Laura Laugwitz, M. Sc. Digital Media & Technology Explaining Machine Learning Models to Establish Validity in Automated Content Analysis 

Standardized Content Analysis + Supervised Text Classification = Automated Content Analysis 2 Explaining ML Models to Establish Validity in ACA 

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 

Standardized Content Analysis + Supervised Text Classification = Automated Content Analysis 4 Explaining ML Models to Establish Validity in ACA 

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 

Approaches to Explainability 6 Explaining ML Models to Establish Validity in ACA Output Input model agnostic Output Input model specific Output Input interpretable 

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 

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 

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 

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 

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 

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 

Conclusion • Faithfulness to the model vs. sense-making • Collaboration on interpretable and text-focussed methods 16 Explaining ML Models to Establish Validity in ACA 

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