The Recommendation Architecture: Lessons from Large-Scale Electronic Systems Applied to Cognition

A fundamental approach of cognitive science is to understand cognitive systems by separating them into modules. Theoretical reasons are described which force any system which learns to perform a complex combination of real time functions into a modular architecture. Constraints on the way modules divide up functionality are also described. The architecture of such systems, including biological systems, is constrained into a form called the recommendation architecture, with a primary separation between clustering and competition. Clustering is a modular hierarchy which manages the interactions between functions on the basis of detection of functionally ambiguous repetition. Change to previously detected repetitions is limited in order to maintain a meaningful, although partially ambiguous context for all modules which make use of the previously defined repetitions. Competition interprets the repetition conditions detected by clustering as a range of alternative behavioural recommendations, and uses consequence feedback to learn to select the most appropriate recommendation. The requirements imposed by functional complexity result in very specific structures and processes which resemble those of brains. The design of an implemented electronic version of the recommendation architecture is described, and it is demonstrated that the system can heuristically define its own functionality, and learn without disrupting earlier learning. The recommendation architecture is compared with a range of alternative cognitive architectural proposals, and the conclusion reached that it has substantial potential both for understanding brains and for designing systems to perform cognitive functions

Modeling Epistemological Principles for Bias Mitigation in AI Systems: An Illustration in Hiring Decisions

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase

Designing Fair Ranking Schemes

Items from a database are often ranked based on a combination of multiple criteria. A user may have the flexibility to accept combinations that weigh these criteria differently, within limits. On the other hand, this choice of weights can greatly affect the fairness of the produced ranking. In this paper, we develop a system that helps users choose criterion weights that lead to greater fairness. We consider ranking functions that compute the score of each item as a weighted sum of (numeric) attribute values, and then sort items on their score. Each ranking function can be expressed as a vector of weights, or as a point in a multi-dimensional space. For a broad range of fairness criteria, we show how to efficiently identify regions in this space that satisfy these criteria. Using this identification method, our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and, if it does not, to suggest the smallest modification that does. We develop user-controllable approximation that and indexing techniques that are applied during preprocessing, and support sub-second response times during the online phase. Our extensive experiments on real datasets demonstrate that our methods are able to find solutions that satisfy fairness criteria effectively and efficiently

Statistical Feature Combination for the Evaluation of Game Positions

This article describes an application of three well-known statistical methods in the field of game-tree search: using a large number of classified Othello positions, feature weights for evaluation functions with a game-phase-independent meaning are estimated by means of logistic regression, Fisher's linear discriminant, and the quadratic discriminant function for normally distributed features. Thereafter, the playing strengths are compared by means of tournaments between the resulting versions of a world-class Othello program. In this application, logistic regression - which is used here for the first time in the context of game playing - leads to better results than the other approaches.Comment: See http://www.jair.org/ for any accompanying file

Developmental disorders

Introduction: Connectionist models have recently provided a concrete computational platform from which to explore how different initial constraints in the cognitive system can interact with an environment to generate the behaviors we find in normal development (Elman et al., 1996; Mareschal & Thomas, 2000). In this sense, networks embody several principles inherent to Piagetian theory, the major developmental theory of the twentieth century. By extension, these models provide the opportunity to explore how shifts in these initial constraints (or boundary conditions) can result in the emergence of the abnormal behaviors we find in atypical development. Although this field is very new, connectionist models have already been put forward to explain disordered language development in Specific Language Impairment (Hoeffner & McClelland, 1993), Williams Syndrome (Thomas & Karmiloff-Smith, 1999), and developmental dyslexia (Seidenberg and colleagues, see e.g. Harm & Seidenberg, in press); to explain unusual characteristics of perceptual discrimination in autism (Cohen, 1994; Gustafsson, 1997); and to explore the emergence of disordered cortical feature maps using a neurobiologically constrained model (Oliver, Johnson, Karmiloff-Smith, & Pennington, in press). In this entry, we will examine the types of initial constraints that connectionist modelers typically build in to their models, and how variations in these constraints have been proposed as possible accounts of the causes of particular developmental disorders. In particular, we will examine the claim that these constraints are candidates for what will constitute innate knowledge. First, however, we need to consider a current debate concerning whether developmental disorders are a useful tool to explore the (possibly innate) structure of the normal cognitive system. We will find that connectionist approaches are much more consistent with one side of this debate than the other