Exploring Algorithmic Fairness in Deep Speaker Verification

To allow individuals to complete voice-based tasks (e.g., send messages or make payments), modern automated systems are required to match the speaker’s voice to a unique digital identity representation for verification. Despite the increasing accuracy achieved so far, it still remains under-explored how the decisions made by such systems may be influenced by the inherent characteristics of the individual under consideration. In this paper, we investigate how state-of-the-art speaker verification models are susceptible to unfairness towards legally-protected classes of individuals, characterized by a common sensitive attribute (i.e., gender, age, language). To this end, we first arranged a voice dataset, with the aim of including and identifying various demographic classes. Then, we conducted a performance analysis at different levels, from equal error rates to verification score distributions. Experiments show that individuals belonging to certain demographic groups systematically experience higher error rates, highlighting the need of fairer speaker recognition models and, by extension, of proper evaluation frameworks

An Empirical Investigation of the Right to Explanation Under GDPR in Insurance

The GDPR aims at strengthening the rights of data subjects and to build trust in the digital single market. This is manifested by the introduction of a new principle of transparency. It is, however, not obvious what this means in practice: What kind of answers can be expected to GDPR requests citing the right to “meaningful information”? This is the question addressed in this article. Seven insurance companies, representing 90–95% of the Swedish home insurance market, were asked by consumers to disclose information about how premiums are set. Results are presented first giving descriptive statistics, then characterizing the pricing information given, and lastly describing the procedural information offered by insurers as part of their answers. Overall, several different approaches to answering the request can be discerned, including different uses of examples, lists, descriptions of logic, legal basis as well as data related to the process of answering the requests. Results are analyzed in light of GDPR requirements. A number of potential improvements are identified—at least three responses are likely to fail the undue delay requirement. The article is concluded with a discussion about future work

Remote explainability faces the bouncer problem

International audienceThe concept of explainability is envisioned to satisfy society’s demands for transparency about machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. Although this approach is promising in a local context (for example, the model creator explains it during debugging at the time of training), we argue that this reasoning cannot simply be transposed to a remote context, where a model trained by a service provider is only accessible to a user through a network and its application programming interface. This is problematic, as it constitutes precisely the target use case requiring transparency from a societal perspective. Through an analogy with a club bouncer (who may provide untruthful explanations upon customer rejection), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we observe the impossibility of remote explainability for single explanations by constructing an attack on explanations that hides discriminatory features from the querying user. We provide an example implementation of this attack. We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote explainability in general