Section outline

  • LESSON 7 - ARTIFICIAL INTELLIGENCE FOUNDATIONS FOR BROADENING CULTURAL HORIZONS

    Biases and Ambiguity

    In this lesson, we analyze the different types of biases that can emerge in artificial intelligence systems (social, emergent, and automation bias) and how they influence the responses generated. Through concrete examples, we observe how models can reinforce stereotypes, such as associating certain professions with income levels or linking "intelligence" exclusively to developed nations. These cases demonstrate that model responses are not neutral, but rather reflect cultural stereotpyes and hierarchies present in their training data.

    We also examine the concept of uncertainty, distinguishing between ambiguous questions and scenarios where the model lacks sufficient knowledge to answer with certainty. When AI lacks awareness of local cultural contexts or regional knowledge, it tends to fill those gaps with generic or stereotyped responses. Therefore, recognizing the limitations of these systems and the importance of being able to state "I don't know" is essential for critical and responsible application in educational settings.

    At the end of the lesson, you will be asked to answer a few questions. The answers will be graded, and completing this activity is required to finish and earn credit for the course.

    👨‍🏫 Instructor: Guido Ivetta

    • Biases and Ambiguity URL

      Prefer to watch in a language other than Spanish? Just turn on CC, go to Settings (⚙️) > Subtitles > Auto-translate, and select your language!

    • Below you will find the lesson slides, designed as a reference to revisit the ideas, questions, and tensions raised in the video. (Please note: The slides are in Spanish).

      📚 Key Concepts

      Denotational Ambiguity: Occurs when an instruction or question is not specific enough (e.g., "Who won the World Cup?" without specifying the year or sport).

      Epistemic Ambiguity: Occurs when, even if the question is clear, the answer is unknown, controversial, or there is a lack of information within the model.

      Disambiguation by Bias: An AI behavior where, when faced with uncertainty, the model "fills in" missing information using prejudices or stereotypes (e.g., assuming a "doctor" is always male).

      Intersectionality: A phenomenon where multiple types of bias intersect (e.g., gender, race, and social class) in the same scenario, amplifying discrimination.

      Automation Bias: The human tendency to blindly trust the outputs of a technological system, assuming it is infallible without verifying the information.

      Emergent Bias: Occurs when a system is trained in one context (e.g., Western society) and deployed in a vastly different one (e.g., Asia), causing errors due to a lack of representativeness.

      Social Bias: An unfair inclination or prejudice for or against a person or group, reflecting human stereotypes present in the training data.

      A creation by Fundación Vía Libre in collaboration with FAMAF – UNC.

    • Assignment: Biases and Ambiguity in EDIA

      Below you will find a video to help you carry out this activity.

      If you don't remember how to log in to the tool, you can rewatch the video from the "Typical Phrases" activity.

      Access the EDIA tool to start the activity: https://edia.ngrok.app/

      Ambiguities Activity: Let's get started!

      Below we explain how to perform the activity. We have also included additional written material explaining it step by step.