skip to main content
10.1145/3408877.3432360acmconferencesArticle/Chapter ViewAbstractPublication PagessigcseConference Proceedingsconference-collections
research-article

How Students in Computing-Related Majors Distinguish Social Implications of Technology

Published: 05 March 2021 Publication History

Abstract

The demand for machine learning and data science has grown exponentially in recent years. Yet, as the influence of these fields reach farther into daily life, the disparate impacts of these algorithms and models on more marginalized populations have also begun to surface rapidly. To address this emerging crisis, it is necessary to equip the next generation of computer scientists with the ethical tools needed to tackle these issues. Thus, an exploratory study was conducted to investigate how students who are currently enrolled in computing-related programs evaluate and understand the ethical and social impact of technology. 43 students in computing majors were presented with 5 scenarios of different technologies that utilizes machine learning to address potentially sensitive areas (e.g. policing, medical diagnosing). The long-format responses to these scenarios were qualitatively analyzed. Additionally, quantitative analysis was conducted after qualitatively coding the long-format responses into four sentiments. Ultimately, we found that participants were able to decipher the social implications of technology. However, many issues of systemic discrimination were missing from participants' analysis. Alarmingly, our findings also indicated that 50% or more of participants were not exposed to most of the technologies highlighted in the scenarios, which highlights a potential gap in computing curriculum of connecting ethics as well as racial, cultural, and socioeconomic understanding to computer science. Based on these results, we suggest that computing-related curriculum be reevaluated with ethical training in mind.

