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Ethics of Using Online Proctoring Systems

The emergence of the COVID-19 pandemic in early 2020 has rapidly and fundamentally changed the status quo in all areas of human life, including education and technology. The unprecedented and rapid nature of changing educational mediums by switching from face-to-face classes to online education has revealed practical and ethical issues. This report focuses on identifying ethical problems that have resulted from using online proctoring systems (OPS) in the classroom setting following the switch to remote learning.

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A critical issue raised following the widespread usage of online proctoring platforms is biased machine learning (ML) algorithms, which offer little social diversity by disproportionately affecting non-white learners (Swauger, 2020). Individuals who do not have a Caucasian appearance also suffer from using specific proctoring systems due to what is described as insufficient lighting (García-Bullé, 2021). Overall, ML algorithms need to be improved to calibrate facial identification parameters correctly.

Confidentiality concerns have been voiced by those disadvantaged by online proctoring systems. For example, some proctoring systems require identity verification, which potentially endangers transgender and undocumented individuals (García-Bullé, 2021). This calls for a reconsideration of confidentiality policies in OPS.

Such platforms also create the potential for anonymity or data privacy violation due to the risk of data breaches that can leak their biometrical and behavioral data (Stewart, 2020). These issues should be addressed not to endanger vulnerable learners.

Maleficence is also seen as a disadvantage of OPS. Inaccurate cheating claims undermine one’s self-confidence and enable unfairness in an educational setting (Coghlan, Peterson, and Miller, 2020). Moreover, facial recognition software has been linked to bias due to the definition of ‘normal behavior’ (Coghlan, Peterson, and Miller, 2020). In other words, focusing on eye movements or making all students conform to certain behavioral standards disadvantages those with disabilities (Coghlan, Peterson, and Miller, 2020). Therefore, students’ academic performance and career prospects might suffer from ungrounded cheating claims.

The disregard for one’s autonomy is another important issue concerning online proctoring platforms. For example, students can mutter or use the restroom during in-person exams, whereas online proctored exams punish such ubiquitous activities (Coghlan, Peterson, and Miller, 2020). Limiting students’ freedom harms them psychologically and academically.

Overall, online proctoring systems that rely on machine learning have numerous ethical limitations. These include, but are not limited to, racial and disability discrimination, privacy violation, autonomy disregard, and unfairness. Those concerns should be addressed to increase the system’s efficacy.

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Reference List

Coghlan, S., Peterson, J. and Miller, T. (2020) ‘Good proctor or “Big Brother”? AI Ethics and Online Exam Supervision Technologies.’ arXiv preprint arXiv:2011.07647.

García-Bullé, S. (2021) ‘The Dark Side of Online Exam Proctoring’, Observatory of Educational Innovation. Web.

Stewart, B. (2020) ‘Online Exam Monitoring Can Invade Privacy and Erode Trust at Universities’, The Conversation. Web.

Swauger, S. (2020) ‘Our Bodies Encoded: Algorithmic Test Proctoring in Higher Education’, Hybrid Pedagogy. Web.

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