Erasure of Unaligned Attributes from Neural Representations

Erasure of Unaligned Attributes from Neural Representations Shun Shao∗ Yftah Ziser∗ Shay B. Cohen Institute for Language, Cognition and Computation School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh, EH8 9AB, UK s.shao-11@inf.ed.ac.uk yftah.ziser@inf.ed.ac.uk scohen@inf.ed.ac.uk Abstract We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algo- rithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input rep- resentations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of data- sets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset, and the BiasBench benchmark. The latter bench- mark includes four datasets with various types of protected attributes. Our results demon- strate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.1 1 Introduction Developing a methodology for adjusting neural representations to preserve user privacy and avoid encoding bias in them has been an active area of research in recent years. Previous work shows it is possible to erase undesired information from rep- resentations so that downstream classifiers can- not use that information in their decision-making process. This previous work assumes that this sensitive information (or guarded attributes, such as gender or race) is available for each input in- stance. These guarded attributes, however, are sen- sitive, and obtaining them on a large scale is often challenging and, in some cases, not feasible (Han et al., 2021b). For example, Blodgett et al. (2016) ∗Equal contribution. 1Our code is available at https://github.com /jasonshaoshun/AMSAL. studied the characteristics of African-American English on Twitter, and could not couple the ethnicity attribute directly with the tweets they collected due to the attribute’s sensitivity. This paper introduces a novel debiasing setting in which the guarded attributes are not paired up with each input instance and an algorithm to remove information from representations in that setting. In our setting, we assume that each neural input representation is coupled with a guarded at- tribute value, but this assignment is unavailable. In cases where the domain of the guarded attri- bute is small (for example, with binary attributes), this means that the guarded attribute informa- tion consists of priors with respect to the whole population and not instance-level information. The intuition behind our algorithm is that if we were to find a strong correlation between the input variable and a set of guarded grounded attributes either in the form of an unordered list of records or as priors, then it is unlikely to be coincidental if the sample size is sufficiently large (§3.5). We implement this intuition by jointly finding pro- jections of the input samples and …

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