In education, the real-life implications of implicit biases can create invisible barriers to opportunity and achievement for some students—a stark contrast to the values and intentions of educators and administrators who dedicate their professional lives to their students’ success.

Cheryl Staats – Understanding Implicit Bias: What Educators Should Know

In view of technology, especially data analytics and AI, playing a much larger role in academic life, instructors must consider how their technical skills affect their students.

Data generated by students in the LMS will follow them for the rest of their academic career. Using the LMS in unintended ways can lead to seeding analytics that are not representative of students’ academic history or capacity. Consider scenarios where students could be misidentified. For example, if faculty collect assignments outside of the LMS (e.g., via email or face to face), the due dates in the LMS gradebook may mistakenly record late submissions. Incorporating course design training, standards, and preferred practices helps avoid these hazards (ex: Blackboard ECP or Quality Matters). So, continue to improve learning management system (LMS) acumen and use. 

Support data democratization, including data literacy, data accessibility, and data science adoption.

  • Data literacy: Stakeholders—faculty, administrators, and students—should learn how to read data and data visualizations and should understand the capacity for bias in data. Depending on the audience, some data education can be formal, such as the Blackboard Academy course “Getting Started with Learning Analytics.” Other approaches can be very informal, such as promoting the popular Netflix documentary Coded Bias or HBO’s documentary Persona.
  • Data accessibility: Data should be available to stakeholders for their own analysis and validation. Institutions should deploy self-service analytics tools with data manipulations and visualizations that allow divergent ideas to emerge. Additionally, web accessibility must be a priority.
  • Data science adoption: Stakeholder data scientists should be supported through institutional data governance and data security policies. Making student success analytics data available to people who are outside of the administrative inner circle is a core concept of data democratization. Doing so brings people from different areas of the institution, with their varied perspectives, into the data science and analytics world.

Learn more about different types of biases and consider how they might manifest when using AI/ML to inform student success initiatives. AI/ML biases include sample, prejudice, measurement, and algorithmic bias.

  • Sample bias: Using a training data sample that is not representative of the population
  • Prejudice bias: Using training data that are influenced by cultural or other stereotypes
  • Measurement bias: Using training data that are distorted by the way in which they were collected
  • Algorithmic bias: Using training models that are either too rigid or too sensitive to “noise” in the data (i.e., data that distract from significant or meaningful factors)

Cognitive biases include survivorship and confirmation bias.

  • Survivorship bias: Focusing on data that are available versus purposefully collecting data while considering data that may be missing
  • Confirmation bias: Beginning with an idea and searching for data to support it, often omitting contradictory data

Addressing issues of bias in emerging AI and ML technologies that are being integrated with student success analytics may seem overwhelming. There is much for all of us to learn about the technologies and the social implications of these innovations in higher education. But ignoring that challenge or passing the responsibility off to others is not a solution. AI and ML technologies are not going away—they are only advancing in their capabilities as the sea of data expands. If we do not create our own knowledge and literacy around the use of data in AI and ML, the advancing technologies will, by default, drive our practices rather than inform them. Let us be the ones to steer data practices and policies toward our vision for student success analytics and to create the future we want to see for our students and our higher education institutions.

This is an excerpt from the full article in Educause.