For Engineering and Science
Aditya Johri
Commentary On
Role-play Instructions
1. Each student is assigned a role a week before the discussion.
2. Student assigned to the role of Trisha Brown serves as the moderator and leads the conversation based on the script below.
3. The script provided below is there to guide the discussion, but you should leave room for the conversation to flow naturally and allow everyone to contribute.
Script for the Role-play
1. What role are you playing in the role-play group discussion? Please state the name, title, and describe the role in your own words (couple of sentences).
[to be answered by each group members individually and in a sequence]
2. From the perspective of your role, what is your recommendation for Trisha regarding the use of FRT?
[to be answered by each group members individually and in a sequence]
3. From the perspective of your role, are there alternative solutions you would like to present to Trisha? Why do you think the approach you suggest is good and what are the main barriers to this approach?
[to be answered by each group members individually and in a sequence]
4. What is your overall group recommendation to Trisha?
[open discussion, anyone can chime in]
One way to ensure students are prepared for the discussion is to assign a few questions from the script as a pre-discussion assignment (short answers). Similarly, to ensure students reflect on the discussion, they can be assigned the last question from the script as a post-discussion exercise. They can also be asked specifically about ethical concepts or concerns related to FRT that have been introduced through the readings.
Extra Assignment - Concept Mapping
Draw a concept map to depict your group's decision. It should include different aspects of technology, applications, stakeholders, and/or other aspects that you considered in your discussion. The map should have between 10-12 concepts or items and should convey how they are related. You can use any medium to create and upload it, ideally as a jpeg. You can take a screenshot or even draw on paper and take a picture and upload it.
Resources to help with concept maps
https://learningcenter.unc.edu/tips-and-tools/using-concept-maps/ (see Example 3)
https://en.wikipedia.org/wiki/Concept_map
FRT Code of Ethics, Frameworks and Guidelines
• Ethical Framework for FRT Submitted by the ACLU to the NTIA Multistakeholder Process on Facial Recognition Technology: https://www.ntia.doc.gov/files/ntia/publications/aclu_an_ethical_framework_for_face_recognition.pdf
• A. K. Roundtree, "Facial Recognition Technology Codes of Ethics: Content Analysis and Review," 2022 IEEE International Professional Communication Conference (ProComm), Limerick, Ireland, 2022, pp. 211-220, doi: 10.1109/ProComm53155.2022.00045. https://ieeexplore.ieee.org/document/9881633
• Center for Strategic and International Studies (CSIS) report on “Facial Recognition Technology: Responsible Use Principles and the Legislative Landscape”: https://www.csis.org/analysis/facial-recognition-technology-responsible-use-principles-and-legislative-landscape
Background Readings, Videos, and Other Resources
• Wicker, S. & Ghosh, D. (2020). Reading in the Panopticon: Your Kindle May Be Spying on You, But You Can't Be Sure. Communications of the ACM, Vol. 63 No. 5, Pages 68-73.
• Lanchester, J. (2017). You are the product. London Review of Books. Vol. 39, No. 6.
• ACLU Resource Page on FRT: https://www.aclu.org/issues/privacy-technology/surveillance-technologies/face-recognition-technology
• Kate Crockford – What you need to know about face surveillance (2019): https://www.ted.com/talks/kade_crockford_what_you_need_to_know_about_face_surveillance?language=en
• Alessandro Acquisti – What will a future without secrets look like? (2013): https://www.ted.com/talks/alessandro_acquisti_what_will_a_future_without_secrets_look_like
• Glenn Greenwald – Why privacy matters (2014): https://www.ted.com/talks/glenn_greenwald_why_privacy_matters
• UK Government's Center for Data Ethics and Innovation's Independent report "Snapshot Paper - Facial Recognition Technology" May 2020
https://www.gov.uk/government/publications/cdei-publishes-briefing-paper-on-facial-recognition-technology/snapshot-paper-facial-recognition-technology
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/905267/Facial_Recognition_Technology_Snapshot_UPDATED.pdf
Authorship and Project Information and Acknowledgements
The scenarios and roles were conceptualized and written by Aditya Johri. Feedback was provided by Ashish Hingle, Huzefa Rangwala, and Alex Monea, who also collaborated on initial implementation and empirical research. This work is partly supported by U.S. National Science Foundation Awards# 1937950, 2335636, 1954556; USDA/NIFA Award# 2021-67021-35329. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. The research study associated with the project was approved by the Institutional Review Board at George Mason University.
