Responsible AI Principles and Practices

At sideby, we are dedicated to designing and teaching about AI in ways that reflect our values of amplifying educator strengths, fostering inclusive innovation, and creating equitable, sustainable solutions. While we are currently a small, agile startup, our vision is to grow into a global organization. These principles will remain at the heart of our work, evolving thoughtfully as we expand our reach and impact.

Core Principles

1. Equity-Driven Innovation

  • Commitment: Develop AI tools and educational practices that celebrate diversity, amplify strengths, and provide equitable opportunities for educators and learners globally.

  • Practices:

    • Center design processes on the lived experiences and insights of diverse communities (Barik, 2023; Moore et al., 2018).

    • Conduct ongoing equity-focused evaluations to ensure all tools serve a broad range of needs inclusively (Brownlee, 2022).

2. Transparent and Trustworthy Practices

  • Commitment: Build trust by being open about how our AI systems operate, their strengths, and their limitations.

  • Practices:

    • Offer clear, accessible explanations for AI decisions, fostering user confidence and informed engagement (Google AI Principles, 2018; Stanford Institute for Human-Centered Artificial Intelligence, 2024).

    • Create open communication channels to gather user feedback, enabling continuous improvement (Google AI Principles, 2018).

3. Asset-Based Privacy and Security

  • Commitment: Treat user data as a valuable resource for empowerment while upholding the highest privacy and security standards.

  • Practices:

    • Design privacy-first systems that prioritize user control and informed consent (UNESCO, 2021; Katz & Rideout, 2021).

    • Regularly review and strengthen data security protocols to reflect evolving risks and user needs (Katz & Rideout, 2021).

4. Continuous Learning and Adaptation

  • Commitment: Maintain a culture of curiosity and growth, ensuring that our tools and practices evolve with the needs of educators, learners, and the global community.

  • Practices:

    • Use iterative design informed by user feedback and cutting-edge research (Holland & The Learner Accelerator Team, 2022; Reich & Ito, 2017).

    • Transparently test and refine tools to deliver reliable, impactful solutions while fostering a spirit of learning (Reich & Ito, 2017).

5. Holistic, Human-Centered Design

  • Commitment: Embed empathy, cultural humility, and inclusive practices in every aspect of AI and education design.

  • Practices:

    • Equip our team with training in cultural responsiveness, equity-centered design, and addressing unconscious biases (Buolamwini, 2016; Yosso, 2005).

    • Co-create solutions with stakeholders, ensuring tools reflect diverse needs and priorities (Buolamwini, 2016; Yosso, 2005).

6. Sustainability and Scalability

  • Commitment: Design systems that are both sustainable for our current team and scalable to support global communities.

  • Practices:

    • Prioritize adaptable, resource-efficient solutions that grow alongside our users and their environments (Signé, 2022; Office of Educational Technology, n.d.).

    • Focus on providing accessible tools that promote long-term impact across educational ecosystems (Jackson et al., 2024).

Operational Practices

1. Inclusive Team Building

  • Commitment: Assemble a team as diverse and innovative as the educators and learners we serve.

  • Practices:

    • Use inclusive, equity-driven hiring processes to attract exceptional talent committed to digital equity (Johnson, Bates, & Smith, 2023; Byrum & Benjamin, 2022).

    • Design onboarding processes that foster collaboration, shared ownership, and alignment with sideby’s mission (Johnson, Bates, & Smith, 2023).

2. Ethical Decision-Making Frameworks

  • Commitment: Anchor all decisions in ethical, user-centered principles to build a trusted global presence.

  • Practices:

    • Engage an advisory board representing diverse expertise and perspectives to guide organizational growth and strategy (Byrum & Benjamin, 2022).

    • Make decisions collaboratively, emphasizing transparency and adaptability to align with ethical standards (Byrum & Benjamin, 2022).

3. Accessible Design and Deployment

  • Commitment: Ensure our tools are intuitive, adaptable, and meaningful for all educators and learners, regardless of context.

  • Practices:

    • Provide professional learning opportunities to empower educators in using AI effectively (Transcend, 2024; Liu et al., 2024).

    • Support customization and multilingual features to reflect the needs of global communities (Jackson et al., 2024).

4. Amplifying Educator Agency

  • Commitment: Place educators at the center of innovation as co-creators and leaders in AI integration.

  • Practices:

    • Foster professional development opportunities that enhance AI literacy and practice (Transcend, 2024; Liu et al., 2024).

    • Actively incorporate educator feedback into tool design, creating iterative, user-informed improvements (Reich & Ito, 2017; Holland & The Learner Accelerator Team, 2022).

Commitment to Growth with Integrity

sideby envisions becoming a global leader in AI and education, bringing together educators, technologists, and learners to co-create meaningful change. As we grow, these principles will remain a foundation of our identity—adapting to new challenges and opportunities while staying rooted in equity, transparency, and a commitment to amplifying the strengths of the communities we serve.

Developed from*:

Barik, N. (2023). Global research on digital divide: A bibliometric study of Web of Science indexed literature. Global Knowledge, Memory and Communication.

Brownlee, M. I. (2022). Bridging the digital divide and digital equity gap in higher education. EdTech Magazine. Retrieved from https://edtechmagazine.com/higher/article/2022/03/understanding-digital-equity-gap-and-bridging-digital-divide-higher-ed-perfcon

Buzzetto-Hollywood, N. A., Wang, H. C., Elobeid, M., & Elobaid, M. E. (2018). Addressing information literacy and the digital divide in higher education. Interdisciplinary Journal of e-Skills and Lifelong Learning, 14(1), 77–93.

