AI

Cultivating critical AI literacy with a focus on ethics, connections to ethnic studies and critical civic inquiry can inspire movement toward social justice. Students can deepen their critical AI literacy while studying topics such as: algorithmic bias and harm, emotion and affect recognition, surveillance, datafication, labor practices, environmental impact, economic trade-offs, emerging transformative uses of AI, policy and regulation. These are anchored in state content standards and curriculum frameworks with interdisciplinary and co-design approaches. They also align to the ISTE Standards addressing digital citizenship for students, educators, and education leaders as equity and citizenship advocates. 

The following lessons are provided as a unit on California Educators Together with these corresponding essential questions:

AI Bias & Harm 

How is artificial intelligence (AI) used in ways that reinforce bias and prejudice?

AI Environmental Impact 

How does using GenAI impact the environment and what should we do about it?

AI, Media & Politics

How is AI used to influence public opinion of political issues?

• AI Competency Framework for Teachers (UNESCO, 2024): 

  • "human rights, human agency, promotion of linguistic and cultural diversity, inclusion and environmental sustainability" (p. 29) ... "data privacy, intellectual property rights and other legal frameworks" (p. 34) ... "sociocultural and environmental concerns in the design and use of AI, and contributing to the co-creation of ethical rules for AI practices in education" (p. 39)

• AI Competency Framework for Students (UNESCO, 2024)

  • "students as AI co-creators and responsible citizens… emphasizes critical judgement of AI solutions"

  • Ethics of AI - Embodied Ethics:

  • “Students are expected to be able to develop a basic understanding of the ethical issues around AI, and the potential impact of AI on human rights, social justice, inclusion, equity and climate change within their local context and with regard to their personal lives. They will understand, and internalize the following key ethical principles, and will translate these in their reflective practices and uses of AI tools in their lives and learning:

• Do no harm: Evaluating AI’s regulatory compliance and potential to infringe on human rights

• Proportionality: Assessing AI’s benefits against risks and costs; evaluating context- appropriateness

• Non-discrimination: Detecting biases and promoting inclusivity and sustainability (understanding AI’s environmental and societal impacts) 

• Human determination: Emphasizing human agency and accountability in AI use

• Transparency: advocating for the rights of users to understand AI operations and decisions

• Ethics of AI - Safe and Responsible Use: Students are expected to be able to carry out responsible AI practices in compliance with ethical principles and locally applicable regulations. They are expected to be conscious of the risks of disclosing data privacy and take measures to ensure that their data are collected, used, shared, archived and deleted only with their deliberate and informed consent. They are also expected to be conscious of typical AI incidents and the specific risks of certain AI systems, and be able to protect their own safety and that of their peers when using AI. 

• Ethics of AI - Ethics by Design: Students are expected to be able to adopt an ethics-by-design approach to the design, assessment and use of AI tools as well as the review and adaptation of AI regulations. Students are expected to be aware that the assessment and ratification of the intent of the AI design should start from the conceptualization stage and cover all steps of the AI life cycle. Students should be able to apply parameters to assess the compliance of an AI tool with ethical regulations and use an ethical matrix of multi-stakeholders to review AI regulations and inform adaptation.

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