Commentary On
Role-play Instructions
1. Each student is assigned a role a week before the discussion.
2. Students assigned to the role of Nina serve as the moderator and lead the conversation based on the script below.
3. The script provided below is there to guide the discussion, but you should leave room for the conversation to flow naturally and allow everyone to contribute.
Role-play script (for Nina)
1. What role are you playing in the role-play group discussion? Please state the name, title, and describe the role in your own words (couple of sentences).
[to be answered by each group members individually and in a sequence]
2. From the perspective of your role, what do you consider to be the best approach to decide on a loan application – what factors should be considered and how should these factors be weighed (what should get more importance)?
[to be answered by each group members individually and in a sequence]
3. What decision should Nina take on Yilmaz’s loan: should it be approved or declined? What additional information would you recommend Nina try to obtain in order to make the decision, keeping in mind that there is not much time left to acquire that information?
[to be answered by each group members individually and in a sequence]
4. What is your overall group recommendation to Nina?
[open discussion, anyone can chime in]
One way to ensure students are prepared for the discussion is to assign a few questions from the script as a pre-discussion assignment (short answers). Similarly, to ensure students reflect on the discussion, they can be assigned the last question from the script as a post-discussion exercise. They can also be asked specifically about concepts or concerns considered in making a loan.
Reflective Exercise
[This can be individual or group]
- What solution was reached following the discussion?
- What criteria were considered to reach this solution?
- Was the solution agreed to by all or did one person have more influence? Why?
- Do you personally agree with the solution reached? Why/Why not?
- Did playing a role help you/change in perspective (before/after the discussion)?
Dataset for Additional Analysis
https://www.kaggle.com/datasets/uciml/german-credit
Lee, M. S. A., & Floridi, L. (2021). Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs. Minds and Machines, 31(1), 165-191. (Link to data used: https://www.consumerfinance.gov/data-research/hmda/historic-data/)
Frameworks
Klein, A. (2020). Reducing bias in AI based financial services, Brookings Institution.
https://www.brookings.edu/articles/reducing-bias-in-ai-based-financial-services/
World Bank’s Credit Scoring Approaches Guidance:
https://thedocs.worldbank.org/en/doc/935891585869698451-0130022020/original/CREDITSCORINGAPPROACHESGUIDELINESFINALWEB.pdf
Background Resources
• Susan Etlinger - What do we do with all this big data? TED Talk http://www.ted.com/talks/susan_etlinger_what_do_we_do_with_all_this_big_data
• Cathy O’Neil: The Era of Blind Faith in Big Data Must End TED Talk https://www.ted.com/talks/cathy_o_neil_the_era_of_blind_faith_in_big_data_must_end?language=en
• Shivani Siroya – A smart loan for people with no credit history (yet) TED Talk https://www.ted.com/talks/shivani_siroya_a_smart_loan_for_people_with_no_credit_history_yet?language=en
• Michael Volpe, "Experts say artificial intelligence contributes to discrimination in lending" July 10, 2019, In The News https://sylviagarcia.house.gov/media/in-the-news/experts-say-artificial-intelligence-contributes-discrimination-lending
• New York Times, Is an Algorithm Less Racist than a loan officer?
https://www.nytimes.com/2020/09/18/business/digital-mortgages.html
• Townson, S. “AI can make bank loans more fair”, HBR (2020)
https://hbr.org/2020/11/ai-can-make-bank-loans-more-fair
• Berg et al., NBER Working Paper, “The rise of FinTechs: Credit scoring using digital footprints” https://www.nber.org/papers/w24551
Authorship and Project Information and Acknowledgements
The scenarios and roles were conceptualized and written by Aditya Johri. Feedback was provided by Ashish Hingle, Huzefa Rangwala, and Alex Monea, who also collaborated on initial implementation and empirical research. This work is partly supported by U.S. National Science Foundation Awards# 1937950, 2335636, 1954556; USDA/NIFA Award# 2021-67021-35329. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. The research study associated with the project was approved by the Institutional Review Board at George Mason University.
Role-play Instructions
1. Each student is assigned a role a week before the discussion.
2. Students assigned to the role of Brad Jorgensen and/or Kathy Schmidt serve as the moderator and lead the conversation based on the script below.