Buolamwini, J. (2016, November). How I'm fighting bias in algorithms [Video]. TED. https://www.ted.com/talks/joy_buolamwini_how_i_m_fighting_bias_in_algorithms?subtitle=en

Byrum, G., & Benjamin, R. (2022, June 16). Disrupting the gospel of tech solutionism to build tech justice. Stanford Social Innovation Review. https://ssir.org/articles/entry/disrupting_the_gospel_of_tech_solutionism_to_build_tech_justice

Eloundou, T., Beutel, A., Robinson, D. G., Gu-Lemberg, K., Brakman, A.-L., Mishkin, P., Shah, M., Heidecke, J., Weng, L., & Kalai, A. T. (2024, October 15). First-person fairness in chatbots. OpenAI. https://cdn.openai.com/papers/first-person-fairness-in-chatbots.pdf

Holland, B. & The Learner Accelerator Team. (2022). From digital access to digital equity: Critical challenges that leaders and policymakers must address to move beyond boxes & wires. The Learning Accelerator. https://practices.learningaccelerator.org/artifacts/from-digital-access-to-digital-equity-critical-challenges-that-leaders-and-policymakers-must-address-to-move-beyond-boxes-wires

Google. (n.d.). AI principles. Google AI. Retrieved January 14, 2025, from https://ai.google/responsibility/principles/

Jackson, J. K., Starr, J., & Weaver, D. (2024). A framework for digital equity. Digital Promise. https://digitalpromise.org/digital-equity/about-the-framework/

Johnson, A. M., Bates, S., & Smith, K. (2023). Hidden in plain sight: The opportunity to bridge district equity gaps by fostering collaborations with BIPOC solution providers. Digital Promise. https://digitalpromise.dspacedirect.org/items/b6e86f03-55b6-4577-a7a6-927065ac9bff

Kannan, P. (2024, October 3). How harmful are AI’s biases on diverse student populations? Stanford HAI. https://hai.stanford.edu/news/how-harmful-are-ais-biases-diverse-student-populations

Katz, V. S., & Rideout, V. (2021). Learning at home while under-connected: Lower-income families during the COVID-19 pandemic. Common Sense Media. https://d1y8sb8igg2f8e.cloudfront.net/documents/Katz_and_Rideout_-_Learning_at_Home_While_Under-Connected_gFRh1AO.pdf

Liu, S. W., Shourie, V., Ahmed, I., Moraski, B., & Ngo, C. (2024, September 10). The Higher Education Language Model Multidimensional Multimodal Evaluation Framework. ASU Enterprise Technology. https://issuu.com/asu_uto/docs/highered_language_model_evaluation_framework

Moore, R., Vitale, D., & Stawinoga, N. (2018). The digital divide and educational equity: A look at students with very limited access to electronic devices at home. ACT Center for Equity in Learning. https://files.eric.ed.gov/fulltext/ED593163.pdf

Office of Educational Technology. (n.d.). The digital access divide. U.S. Department of Education. Retrieved September 30, 2024, from https://tech.ed.gov/netp/digital-access-divide/

Reich, J., & Ito, M. (2017). From good intentions to real outcomes: Equity by design in learning technologies. Connected Learning Alliance. https://clalliance.org/wp-content/uploads/2017/11/GIROreport_1031.pdf

Schwartz, D., & Pope, D. (Hosts). (2021, November 17). The future of digital education [Audio podcast episode]. In School’s In. Stanford Graduate School of Education. Retrieved from https://ed.stanford.edu/news/future-digital-education

Software & Information Industry Association. (2023). Principles for AI in education. https://edtechprinciples.com/principles-for-ai-in-education/

Stanford Institute for Human-Centered Artificial Intelligence. (2024). Responsible AI. In AI Index Report 2024 (Chapter 3). Stanford University. https://aiindex.stanford.edu/wp-content/uploads/2024/04/HAI_AI-Index-Report-2024_Chapter3.pdf

Signé, L. (2022). Fixing the global digital divide and digital access gap. Brookings Institution. https://www.brookings.edu/articles/fixing-the-global-digital-divide-and-digital-access-gap/

Transcend. (2024). AI and extraordinary learning for all: A vision for transformative education. Transcend Education. https://transcendeducation.org/wp-content/uploads/2024/09/AI-and-Extraordinary-Learning-for-All_20240320.pdf

UNESCO. (2021). AI Competency Framework for Students. https://unesdoc.unesco.org/ark:/48223/pf0000391105

United Nations Development Programme. (n.d.). Bridge the digital divide. United Nations Development Programme. Retrieved from https://www.undp.org/digital/standards/2-bridge-digital-divide

Yosso, T. J. (2005). Whose culture has capital? A critical race theory discussion of community cultural wealth. Race Ethnicity and Education, 8(1), 69–91. https://doi.org/10.1080/1361332052000341006

Vassilakopoulou, P., & Hustad, E. (2023). Bridging digital divides: A literature review and research agenda for information systems research. Information Systems Frontiers, 25, 955–969. https://doi.org/10.1007/s10796-020-10096-3

*These are the resources specific to digital equity, used specifically in the creation of our AI policies and practices, but we also pulled from our Core Values as an organization which have been developed from additional resources as cited there.

These principles and practices reflect our current commitments and values. As we grow and learn, they may evolve to better serve our mission and community. We will adapt thoughtfully and communicate updates as needed.