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Explaining Machine Learning to Establish Validity in Automated Content Analysis

Explaining Machine Learning to Establish Validity in Automated Content Analysis

Due to growing amounts of data, accessible tutorials, and evolving computing capacities (Atteveldt and Peng, 2018), content analysis of text in particular is increasingly supported by machine learning methods (Boyd and Crawford, 2012; Trilling and Jonkman, 2018). In merging communication studies’ manual content analysis and machine learning’s supervised text classification, a model is trained to reproduce the labeling of concepts developed for and coded within a manually created corpus (Scharkow, 2012; Boumans and Trilling, 2016). While multiple strategies to measure reliability for automated content analysis (Scharkow, 2012; Krippendorff, 2019) and to increase reproducibility (Pineau, 2020; Mitchell et al., 2019) have recently been suggested, testing the validity of machine learning models is yet to be expanded. Some scholars suggest applying their model to a second dataset in order to test validity of inference (Pilny et al., 2019). Other scholars attempt to examine content validity by examining weights for individual features (Stoll et al., 2020). Based on systemic literature reviews, we have identified five model-agnostic methods, six methods specific to neural networks and three interpretable models which aim at explaining models for supervised text classification. We examine each solution with respect to how they may be leveraged to establish content validity in automated content analysis. We find that interpretable models show the most promise, yet are underrepresented in explainability research. Thus, we encourage communication researchers and machine learning experts to further collaborate on the development of such methods.

Presented at the 71st Annual Conference of the International Communication Association for the Computational Methods Division

Image on the final slide is user under a Creative Commons License from https://www.wocintechchat.com/

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Laura Laugwitz

May 27, 2021
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  1. Laura Laugwitz, M. Sc. Digital Media & Technology Explaining Machine

    Learning Models to Establish Validity in Automated Content Analysis
  2. Standardized Content Analysis + Supervised Text Classification = Automated Content

    Analysis 2 Explaining ML Models to Establish Validity in ACA
  3. Epistemological Differences in Communication & ML Critical Rationalism (Popper 2014)

    • Testing hypothesis’ acceptability a priori through logic • Testing hypothesis a posteriori through empirical measurement  quality = reliable, valid and intersubjectively comprehensible knowledge Technocratic Paradigm (Eden 2007) • a priori knowledge about the behavior of a program • Gaining a posteriori knowledge through testing  quality = a satisfactory, reusable application 3 Explaining ML Models to Establish Validity in ACA
  4. Standardized Content Analysis + Supervised Text Classification = Automated Content

    Analysis 4 Explaining ML Models to Establish Validity in ACA
  5. Validity Defined „A measuring instrument is considered valid if it

    measures what its user claims it measures.“ (Krippendorff, 2019: 361) Explaining ML Models to Establish Validity in ACA 5
  6. Approaches to Explainability 6 Explaining ML Models to Establish Validity

    in ACA Output Input model agnostic Output Input model specific Output Input interpretable
  7. Model-agnostic Explainability Methods • PALM (Krishnan and Wu, 2017) •

    LIME (Ribeiro et al., 2016b) • Anchors (Ribeiro et al., 2018) • MES (Turner, 2016) • Shapley values (Chen et al., 2019) 8 Explaining ML Models to Establish Validity in ACA
  8. Model-agnostic Explainability Methods • PALM (Krishnan and Wu, 2017) •

    LIME (Ribeiro et al., 2016) • Anchors (Ribeiro et al., 2018) • MES (Turner, 2016) • Shapley values (Chen et al., 2019) 10 Explaining ML Models to Establish Validity in ACA
  9. Model-specific Explainability Methods • Rationales (Lei et al., 2016) •

    DeepLIFT (Shrikumar et al., 2017) • Integrated gradients (Sundararajan et al., 2017) • VisBERT (Aken et al., 2020) • Distillation (Hinton et al., 2015) • TCAV (Kim et al., 2018) 11 Explaining ML Models to Establish Validity in ACA
  10. Model-specific Explainability Methods • Rationales (Lei et al., 2016) •

    DeepLIFT (Shrikumar et al., 2017) • Integrated gradients (Sundararajan et al., 2017) • VisBERT (Aken et al., 2020) • Distillation (Hinton et al., 2015) • TCAV (Kim et al., 2018) 12 Explaining ML Models to Establish Validity in ACA
  11. Interpretable Methods • Naive Bayes (e.g. Stoll et al., 2020)

