Academic Writing Assessment Task: AI Ethics
Title
Are Current Governance Frameworks Sufficient to Ensure Accountability in High-Risk AI Systems?
Purpose
This assignment evaluates students’ ability to construct a rigorous, evidence-based argument about AI ethics, focusing on accountability mechanisms (e.g., responsibilities, transparency, auditability, and remedies) in high-risk domains such as healthcare, hiring, credit scoring, and public safety. It assesses skills in thesis formulation, analytical evaluation, integration of credible sources, and adherence to scholarly conventions.
Task Instructions
- Write an argumentative research essay that critically appraises whether contemporary AI ethics guidelines and regulatory instruments sufficiently ensure accountability in high-risk AI systems.
- Develop a clear, defensible thesis and support it with logically structured arguments and robust evidence.
- Engage with multiple governance instruments and technical practices (e.g., EU AI Act, OECD AI Principles, NIST AI Risk Management Framework, model cards, datasheets, audits).
- Incorporate analysis of enforcement, oversight, and remedies (including redress for affected individuals and organizational responsibility).
- Address counterarguments and limitations.
- Conclude with precise, actionable recommendations (policy, organizational, or technical) justified by your analysis.
Deliverables and Format
- Length: 2,000–2,500 words (excluding references).
- Citation style: APA 7th edition.
- Sources: Minimum of 12 scholarly or policy sources; prioritize peer-reviewed studies, official standards, and statutes/regulations.
- Academic integrity: Use paraphrase with citations; include a reference list; disclose search strategies if asked.
Exemplar Thesis and Argument Structure (for guidance)
- Thesis statement:
While recent governance instruments—such as the EU AI Act, OECD AI Principles, and NIST’s AI RMF—advance accountability for high-risk AI, they remain insufficient without binding audit mandates, standardized documentation practices, and enforceable remedies for impacted individuals; therefore, a combined regime of statutory auditing, transparent model documentation, and rights-based redress is necessary to close the accountability gap (OECD, 2019; National Institute of Standards and Technology [NIST], 2023; European Union, 2024; Raji et al., 2020).
- Key arguments:
1) Conceptual adequacy: Ethics principles (e.g., transparency, fairness, accountability) show global convergence but vary in operationalization; principles alone do not create enforceable duties (Jobin, Ienca, & Vayena, 2019; OECD, 2019).
2) Regulatory scope and obligations: The EU AI Act’s risk-based approach imposes obligations (risk management, data governance, transparency) on high-risk systems, but practical oversight hinges on auditing capacity and supervisory resourcing; remedy mechanisms need clearer pathways for individual redress (European Union, 2024).
3) Risk management vs. accountability: NIST’s AI RMF operationalizes risk identification and mitigation but is voluntary; accountability requires clear role assignment, documentation, and independent audits (NIST, 2023; Raji et al., 2020).
4) Technical documentation as accountability infrastructure: Model cards and datasheets increase traceability and enable audits but must be standardized and mandated for high-risk deployments (Mitchell et al., 2019; Gebru et al., 2018).
5) Evidence of persistent harms: Empirical work on bias and opacity indicates ongoing risks in language models and decision-support systems, underscoring the need for stronger external audits and impact assessments (Bender et al., 2021).
6) Counterarguments: Overly prescriptive rules may slow innovation; however, targeted, proportionate auditing and transparency obligations in high-risk contexts balance innovation with harm prevention (OECD, 2019; IEEE, 2019).
7) Recommendations: Introduce statutory, third-party algorithmic audits for high-risk systems; mandate standardized documentation (model cards, datasheets); strengthen rights to contest and obtain explanations; align organizational accountability with professional codes and sectoral regulators (European Union, 2024; ACM, 2018; Executive Office of the President, 2023).
Evaluation Criteria (Rubric)
- Thesis and Argument Quality (25%): Clear, specific, and debatable thesis; coherent, logical progression; justified claims.
- Use of Evidence (25%): Relevance, credibility, and sufficiency of sources; accurate interpretation; triangulation across legal, technical, and empirical literature.
- Ethical Analysis and Conceptual Precision (15%): Correct definition and application of accountability, transparency, fairness, and remedy; domain-appropriate reasoning for high-risk AI.
- Engagement with Counterarguments and Limitations (10%): Fair representation of opposing viewpoints; discussion of trade-offs and uncertainty.
- Organization, Clarity, and Style (15%): Formal academic tone; clear structure; precise language; coherent paragraphs and transitions.
- Citation and Academic Integrity (10%): Correct APA formatting; consistent in-text citations; complete references; ethical use of sources.
Recommended Sources (APA 7th)
- ACM. (2018). ACM Code of Ethics and Professional Conduct. https://www.acm.org/code-of-ethics
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21), 610–623. https://doi.org/10.1145/3442188.3445922
- European Commission High-Level Expert Group on AI. (2019). Ethics guidelines for trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai
- European Union. (2024). Artificial Intelligence Act (Regulation on harmonised rules for AI). Official Journal of the European Union. [Use the consolidated text or official journal citation available at EUR-Lex.]
- Executive Office of the President. (2023). Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence (No. 14110). https://www.federalregister.gov
- Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2018). Datasheets for datasets. arXiv. https://arxiv.org/abs/1803.09010
- IEEE Standards Association. (2019). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems (1st ed.). https://ethicsinaction.ieee.org
- Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-5
- Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* ’19), 220–229. https://doi.org/10.1145/3287560.3287596
- National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). https://nvlpubs.nist.gov/nistpubs/AI/NIST.AI.100-1.pdf
- OECD. (2019). Recommendation of the Council on Artificial Intelligence (OECD/LEGAL/0449). https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
- Raji, I. D., Smart, A., White, R., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Denton, E., & Barnes, P. (2020). Closing the AI accountability gap: Defining roles, responsibilities, and remedies. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAccT ’20), 33–44. https://doi.org/10.1145/3351095.3372873
Notes for Students
- Define “accountability” explicitly (e.g., assignment of responsibilities, traceability, auditability, and enforceable remedies).
- Distinguish voluntary frameworks (principles, guidelines) from binding obligations (statutes/regulations) and discuss implications for enforcement.
- Use case evidence from high-risk sectors to ground claims.
- Ensure your recommendations follow logically from identified gaps and are feasible within organizational and regulatory constraints.
