Pilot Project: Assessing Trustworthiness of the use of Generative AI for higher Education.
“I, personally and as president of ENSA (European Education New Society Association), fully endorse and support this pilot project of the well known Z-inspection® initiative. It is time to assess the impact and potential of generative AI on Universities and, in this respect, I believe the Z-inspection process, in combination with the European Commission ALTAI and UNESCO policy guidance, is best placed to achieve significant and inspiring results.“
This pilot project of the Z-inspection® initiative (https://z-inspection.org) aims at assessing the use of Generative AI in higher level education considering specific use cases.
For this pilot project, we will assess the ethical, technical, domain-specific (i.e. education) and legal implications of the use of Generative AI-product/service within the university context.
We follow the UNESCO guidance for policymakers on Geneative AI and education. In particular the policy recommendation: Pilot testing, monitoring and evaluation, and building an evidence base.
SELECTED USE CASE
Use of AI in Marking and Providing Feedback to Graduate Students for Individual Written Assignments in Health Informatics Courses.
Institution:
Dalla Lana School of Public Health, University of Toronto, Canada
Stakeholders:
Dr. Karim Keshavjee, Assistant Professor, Institute for Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto
Jennifer Tin, Adjunct Professor, IHPME, Dalla Lana School of Public Health, University of Toronto
Parisa Osivand, Adjunct Lecturer, IHPME, Dalla Lana School of Public Health, University of Toronto
Description of the use case: Marking and providing feedback on narrative assignments is time consuming and cognitively taxing. This leads to delayed and terse feedback that may not be satisfactory to the learner. Can AI be used to speed up marking and provide more substantive feedback to learners?
LINK to Overview of the Use Case (LinkedIn)
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Advisory Board
Kiran Bhujun, Professor, Director of the Tertiary Education and Scientific Research Division of the Ministry of Education, Tertiary Education, Science and Technology of the Republic of Mauritius.
Yves Deville, Professor, Université catholique de Louvain. Senior Advisor to the President for the Digital University at UCLouvain. Belgium
Julio Cesar Duhalde, Technical, economic and legal advisor at the Ministry of Economy – Buenos Aires, Argentina.
Erja Heikkinen, PhD, Director General (temp), Ministry of Education and Culture, Helsinki, Finland
Lambert Hogenhout, Chief Data, Analytics and Emerging Technologies, United Nations, New York, USA.
Jonathan Michie OBE FAcSS, Professor of Innovation and Knowledge Exchange, University of Oxford, Pro-Vice-Chancellor (without portfolio), President of Kellogg College, University of Oxford, UK.
Irina Mirkina, AI Lead, Office of Innovation, UNICEF, Stockholm, Sweden.
Victor Ochen, The African Youth Initiative Network (AYINET). Member of the Advisory Group to the United Nations High Commissioner for Refugees at United Nations- Lira, Uganda.
Sung Jae Park, Ph.D. Research fellow, Korean Educational Development Institute (KEDI). Former Senior Advisor to the Deputy Prime Minister and Minister of Education of the Republic of Korea. Seoul, South Korea.
Gerald Santucci, President, EUROPEAN EDUCATION NEW SOCIETY ASSOCIATION (ENSA)- France.
Willy Tadema, AI Ethics Lead, Rijks ICT Gilde (RIG), Ministry of Interior and Kingdom Relations; AI policy advisor, Ministry of Interior and Kingdom Relations; Member of the Dutch National Standards Body for AI (NEN), The Netherlands.
Peter J. Wells, Head of Education Southern Africa, UNESCO- Harare, Zimbabwe.
Approach
An interdisciplinary team of experts will assess the trustworthiness of Generative AI for selected use cases in High Education using the Z-Inspection® process: https://z-inspection.org
Z-Inspection® is a holistic process based on the method of evaluating new technologies, where ethical issues need to be discussed through the elaboration of socio-technical scenarios. In particular, Z-Inspection® can be used to perform independent assessments and/or self-assessments together with the stakeholders owning the use case.