References

[1]
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine bias. ProPublica, May, Vol. 23 (2016), 2016.
[2]
Ilse Baumgartner and Venky Shankararaman. 2015. Case studies in computing education: Presentation, evaluation and assessment of four case study-based course design and delivery models. In Proceedings - Frontiers in Education Conference, FIE, Vol. 2015-February. Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/FIE.2014.7044194
[3]
BBC. 2019. Apple's 'sexist' credit card investigated by US regulator . https://www.bbc.com/news/business-50365609
[4]
Reuben Binns, Michael Veale, Max Van Kleek, and Nigel Shadbolt. 2017. Like trainer, like bot? Inheritance of bias in algorithmic content moderation. In International conference on social informatics. Springer, 405--415.
[5]
Ali Breland. 2017. How white engineers built racist code -- and why it's dangerous for black people . https://www.theguardian.com/technology/2017/dec/04/racist-facial-recognition-white-coders-black-people-police
[6]
Louis Columbus. 2017. IBM Predicts Demand For Data Scientists Will Soar 28% By 2020 . Forbes (may 2017). https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data-scientists-will-soar-28-by-2020/#49ad522f7e3b
[7]
Ben Dattner, Tomas Chamorro-Premuzic, Richard Buchband, and Lucinda Schettler. 2019. The legal and ethical implications of using AI in hiring. Harvard Business Review, Vol. 25 (2019).
[8]
Ira Diethelm, Peter Hubwieser, and Robert Klaus. 2012. Students, Teachers and Phenomena: Educational Reconstruction for Computer Science Education. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research (Koli, Finland) (Koli Calling '12). Association for Computing Machinery, New York, NY, USA, 164--173. https://doi.org/10.1145/2401796.2401823
[9]
Casey Fiesler, Natalie Garrett, and Nathan Beard. 2020. What Do We Teach When We Teach Tech Ethics?. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education. ACM, New York, NY, USA, 289--295. https://doi.org/10.1145/3328778.3366825
[10]
Ahmad Ghafarian. 2002. Integrating ethical issues into the undergraduate computer science curriculum. Journal of Computing Sciences in Colleges, Vol. 18, 2 (2002), 180--188.
[11]
Don Gotterbarn and Keith W Miller. 2004. Computer ethics in the undergraduate curriculum: Case studies and the joint software engineer's code. Journal of Computing Sciences in Colleges, Vol. 20, 2 (2004), 156--167.
[12]
Barbara J. Grosz, David Gray Grant, Kate Vredenburgh, Jeff Behrends, Lily Hu, Alison Simmons, and Jim Waldo. 2019. Embedded EthiCS . Commun. ACM, Vol. 62, 8 (jul 2019), 54--61. https://doi.org/10.1145/3330794
[13]
Sean Hollister. 2019. Google contractors reportedly targeted homeless people for Pixel 4 facial recognition . The Verge (oct 2019). https://www.theverge.com/2019/10/2/20896181/google-contractor-reportedly-targeted-homeless-people-for-pixel-4-facial-recognition
[14]
Peter Hubwieser, Michal Armoni, Torsten Brinda, Valentina Dagiene, Ira Diethelm, Michail N. Giannakos, Maria Knobelsdorf, Johannes Magenheim, Roland Mittermeir, and Sigrid Schubert. 2011. Computer Science/Informatics in Secondary Education. In Proceedings of the 16th Annual Conference Reports on Innovation and Technology in Computer Science Education - Working Group Reports (Darmstadt, Germany) (ITiCSE-WGR '11). Association for Computing Machinery, New York, NY, USA, 19--38. https://doi.org/10.1145/2078856.2078859
[15]
Peter Hubwieser, Michail N. Giannakos, Marc Berges, Torsten Brinda, Ira Diethelm, Johannes Magenheim, Yogendra Pal, Jana Jackova, and Egle Jasute. 2015. A Global Snapshot of Computer Science Education in K-12 Schools. In Proceedings of the 2015 ITiCSE on Working Group Reports (Vilnius, Lithuania) (ITICSE-WGR '15). Association for Computing Machinery, New York, NY, USA, 65--83. https://doi.org/10.1145/2858796.2858799
[16]
Lauren Katz. 2019. Google tried to make Pixel 4's facial recognition tech more inclusive for black people. The ends don't justify the means. - Vox . Vox recode (oct 2019). https://www.vox.com/recode/2019/10/17/20917285/google-pixel-4-facial-recognition-tech-black-people-reset-podcast
[17]
Maria Knobelsdorf and Ralf Romeike. 2008. Creativity as a Pathway to Computer Science. SIGCSE Bull., Vol. 40, 3 (June 2008), 286--290. https://doi.org/10.1145/1597849.1384347
[18]
Logan Koepke. 2016. Predictive Policing Isn't About the Future, It's about the past. SLATE (oct 2016). https://slate.com/technology/2016/11/predictive-policing-is-too-dependent-on-historical-data.html
[19]
Heidi Ledford. 2019. Millions of black people affected by racial bias in health-care algorithms ., bibinfonumpages608--609 pages. https://doi.org/10.1038/d41586-019-03228--6
[20]
Leila Meliani. [n.d.]. Machine Learning at PredPol: Risks, Biases, and Opportunities for Predictive Policing. ( [n.,d.]).
[21]
Vishnu S Pendyala and Silvia Figueira. 2017. Automated medical diagnosis from clinical data. In 2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService). IEEE, 185--190.
[22]
Mayring Philipp. 2000. Qualitative content analysis. In Forum: Qualitative Social Research, Vol. 1. 10.
[23]
Charles P. Riedesel, Eric D. Manley, Susan Poser, and Jitender S. Deogun. 2009. A model academic ethics and integrity policy for computer science departments. In Proceedings of the 40th ACM technical symposium on Computer science education - SIGCSE '09. ACM Press, New York, New York, USA, 357. https://doi.org/10.1145/1508865.1508994
[24]
David G. Robinson. 2017. The Challenges of Prediction: Lessons from Criminal Justice . I/S: A Journal of Law and Policy for the Information Society, Vol. 14, 151 (oct 2017). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3054115
[25]
Carsten Schulte and Maria Knobelsdorf. 2007. Attitudes towards Computer Science-Computing Experiences as a Starting Point and Barrier to Computer Science. In Proceedings of the Third International Workshop on Computing Education Research (Atlanta, Georgia, USA) (ICER '07). Association for Computing Machinery, New York, NY, USA, 27--38. https://doi.org/10.1145/1288580.1288585
[26]
Tom Simonite. 2019. A Health Care Algorithm Offered Less Care to Black Patients . https://www.wired.com/story/how-algorithm-favored-whites-over-blacks-health-care/
[27]
Alicia Nicki Washington. 2020. When Twice as Good Isn't Enough: The Case for Cultural Competence in Computing. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education . 213--219.
[28]
Gregory B. White and Udo W. Pooch. 1994. Computer ethics education. In Proceedings of the conference on Ethics in the computer age -. ACM Press, New York, New York, USA, 170--173. https://doi.org/10.1145/199544.199610

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
March 2021
1454 pages
ISBN:9781450380621
DOI:10.1145/3408877
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 March 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. computing education
  2. ethics
  3. social implication
  4. technology

Qualifiers

  • Research-article

Conference

SIGCSE '21
Sponsor:

Acceptance Rates

Overall Acceptance Rate 1,787 of 5,146 submissions, 35%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 196
    Total Downloads
  • Downloads (Last 12 months)29
  • Downloads (Last 6 weeks)4
Reflects downloads up to 08 Mar 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media