inception of these technologies. In the 1980s, this led to the development of a discourse often referred to as computer ethics. More recently, since the middle of the 2010s, a highly visible discourse on the ethics of artificial intelligence (AI) has developed. This paper discusses the relationship between these two discourses and compares their scopes, the topics and issues they cover, their theoretical basis and reference disciplines, the solutions and mitigations options they propose and their societal impact. The paper argues that an understanding of the similarities and differences of the discourses can benefit the respective discourses individually. More importantly, by reviewing them, one can draw conclusions about relevant features of the next discourse, the one we can reasonably expect to follow after the ethics of AI. The paper suggests that instead of focusing on a technical artefact such as computers or AI, one should focus on the fact that ethical and related issues arise in the context of socio-technical systems. Drawing on the metaphor of ecosystems which is widely applied to digital technologies, it suggests preparing for a discussion of the ethics of digital ecosystems. Such a discussion can build on and benefit from a more detailed understanding of its predecessors in computer ethics and the ethics of AI.
... 2) what are the use cases in their project that can impact users negatively, 3) how users can be provided with options for better decision making, 4) what design changes are needed so users can take informed decision, and 5) what sort of design decisions and implementation techniques are required so users' data cannot be used for profiling. Students were provided with resources [17,18] before the discussion, which was used by students, so they can conduct a meaningful discussion based on the frameworks identified for ethical computing and computing for social good. ...
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The Computer Science for All movement is bringing CS to K-12 classrooms across the nation. At the same time, new technologies created by computer scientists have been reproducing existing inequities that directly impact today's youth, while being “promoted and perceived as more objective or progressive than the discriminatory systems of a previous era” [1, p. 5–6]. Current efforts are being made to expose students to the social impact and ethics of computing at both the K-12 and university-level—which we refer to as “socially responsible computing” (SRC) in this paper. Yet there is a lack of research describing what such SRC teaching and learning actively involve and look like, particularly in K-12 classrooms. This paper fills this gap with findings from a research-practice partnership, through a qualitative study in an Advanced Placement Computer Science Principles classroom enrolling low-income Latino/a/x students from a large urban community. The findings illustrate 1) details of teaching practice and student learning during discussions about SRC; 2) the impact these SRC experiences have on student engagement with CS; 3) a teacher's reflections on key considerations for effective SRC pedagogy; and 4) why students’ perspectives and agency must be centered through SRC in computing education.