For the context of this pilot project we define ethics in line with the essence of modern democracy i.e. “respect for others, expressed through support for fundamental human rights”. We take into consideration that “trust” in the development, deployment and use of AI systems concerns not only the technology’s inherent properties, but also the qualities of the socio-technical systems involving AI applications.
Specifically, we consider the ethics guidelines for trustworthy artificial intelligence defined by the EU High-Level Expert Group on AI, which defined trustworthy AI as:
(1) lawful – respecting all applicable laws and regulations
(2) ethical – respecting ethical principles and values
(3) robust – both from a technical perspective and taking into account its social environment
And we use the four ethical principles, rooted in fundamental rights defined in [13], acknowledging that tensions may arise between them:
(1) Respect for human autonomy
(2) Prevention of harm
(3) Fairness
(4) Explicability
Furthermore, we also consider the seven requirements of Trustworthy AI defined by the High Level experts group set by the EU. Each requirement has a number of sub-requirements as indicated in Table 1.
Table 1. Requirements and sub-requirements Trustworthy AI.
1 Human agency and oversight Including fundamental rights, human agency and human oversight
2 Technical robustness and safety Including resilience to attack and security, fall back plan and general safety, accuracy, reliability and reproducibility
3 Privacy and data governance Including respect for privacy, quality and integrity of data, and access to data
4 Transparency Including traceability, explainability and communication
5 Diversity, non-discrimination and fairness Including the avoidance of unfair bias, accessibility and universal design, and stakeholder participation
6 Societal and environmental wellbeing Including sustainability and environmental friendliness, social impact, society and democracy
7 Accountability Including auditability, minimization and reporting of negative impact, trade-offs and redress.
While we consider the seven requirements comprehensive, we believe additional ones can still bring value. Two of such additional requirements proposed by the Z-Inspection® initiative are “Assessing if the ecosystems respect values of Western Modern democracy” and “Avoiding concentration of power” .
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From the UNESCO guidance for policymakers on AI and education sets out policy recommendations in seven areas:
(https://unesdoc.unesco.org/ark:/48223/pf0000376709)
1. A system-wide vision and strategic priorities
2. Overarching principle for AI and education policies
3. Interdisciplinary planning and inter-sectoral governance
4. Policies and regulations for equitable, inclusive, and ethical use of AI
5. Master plans for using AI in education management, teaching, learning, and assessment
6. Pilot testing, monitoring and evaluation, and building an evidence base
7. Fostering local AI innovations for education
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from the Guidelines for the use of AI in teaching at the University of Helsinki Academic Affairs Council 16.2.2023
(https://teaching.helsinki.fi/instructions/article/artificial-intelligence-teaching)
“Large artificial intelligence (AI)-based language models such as Chat GPT, Google Bard, and DeepL have evolved to the point where they can produce human-like text and conversations and correct and transform text at such a high level that it can be difficult to distinguish the result from human- generated text. It is foreseeable that more such models will emerge, and their functionalities will continue to evolve, so their existence should be taken into account in university teaching and research.
The existence of large language models should be seen as an opportunity. Degree programmes and teachers are encouraged to use AI in their teaching and to prepare students for a society of the future where AI methods will be widely used.
As AI brings new possibilities for producing text whose origin and reliability is unclear, they should be used in a controlled way. Use may be restricted in teaching in situations where the use would not promote student learning.
At EU level, an AI regulation is under preparation, which will also apply to AI systems in education. In addition, there is an ethical policy on AI and its use, as well as an ethical code for teachers1 . The University’s guidelines may be further specified in the light of future regulation and technological developments.”
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Resources
1. Policy paper. Generative artificial intelligence in education. UK: The Department for Education’s (DfE) position on the use of generative artificial intelligence (AI) in High Education. Link to .PDF
2. Do Foundation Model Providers Comply with the Draft EU AI Act? Stanford researchers evaluate foundation model providers like OpenAI and Google for their compliance with proposed EU law on AI. Link
They identified a final list of 12 requirements and scored the 10 models using a 5-point rubric. The methodology for the study can be found here.
3. Leading universities in the UK (the Russell Group universities) have developed a set of principles on the use of generative AI tools in education. Here is the link.
