The Integrated Curriculum for Socially Responsible Computing is our answer. It sets out the social, ethical, and political knowledge and skills we want students to build over the course of a CS degree, and suggests where in the curriculum each topic fits best. Rather than gathering these questions into a single ethics course, it weaves them into technical courses, from introductory sequences to upper-level electives, so that students meet them where they actually arise in computing work and deepen their understanding as they progress.
The curriculum is a resource for instructors and teaching staff, including our Socially Responsible Computing Teaching Assistants (STAs), deciding what SRC content to include in a course, and for students and colleagues who want to see how responsible computing fits into a computer science education. It is organized into five topic areas. Each one contains learning objectives that state what students should be able to do, at which skill level, and in which kinds of courses we suggest the content be taught.
Help courses determine what to teach in terms of SRC
Reduce duplication of SRC content across courses
Establish the SRC-related skills that a Brown CS graduate should have
Allow courses to build on SRC topics across courses and deepen the quality of content
Five Skill Levels
Each learning objective names the skill level students should reach. The levels build on one another, from knowing that an issue exists to acting on it in technical work. Throughout this page, they are marked with these colored labels.
Students have heard of a concept/idea and how it relates to a given (technical) context. Students are able to recall the concept/idea as relevant in a specific context. (“I know X is a thing.”)
Students can explain the meaning and/or significance of a specific concept/idea. They can articulate how it functions and/or why it is important.
Students are able to recognize relevant concerns in a specific technical context without prompting and are able to articulate how they would affect relevant technical decisions.
Students are able to critically evaluate a specific concept/idea, either abstractly or in a specific (social or technical) context. They are able to cite and engage with different concerns and/or (conflicting) perspectives on the topic in question.
Students are able to execute concrete technical decisions or use specific techniques to achieve a desired objective.
I.a.1: Representing aspects of the world through data is necessarily reductive and incomplete. Developer perspective shapes the choice and design of variables.
Students have heard of the limitations of data and developer perspective for creating truly representative models. (“I know all models are imperfect.”)
For example: They have heard of the impossibility of creating a comprehensive climate model.
Students can explain the meaning and/or significance of representing aspects of the world through data. They can articulate how data and datasets lead to imperfect models, how developer choice will shape the design of the model, and why data are important to interpreting and building incomplete and reductive models.
For example: Students can explain why no dataset can possibly cover all relevant variables when building a model predicting the rise of temperatures in Latin America, and how/why the choices of what to include/exclude will impact any model, including a climate model.
Students are able to recognize the limitations of a given model or dataset without prompting. They are able to articulate how the choice and design of variables affect a model.
For example: Given a dataset of historical temperatures in Latin American countries, students are able to recognize the inclusion/exclusion of some country or variable, such as cloud cover, wind speed, or time of temperature recorded will impact the accuracy of a climate model.
Students are able to critically evaluate choice and design of variables in creating an imperfect model. They are able to cite and engage with different concerns and/or (conflicting) perspectives on creating models.
For example: Students are able to critically evaluate whether data on rising sea levels is or is not important to include in a climate model, and how this potentially interacts with other values, such as runtime, transparency, or accessibility.
Students are able to execute concrete technical decisions or use specific techniques to achieve a desired model for their purposes and within the appropriate context.
For example: Students are able to execute feature engineering or hyperparameter tuning to further the accuracy of their model, while recognizing that their model will still be necessarily reductive and incomplete.
