Genetic programming approaches to learning fair classifiers

Society has come to rely on algorithms like classifiers for important decision making, giving rise to the need for ethical guarantees such as fairness. Fairness is typically defined by asking that some statistic of a classifier be approximately equal over protected groups within a population. In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification. We propose two ideas. The first is to incorporate a fairness objective into multi-objective optimization. The second is to adapt lexicase selection to define cases dynamically over intersections of protected groups. We describe why lexicase selection is well suited to pressure models to perform well across the potentially infinitely many subgroups over which fairness is desired. We use a recent genetic programming approach to construct models on four datasets for which fairness constraints are necessary, and empirically compare performance to prior methods utilizing game-theoretic solutions. Methods are assessed based on their ability to generate trade-offs of subgroup fairness and accuracy that are Pareto optimal. The result show that genetic programming methods in general, and random search in particular, are well suited to this task.Comment: 9 pages, 7 figures. GECCO 202

Shared Neural Substrates of Perception and Memory: Testing the Assumptions and Predictions of the Representational-Hierarchical Account

Proponents of the representational-hierarchical (R-H) account claim that memory and perception rely on shared neural representations. In the ventral visual stream, posterior brain areas are assumed to represent simple information (e.g. low-level image properties), but the complexity of representations increases toward more anterior areas, such as inferior temporal cortex (e.g., object-parts, objects), extending into the medial temporal lobe (MTL; e.g. scenes). This view predicts that brain structures along this continuum serve both memory and perception; a structure’s engagement is determined by the representational demands of a task, rather than the cognitive process putatively involved. In a neuroimaging study, I searched for the transition from feature-based representations to conjunction-based representations along this pathway. In the first scan session, participants viewed two stimulus sets with different levels of complexity: fribbles (novel 3D objects) and scenes (novel, computer-generated rooms). According to the R-H account, a neural feature-code for both fribbles and scenes should reside in posterior ventral visual stream. I predicted a transition to conjunction-coding toward MTL, with the transition for the simpler stimulus set (fribbles) occurring earlier. Next, I measured memory signals while varying (1) stimulus complexity and (2) type of retrieved information (features or conjunctions). In a second scan session, participants completed a recognition memory task for fribbles and scenes, with three mnemonic classes of item. Novel items comprised novel features combined in a novel conjunction; Recombination items possessed features that had been seen in the first session, but never within the same item (i.e., familiar features, but novel conjunctions); and Familiar items comprised familiar features and familiar conjunctions. Under the R-H account, a memory task that requires only the retrieval of feature-based information should recruit visual cortex rather than MTL. Further, these “feature memory” signals should map onto feature-coding regions found in the first session. Analyses revealed that visual regions, outside of MTL, contained (1) more information about individual features than conjunctions of features (first session data), and (2) the greatest signal for feature memory (second session data). Thus, cortical regions that best represented feature information during perception also best signaled feature information in memory and were located outside MTL

An automatic part-of-speech tagger for Middle Low German

Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed at facilitating research into the highly under-researched diachronic syntax of Low German. The present paper reports on a crucial step in creating the corpus, viz. the creation of a part-of-speech tagger for Middle Low German (MLG). Having been transmitted in several non-standardised written varieties, MLG poses a challenge to standard POS taggers, which usually rely on normalized spelling. We outline the major issues faced in the creation of the tagger and present our solutions to them