4. Frontier AI Regulation: Managing Emerging Risks to Public Safety arXiv:2307.03718 [cs.CY] LINK
5. Zhaki Abdullah, Students, teachers will learn to properly use tools like ChatGPT: (Singapore Education Minister) Chan Chun Sing, Straits Times, 12 February 2023, LINK
6. U.S. Department of Education, Office of Educational Technology, Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations, Washington D.C., May 2023, LINK
7. Japanese schools to be allowed limited use of generative AI, Kyodo News, 22 June 2023, LINK
8. Higher Education Webinar: Implications of Artificial Intelligence in Higher Education , Tuesday, June 27, 2023, Council on Foreign Relations, LINK
9. Novelli, C., Casolari, F., Rotolo, A. et al. Taking AI risks seriously: a new assessment model for the AI Act. AI & Soc (2023). LINK
10. Academic without Borders, Bimonthly Newsletter n°59, July 2023.
11. On the use of artificial intelligence and in particular of ChatGPT in higher education. (UNESCO). Link to .PDF
12. KU Leuven, Responsible use of Generative Artificial Intelligence (GenAI) in research.These guidelines will be updated with new information and insights to keep them in line with the rapidly evolving technology (last updated June 24, 2023). The further integration of teaching and research guidelines is still on the agenda. LINK
14. The Norwegian Consumer Council published a detailed report “Ghost in the machine – Addressing the consumer harms of generative AI” outlining the harms, legal frameworks, and possible ways forward. In conjunction with this launch, the Norwegian Consumer Council and 14 consumer organizations from across the EU and the US demand that policymakers and regulators act. https://storage02.forbrukerradet.no/media/2023/06/generative-ai-rapport-2023.pdf
15. University of Melbourne: Inquiry into the use of generative AI in the education system. Submission to the House Standing Committee on Employment, Education and Training 14 July 2023. https://about.unimelb.edu.au/__data/assets/pdf_file/0032/396446/UoM-Submission-Inquiry-into-Generative-AI-in-Education-FINAL.pdf
16. Cornell University: Generative Artificial Intelligence for Education and Pedagogy.July 18, 2023. https://teaching.cornell.edu/sites/default/files/2023-08/Cornell-GenerativeAIForEducation-Report_2.pdf
17. The University of North Carolina at Chapel Hill: Teaching Use Guidelines for Generative Artificial Intelligence, https://provost.unc.edu/wp-content/uploads/2023/07/Teaching-Generative-AI-Use-Guidance_UNC-AI-Committee-June-15-202348.pdf
18. University of Sydney: 13 March, 2023 Students answer your questions about generative AI – part 2: Ethics, integrity, and the value of university. https://educational-innovation.sydney.edu.au/teaching@sydney/students-answer-your-questions-about-generative-ai-part-2-ethics-integrity-and-the-value-of-university/
19. The Berkman Klein Center for Internet & Society at Harvard University: Exploring the Impacts of Generative AI on the Future of Teaching and Learning https://cyber.harvard.edu/story/2023-06/impacts-generative-ai-teaching-learning
20. Stanford University: Pedagogic strategies for adapting to generative AI chatbots. Eight strategic steps to help instructors adapt to generative AI tools and chatbots. June 19, 2023, Center for Teaching and Learning. https://docs.google.com/document/d/1la8jOJTWfhUdNna5AJYiKgNR2-54MBJswg0gyBcGB-c/edit
21. Council of Europe: ARTIFICIAL INTELLIGENCE AND EDUCATION A critical view through the lens of human rights, democracy and the rule of law.November 2022
https://rm.coe.int/artificial-intelligence-and-education-a-critical-view-through-the-lens/1680a886bd
22. ANU Centre for Learning and Teaching: Chat GPT and other generative AI tools: What ANU academics need to know February 2023. https://teaching.weblogs.anu.edu.au/files/2023/02/Chat_GPT_FAQ-1.pdf
23.Guidance for Generative AI in education and research | UNESCO, 7 September 2023
25. UNESCO Guidance for generative AI in education and research, 2023
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