Segment

Materials

Introduction (10 minutes)

  • Machine learning is a form of artificial intelligence (AI) which allows software to learn from data where we instruct the software how to learn.
  • It can be used to identify patterns and solve problems.
  • Many leading companies (Apple, Google, Microsoft, etc.) are utilizing it.
  • Machine learning is slowly becoming more integrated in our daily lives.
  • Search engines, personalization, recognition, autocorrection, etc.
  • Supervised learning
  • Use of labeled datasets to train algorithms that to classify data or predict outputs accurately.
  • Training time (giving the model inputs and correct outputs) vs Test time (model generates outputs given inputs)

Presenters

Activity – Rapid Resume Reports (10 minutes)

  1. Each student will get a set of resumes and their corresponding resume screening report scores.
  2. They will spend 5 minutes reading through the resume reports and filling out the observation sheet.
  3. Then, they will spend a few minutes discussing with a peer on their observations.
  4. Have volunteers share what they wrote and any interesting observations they had.

Participants

Reflection (5 minutes)

  • What are some things you noticed about the activity we just did?
  • These inaccuracies were the result of biases in machine learning.
  • Humans have developed many biases in their time; simple biases include colors or foods, but complex biases can form towards personal identities.
  • Computers are not impartial; Our own biases translate into bias expressed by technology
  • There are many main types of machine learning biases, here are a few:
  • Algorithmic: Bias within the algorithm processing data itself.
  • Implicit: Biases within the individuals creating the algorithm.
  • Sample: Bias within the sample of data which is being processed.
  • Reporting: Inaccurate sample size in comparison to real-world.
  • Social: Individual norms leading to biased perceptions of data.

Presenters

Connector (5 minutes)

  • Examples
  • Resume screening bias against women in Amazon algorithm
  • Overconfidence in these algorithms can cause barriers in the workforce and in future job prospects
  • Gender bias in previous versions of Google Translate between languages with no gendered pronouns to a language with gendered pronouns (eg. Turkish to English)
  • Facial recognition bias against darker skin tones
  • A high profile incident involved a chatbot named “Tay”, released on Twitter by Microsoft in 2016. Designed to mimic a “19-year-old American girl” by learning from interactions with Twitter users, it was suspended after 16 hours since it started spewing hate speech (Twitter users taught Tay such speech).
  • Machines learn what we teach them, even if it is vulgar.
  • How can we mitigate biases in machine learning?
  • Have equitable representation in supervised learning.
  • Assess for biases in unsupervised learning.
  • Have more representation/diversity within the AI field and in datasets.

Presenters

Closing Activity – Generative Image AI (10 minutes)

  • Students open generative image AI tool:
  • They will choose one or two of the given prompts and fill out the observation sheet.
  • They can also come up with their own prompts to input into the image generator.
  • Have volunteers share what they saw and any interesting observations they had.

Participants