Learning Objectives
Understand that representing aspects of the world through data is necessarily reductive and incomplete. Developer perspective shapes the choice and design of variables
Identify variables in datasets that emphasize a particular perspective and the possible impacts of redesigning those datasets
Understand different sources of bias in algorithmic and AI systems (societal vs. dataset vs. system parameters) and their interactions
Be aware of and Understand metrics to mathematically interpret fairness
Be aware of approaches for bias mitigation
Be aware of and Understand different concepts of justice as they relate to automated decision-making systems
Understand context-specific limitations of automated decision-making and Analyze when the use of such systems is appropriate and when human decision-making is preferable or required
Understand and Analyze different standards for interpreting algorithmic and AI decisions: e.g., transparency, explainability
Understand different methods and strategies for AI governance
Understand the distinct mechanisms by which AI systems can produce harm
Be aware of strategies to identify, evaluate, and mitigate harms stemming from unpredictability in AI systems
Be aware of the effects of widespread use of synthetic content on one’s ability to control their likeness and public perception
Be aware of the legal frameworks governing copyright and intellectual property as they relate to generative AI
Be aware of opportunities and risks of generative AI in the future of work (e.g., deskilling, lack of connectivity)
Be aware of the extent of data collection and reliance of technologies on large volumes of personal data
Understand and Identify different mechanisms of data collection—overt, covert, and from other sources (i.e., via sharing)—and their implications on privacy
Understand the limitations of the distinction between sensitive and non-sensitive data, especially considering the potential for re-identification
Understand the value and functions of privacy (including how both short-term and long-term privacy risks may threaten those)
Understand (and Implement) key aspects of major privacy regulations, standards, etc. (e.g., CCPA, GDPR, …)
Analyze trade-offs between privacy and other values, such as transparency, accountability, safety, or security
Understand surveillance and Identify instances of data collection that enable surveillance
Understand different conceptions of consent, and its potential and limitations as an instrument for data protection
Be aware of widely-used privacy-preserving/enhancing technologies (e.g., encryption, differential privacy)
Understand (and Implement) different design processes for evaluating the needs of diverse users and Analyze their appropriateness in various contexts
Understand and Identify how different biases can influence one’s judgment and shape design decisions
Understand varying definitions and conceptions of accessibility
Identify opportunities to apply and Implement standards and best practices for accessibility
Identify and Analyze intersections between accessibility and other values such as privacy, security, or sustainability
Be aware of how technology is used to direct users’ attention and shape their interests, preferences, and desires
Be aware of how psychological mechanisms and cognitive biases can be exploited by technical means to direct user attention and behavior (e.g., nudging via deceptive patterns)
Analyze the value of attention and the consequences of yielding control over it
Analyze the effect of technologies on our self-perception and relationship with ourselves
Analyze the effect of technologies on the nature and quality of interpersonal relationships
Be aware of and Identify potential environmental impacts of a specific technology:
Understand how the environmental impacts of technology have disproportionate socioeconomic effects on different populations
Understand the need for infrastructural sustainability and the means and challenges of ensuring hardware and software maintenance and preservation
Understand the need for and challenges of ensuring data sustainability
Identify skills and roles that advancements in specific technologies (e.g., automation, generative AI) are likely to displace, create, or evolve, and Analyze the (socio-)economic implications of these changes
Be aware of the human labor involved in and the physical and psychological effects of:
Understand the effect of content moderation on social pluralism
Understand the effects of algorithmic/AI-based curation of information on public discourse (e.g., filter bubbles, echo chambers)
Understand the role of specific technologies (e.g., social media, generative AI) in spreading misinformation or disinformation
Be aware of the potential and limitations of technical and non-technical mitigation approaches to curb the spread of mis-/disinformation
Understand how technical infrastructure and regulation of that infrastructure shapes the ways in which we communicate and consume information
Be aware of how easy access to recording devices and social networks for distribution creates transparency and enables individuals and social movements to hold institutions and other powerful actors accountable (i.e., sousveillance)
Understand how global power structures shape technological development and how technological development, in turn, reshapes global power structures
Be aware of the long-term history of technological development and Analyze the impact of current changes in light of that history
Analyze the impact of technology on military capabilities and conflict
Identify the underlying epistemological assumptions of Computer Science and Be aware of how concepts such as abstraction, scalability, measurability, and efficiency shape technological design and value systems
Understand the concept of a stakeholder and Identify various stakeholders in a given project
Identify and Implement stakeholder needs
Analyze how different stakeholders’ needs may conflict, and potential resolution strategies
Understand how to communicate technical content to non-technical stakeholders
Analyze the alignment of a project with stakeholders’ intended goals
Identify and Analyze techno-solutionist attitudes and approaches
Analyze the potential negative impacts (e.g., unintended consequences, externalities) of technologies that are ostensibly designed for social benefit
Be aware of responsible disclosure
No learning objectives match these filters. Try removing a filter or searching for a different term.