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Closing keynote: The Miseducation of This Machine While machines continue to learn with more sophisticated algorithms and larger amounts of data, humans need to understand how such learning works in order to take its results with the proper grain of salt. Let's make ML tangible and thus help you become a better machine teacher! Laura Linda Laugwitz - https://twitter.com/lauralindal EuRuKo 2019
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### Summary of the Keynote: The Miseducation of This Machine by Laura Linda Laugwitz In her closing keynote at EuRuKo 2019, Laura Linda Laugwitz explores the intricacies of machine learning (ML) and its relationship with human knowledge and biases. She uses the title "The Miseducation of This Machine" to draw parallels between Lauryn Hill's influential album and the current challenges of understanding and teaching machines. **Key Points:** - **Historical Context and Framework:** Laugwitz begins by referencing the roots of education's impact on knowledge production, emphasizing the importance of understanding who educates and what truths are produced. This sets the stage for discussing how both humans and machines learn. - **Defining Learning:** Laugwitz defines learning in three steps: reproduction, remixing, and reflection. - **Reproduction:** In machine learning, this involves the ability to replicate existing knowledge without understanding context, leading to biases, as seen in examples like Amazon's hiring tool. - **Remixing:** This step pertains to the ability of algorithms to create new outputs from existing data, illustrating how machines can misinterpret data if not programmed correctly. - **Reflection:** Highlighting the need for human involvement, she stresses that reflection is necessary to understand the limitations and implications of knowledge produced by machines. - **Challenges in Machine Learning:** - Laugwitz exemplifies the pitfalls of ML through humorous narratives and serious cases of biased algorithm outcomes. She shares an example where a music recommendation system misled a user's preferences based on skewed data. - The complexity of tasks like identifying hate speech is discussed, showing that ML lacks the nuanced understanding required to interpret language contextualually. - **Research Insights:** Laugwitz shares insights from her research on hate speech analysis, indicating how ML classifiers often fail to recognize misleading data patterns and require human oversight for effective outcomes. - The methodology blends communication science and computer science, emphasizing the need for definitional clarity in detecting hate speech. - **Human's Role in AI Development:** Throughout her talk, Laugwitz advocates for a reflective approach when engaging with machine learning tasks, urging programmers and users to be vigilant about the biases and limitations of their systems. In her conclusion, Laugwitz asserts that the knowledge produced by machine learning is reflective of the data it is trained on, prompting a call for responsibility among those who design and utilize such systems. A closing quote from Lauryn Hill encapsulates the central message regarding awareness and accountability in the realm of machine learning. ### Important Takeaways: - Understanding machine learning requires appreciating the roles of data, algorithms, and human reflection. - There is a critical need for human oversight to mitigate biases and improve machine understanding. - As machines learn from human-generated data, knowing the source and context of that data is vital for ethical and effective AI development.
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