Chapter 7: Research and Development ● How can we more clearly define what we mean by a context-sensitive technology in terms that are both concrete and broad enough? How can we measure it? ● To what extent are technical indicators and human observations of bias or unfairness working together with human observations? How can concerns about ethics and equity in AI technologies become actionable both in R&D, and later, when AI is widely used? ● Are we learning for whom and under what conditions AI systems produce desired benefits and impacts and avoid undesirable discrimination, bias, or negative outcomes? 7.8. Desired National R&D Objectives Attendees sought immediate progress on some key R&D issues, such as these: • Clarifying and achieving a consensus on the terms that go beyond data privacy and data security, including ideas like human-centered, value-sensitive, responsible, ethical, and safe so constituents can advocate for their needs meaningfully and consistently • Creating and studying effective programs for AI literacy for students, teachers, and educational constituents in general, including literacy with regard to the ethics and equity issues specific to AI in educational settings • Advancing research and development to increase fairness, accountability, transparency, and safety in AI systems used in educational settings • Defining participatory or co-designed research processes that include educators in the development and conduct of 84 | P a g e
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