3. The script provided below is there to guide the discussion, but you should leave room for the conversation to flow naturally and allow everyone to contribute.
Role-play Script (for Brad/Kathy)
1. What role are you playing in the role-play group discussion? Please state the name, title, and describe the role in your own words (couple of sentences).
[to be answered by each group members individually and in a sequence]
2. From the perspective of your role, how would you respond to Brad and Kathy’s question about why the disaster happened and how it could have been prevented?
[to be answered by each group members individually and in a sequence]
3. From the perspective of your role, what is your response to Brad and Kathy’s question about how can we ensure future safety and transparency and rebuild trust? Why do you think the approach you suggest is the best approach? What do you think are the main barriers to this approach?
[to be answered by each group members individually and in a sequence]
4. What is your overall group recommendation to Brad/Kathy?
[open discussion, anyone can chime in]
One way to ensure students are prepared for the discussion is to assign a few questions from the script as a pre-discussion assignment (short answers). Similarly, to ensure students reflect on the discussion, they can be assigned the last question from the script as a post-discussion exercise. They can also be asked specifically about ethical concepts or concerns related to safety and transparency.
Ethical Codes and Guidelines
Several different ethical codes or guidelines can be provided to students to prepare for the discussion or to reflect upon during their discussion depending on the students’ disciplinary composition. For instance, for implementation in a computing or technology related course ACM and IEEE guidelines can be more informative and the discussion can be centered largely on the MACS software (how did the algorithm work, why was it implemented, who designed it, why were the pilots not informed about it, etc.).
American Institute of Aeronautics and Astronautics code of ethics:
https://www.aiaa.org/about/Governance/Code-of-Ethics
Airline pilots’ association code of ethics:
https://www.alpa.org/en/about-alpa/what-we-do/code-of-ethics
FAA Ethics of Maintenance:
https://www.faasafety.gov/files/gslac/courses/content/718/2173/HF%20Chapter%2011.pdf
ACM Code of Ethics:
https://www.acm.org/code-of-ethics
IEEE Code of Ethics:
https://www.ieee.org/about/corporate/governance/p7-8.html
National Society of Professional Engineers code of ethics:
https://www.nspe.org/sites/default/files/resources/pdfs/Ethics/CodeofEthics/NSPECodeofEthicsforEngineers.pdf
Background Readings and Resources
One of the goals of this exercise is to motivate students to undertake their own research on the topic to prepare for the role they are playing. But it is important to provide them with preliminary material to start their own research.
Videos
Wall Street Journal report “How Boeing Rocked the Aviation Industry”:
https://www.youtube.com/watch?v=0jTN0JD4I5M&feature=youtu.be
Vox’s “The real reason Boeing’s new plane crashed twice”:
https://youtu.be/H2tuKiiznsY
Bloomberg’s “How Boeing Lost Its Way”:
https://www.youtube.com/watch?v=EESYomdoeCs
Readings
Johnston, P., & Harris, R. (2019). The Boeing 737 MAX saga: lessons for software organizations. Software Quality Professional, 21(3), 4-12.
Herkert, J., Borenstein, J., & Miller, K. (2020). The Boeing 737 MAX: Lessons for engineering ethics. Science and engineering ethics, 26, 2957-2974.
Travis, G. (2019). How the Boeing 737 Max disaster looks to a software developer. IEEE Spectrum, 18.
A Rebuttal to Travis’ article from ACM Risks Digest: https://catless.ncl.ac.uk/Risks/31/21#subj20
Official information provided by Boeing:
https://www.boeing.com/commercial/737max/737-max-software-updates.page
Seattle Times Coverage:
https://www.seattletimes.com/business/boeing-737-max-crisis-2019-news-coverage/
The New Yorker (in collaboration with ProPublica):
MacGillis, A. (2019). The Case Against Boeing.
https://www.newyorker.com/magazine/2019/11/18/the-case-against-boeing
Authorship and Project Information and Acknowledgements
The scenarios and roles were conceptualized and written by Aditya Johri. Feedback was provided by Ashish Hingle, Huzefa Rangwala, and Alex Monea, who also collaborated on initial implementation and empirical research. This work is partly supported by U.S. National Science Foundation Awards# 1937950, 2335636, 1954556; USDA/NIFA Award# 2021-67021-35329. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies. The research study associated with the project was approved by the Institutional Review Board at George Mason University.
