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20809xxx Introductory AI and Data Ethics
Course Information
Description
An introduction to artificial intelligence (AI) and ethical and policy issues related to AI, analytics, "big data," and the use of algorithms to support decision-making which impacts human lives. Explore moral and ethical theories such as consequentialism, deontology, and virtue ethics. Examine technical concepts such as the bias/variance tradeoff and inductive risk. Analyze the impact of automation on society and the use of algorithms, such as in medicine and policing. Assess major debates and controversies in a variety of contexts.
Total Credits
3

Course Competencies (Course Outcomes)
  1. Differentiate between AI, analytics, and big data
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Distinguish the difference between narrow and general AI
    Contrast subsets of AI, including machine learning, natural language processing, and generative AI
    Describe the ethical issues pertaining to analytics, such as privacy, transparency, and bias
    Examine the uses of big data in medicine, criminal justice, education, and other cases of predictive analytics

  2. Describe the use and impact of algorithms in human decision making
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Articulate the machine learning loop that occurs in predictive algorithms
    Describe the role bias may play in the design of algorithms
    Describe the systemic impact of algorithms and their influence on individual autonomy
    Determine when and how one should exercise caution when using these tools

  3. Apply moral reasoning and ethical concepts
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Describe the morally relevant factors
    Assess which ethical theory provides the best guidance and explanatory value in AI
    Assess the risks of predictive algorithms on human autonomy

  4. Describe technical concepts, such as bias and fairness metrics
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Explore a variety of fairness metrics, such as independence, predictive parity, and error rate balance
    Compare the benefits and risks of each fairness metric
    Assess how bias and values inform inductive risk
    Investigate how base rate variance in datasets leads to certain trade-offs

  5. Evaluate the impact automation has on society and the meaning of life
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Rationalize the use of automation in varying fields
    Question how AI might sever the link between agent and creative output
    Determine when human intervention and regulation is needed in an automated society
    Describe AI’s disruptive and productive role in society

  6. Assess arguments, exposing assumptions and responding to objections
    Assessment Strategies
    Written Product, Case Study Response, and/or Project
    Criteria
    Clarify terms in an argument and determine what kind of argument it is
    Find the conclusion and its supporting premises
    Formalize the argument
    Determine if the logic or truth condition is satisfied
    Recognize if the argument is based on unstated assumptions
    Describe the role of unstated assumptions

  7. Correlate philosophical questions with contemporary culture and your own lives
    Assessment Strategies
    Written Product and/or Project
    Criteria
    Describe how philosophical questions and moral issues are reflected in media and culture
    Apply ethical theories and their guiding principles
    Ethical topic is effectively presented to a large non-academic audience

  8. Think critically about arguments made by others with interpretative charity and intellectual humility
    Assessment Strategies
    Written Product and/or Presentation
    Criteria
    Avoid fallacies such as straw man, begging the question, ad hominem, hasty generalizations, and appeal to authority
    Practice charity in assessing opposing arguments
    Formulate objections or critical questions to an argument
    Predict how others will respond to your argument and respond to objections

  9. Write an argumentative paper
    Assessment Strategies
    Paper
    Criteria
    Integrate a citation style, such as MLA, APA, or Chicago
    Reconstruct the main arguments or claims
    Explain the reasoning behind each premise of the argument
    Create one objection to one of its premises
    Anticipate possible objections
    Evaluate possible objections
    Paper is no more than 1200 words
    Integrate a citation style, such as MLA, APA, or Chicago

  10. Cultivate a critique strategy
    Assessment Strategies
    Written and/or Verbal Critique
    Criteria
    Demonstrate active listening
    Use industry-accepted AI terminology
    Verbal and/or written feedback is interpreted by others effectively
    Make decisions about others’ work
    Responses to critiques follow guidelines specified by the instructor
    Revisions are implemented, adapted, or other