Racial and Ethnic Biases in Computational Approaches to Psychopathology

Hitczenko, Kasia; Cowan, Henry R.; Goldrick, Matthew; Mittal, Vijay A.

doi:10.1093/schbul/sbab131

Cited by 28 publications

(17 citation statements)

References 47 publications

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“…transcript length and punctuation) can affect the various coherence measures. This is in line with previous previous studies (Elvevåg et al, 2007; Iter et al, 2018) and with recent evidence showing how some of these features (sentence length) can interact with socio-demographic characteristics and generate bias (Hitczenko et al, 2022). We thus advocate for larger theory-driven validation studies incorporating socio-demographic, linguistic and clinical variability, in order to consistently identify and statistically account for sources of variation in the decreased semantic coherence.…”

Section: Discussionsupporting

confidence: 93%

Section: Rating Scales Sans -Globalsupporting

confidence: 93%

“…Indeed, recent studies seem to suggest that NLP algorithms, and coherence measures in particular, can be biased by socio-demographic variables such as racial identity. For instance, Hitczenko et al (2022) found the NLP algorithms rated entirely asymptomatic Black American participants as having language patterns consistent with thought disorder, thus leading to biased predictions. Because not all previous studies systematically adjusted their estimates for all key socio-demographic variables, we could only speculate that they affected previous findings.…”

Section: Discussionmentioning

confidence: 99%

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Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

Parola

Lin

Simonsen

et al. 2022

Preprint

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Introduction: Language disorders - disorganized and incoherent speech in particular - are distinctive features of schizophrenia. Natural language processing (NLP) offers automated measures of incoherent speech as promising markers for schizophrenia. However, the scientific and clinical impact of NLP markers depends on their generalizability across contexts, samples, and languages, which we systematically assessed in the present study relying on a large, novel, cross-linguistic corpus. Methods: We collected a Danish (DK), German (GE), and Chinese (CH) cross-linguistic dataset involving transcripts from 187 participants with schizophrenia (111DK, 25GE, 51CH) and 200 matched controls (129DK, 29GE, 42CH) performing the Animated Triangle task. Fourteen previously published NLP coherence measures were calculated, and between-groups differences and association with symptoms were tested for cross-linguistic generalizability. Results: One coherence measure robustly generalized across samples and languages. We found several language-specific effects, some of which partially replicated previous findings (lower coherence in German and Chinese patients), while others did not (higher coherence in Danish patients). We found several associations between symptoms and measures of coherence, but the effects were generally inconsistent across languages and rating scales. Conclusions: Using a cumulative approach, we have shown that NLP findings of reduced semantic coherence in schizophrenia have limited generalizability across different languages, samples, and measures. We argue that several factors such as sociodemographic and clinical heterogeneity, cross-linguistic variation, and the different NLP measures reflecting different clinical aspects may be responsible for this variability. Future studies should take this variability into account in order to develop effective clinical applications targeting different patient populations.

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Section: Discussionsupporting

confidence: 93%

Section: Rating Scales Sans -Globalsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

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Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

Parola

Lin

Simonsen

et al. 2022

Preprint

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“…Promising progress has been made in normative modeling 62,63 , which relies on large samples to provide expectations, accounting for clinical and socio-demographic features, and assess individual deviations from such expectations. This approach is particularly relevant in light of recent evidence showing how computational speech analysis may be prone to serious bias determined by socio-demographic factors, such as racial identity 64 , as it may help to identify such potential bias. An alternative approach is to collect larger samples and use propensity scores 65,66 to better match patients and controls and account for potential confounders.…”

Section: Discussionmentioning

confidence: 99%

Voice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investigation

Parola

Simonsen

Lin

et al. 2022

Preprint

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Background and HypothesisVoice atypicalities are potential markers of clinical features of schizophrenia (e.g., negative symptoms). A recent meta-analysis identified an acoustic profile associated with schizophrenia (reduced pitch variability and increased pauses), but also highlighted shortcomings in the field: small sample sizes, little attention to the heterogeneity of the disorder, and to generalizing findings to diverse samples and languages.Study DesignWe provide a critical cumulative approach to vocal atypicalities in schizophrenia, where we conceptually and statistically build on previous studies. We aim at identifying a cross-linguistically reliable acoustic profile of schizophrenia and assessing sources of heterogeneity (symptomatology, pharmacotherapy, clinical and social characteristics). We relied on previous meta-analysis to build and analyze a large cross-linguistic dataset of audio recordings of 231 patients with schizophrenia and 238 matched controls (>4.000 recordings in Danish, German, Mandarin and Japanese). We used multilevel Bayesian modeling contrasting meta-analytically informed and skeptical inferences.Study ResultsWe found only a minimal generalizable acoustic profile of schizophrenia (reduced pitch variability), while duration atypicalities replicated only in some languages. We identified reliable associations between acoustic profile and individual differences in clinical ratings of negative symptoms, medication, age and gender. However, these associations vary across languages.ConclusionsThe findings indicate that a strong cross-linguistically reliable acoustic profile of schizophrenia is unlikely. Rather, if we are to devise effective clinical applications able to target different ranges of patients, we need first to establish larger and more diverse cross-linguistic datasets, focus on individual differences, and build self-critical cumulative approaches.

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“…The implication is that it might be possible for a neural network to use ethnicity (or other protected characteristics) as a predictive factor in contexts where physicians would not consider it or even be aware of it. Especially if training data reflect a historic tendency to over-diagnose an ethnic minority, 7 it seems possible that the algorithm might use ethnicity as a shortcut to racially biased assessments. Due to current limitations in AI interpretability, it will probably be difficult to dissect algorithmic inferences in NLP-based speech analysis models and provide a complete account of the noises they hear.…”

Section: Can Algorithmic Inferences From Speech Be Accounted For?mentioning

confidence: 99%

What’s That Noise? Interpreting Algorithmic Interpretation of Human Speech as a Legal and Ethical Challenge

Hauglid

2022

Schizophrenia Bulletin

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Racial and Ethnic Biases in Computational Approaches to Psychopathology

Cited by 28 publications

References 47 publications

Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

Speech disturbances in schizophrenia: assessing cross-linguistic generalizability of NLP automated measures of coherence

Voice patterns as markers of schizophrenia: building a cumulative generalizable approach via a cross-linguistic and meta-analysis based investigation

What’s That Noise? Interpreting Algorithmic Interpretation of Human Speech as a Legal and Ethical Challenge

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