    • Prototypes (Bien and Tibshirani, 2011) • BCM (Kim et al., 2015) 14 Explaining ML Models to Establish Validity in ACA
  12. Using Explainability Methods for Validity Checks? 15 Explaining ML Models

    to Establish Validity in ACA Output Input model agnostic Output Input model specific Output Input interpretable
  13. Conclusion • Faithfulness to the model vs. sense-making • Collaboration

    on interpretable and text-focussed methods 16 Explaining ML Models to Establish Validity in ACA
  14. Bibliography Aken, Betty van, Benjamin Winter, Alexander Löser, and Felix

    A Gers (2020). „VisBERT:Hidden-State Visualizations for Transformers“. In:Companion Proceedings of the WebConference 2020. New York, NY, USA: ACM, pp. 207–211. Bien, Jacob and Robert Tibshirani (2011). „Prototype selection for interpretable classifica-tion“. In:Annals of Applied Statistics5.4, pp. 2403–2424. Chen, Jianbo, Le Song, Martin J. Wainwright, and Michael I. Jordan (2019). „L-Shapleyand C-Shapley: Efficient model interpretation for structured data“. In:7th InternationalConference on Learning Representations, ICLR 2019. New Orleans, United States, pp. 1–17. Eden, Amnon H (2007). „Three Paradigms of Computer Science“. In:Minds and Machines17.2, pp. 135–167. Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean (2015). „Distilling the Knowledge in a NeuralNetwork“. In:NIPS 2014 Deep Learning Workshop, pp. 1–9. arXiv:1503.02531. Kim, Been, Cynthia Rudin, and Julie Shah (2015). „The Bayesian Case Model: A GenerativeApproach for Case-Based Reasoning and Prototype Classification“. In:Advances in NeuralInformation Processing Systems3.January, pp. 1952–1960. Kim, Been, Martin Wattenberg, Justin Gilmer, et al. (2018). „Interpretability beyond fea-ture attribution: Quantitative Testing with Concept Activation Vectors (TCAV)“. In:35thInternational Conference on Machine Learning, ICML 20186, pp. 4186–4195. Krippendorff, Klaus (2019). Content analysis: An introduction to its methodology (4. Ed.) SAGE Publications Ltd. 27.05.2021 17 Explaining ML Models to Establish Validity in ACA
  15. Bibliography Krishnan, Sanjay and Eugene Wu (2017). „PALM“. In:Proceedings of

    the 2nd Workshop onHuman-In-the-Loop Data Analytics - HILDA’17. Vol. s3-I. 12. New York, USA: ACM Press,pp. 1–6. Lei, Tao, Regina Barzilay, and Tommi Jaakkola (2016). „Rationalizing Neural Predictions“.In:Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing.Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 107–117. Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin (2016a). „Model-Agnostic Inter-pretability of Machine Learning“. In:2016 ICML Workshop on Human Interpretability inMachine Learning Learning (WHI 2016). New York, NY, USA, pp. 91–95. Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin (2018). „Anchors: High-precision model-agnostic explanations“. In:32nd AAAI Conferenceon Artificial Intelligence, AAAI 2018, pp. 1527– 1535. Shrikumar, Avanti, Peyton Greenside, and Anshul Kundaje (2017). „Learning importantfeatures through propagating activation differences“. In:34th International Conference onMachine Learning, ICML 2017. Vol. 7. Sydney, Australia, pp. 4844–4866. Stoll, Anke, Marc Ziegele, and Oliver Quiring (2020). „Detecting Impoliteness and Inci-vility in Online Discussions: Classification Approaches for German User Comments“. In:Computational Communication Research2.1, pp. 109–134. Sundararajan, Mukund, Ankur Taly, and Qiqi Yan (2017). „Axiomatic attribution for deepnetworks“. In:34th International Conference on Machine Learning, ICML 20177, pp. 5109–5118. Turner, Ryan (2016). „A Model Explanation System: Latest Updates and Extensions“. In:ICML Workshop on Human Interpretability in Machine Learning (WHI 2016). New York,NY, USA, pp. 1–5. 27.05.2021 18 Explaining ML Models to Establish Validity in ACA