It is no longer a hypothetical worry that artificial intelligence - more specifically, machine learning (ML) - can propagate the effects of pernicious bias in healthcare. To address these problems, some have proposed the development of 'algorithmic fairness' solutions. The primary goal of these solutions is to constrain the effect of pernicious bias with respect to a given outcome of interest as a function of one's protected identity (i.e., characteristics generally protected by civil or human rights legislation. The technical limitations of these solutions have been well-characterized. Ethically, the problematic implication - of developers, potentially, and end users - is that by virtue of algorithmic fairness solutions a model can be rendered 'objective' (i.e., free from the influence of pernicious bias). The ostensible neutrality of these solutions may unintentionally prompt new consequences for vulnerable groups by obscuring downstream problems due to the persistence of real-world bias.

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.

Cognitive informatics (CI) is the transdisciplinary enquiry of cognitive and information sciences that investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, and their engineering applications via an interdisciplinary approach. CI develops a coherent set of fundamental theories and denotational mathematics, which form the foundation for most information and knowledge based science and engineering disciplines such as computer science, cognitive science, neuropsychology, systems science, cybernetics, software engineering, knowledge engineering, and computational intelligence. This paper reviews the central doctrine of CI and its applications. The theoretical framework of CI is described on the architecture of CI and its denotational mathematic means. A set of theories and formal models of CI is presented in order to explore the natural and computational intelligence. A wide range of applications of CI are described in the areas of cognitive computers, cognitive properties of knowledge, simulations of human cognitive behaviors, cognitive complexity of software, autonomous agent systems, and computational intelligence.

There is a complex relationship between the architecture of a computer, the software it is to run, and the tasks it is designed to perform. The most difficult aspect of building a brain-like computer may not be in its construction, but in its use: How can it be programmed? What can it do well? What does it do poorly? Software development in the past has proved far more difficult and far slower than hardware development. There is no reason to expect a brain like computer to be any different. We suggest some operations a brain like computer might do well. Our system is not so much "neuromorphic" as "cognomorphic", that is, biological constraints suggest new ways to perform cognitive tasks.

Since World War II, a group of scientists has been attempting to understand the human nervous system and to build computer systems that emulate the brain's abilities. Many of the early workers in this field of neural networks came from cybernetics; others came from neuroscience, physics, electrical engineering, mathematics, psychology, even economics. In this collection of interviews, those who helped to shape the field share their childhood memories, their influences, how they became interested in neural networks, and what they see as its future. The subjects tell stories that have been told, referred to, whispered about, and imagined throughout the history of the field. Together, the interviews form a Rashomon-like web of reality. Some of the mythic people responsible for the foundations of modern brain theory and cybernetics, such as Norbert Wiener, Warren McCulloch, and Frank Rosenblatt, appear prominently in the recollections. The interviewees agree about some things and disagree about more. Together, they tell the story of how science is actually done, including the false starts, and the Darwinian struggle for jobs, resources, and reputation. Although some of the interviews contain technical material, there is no actual mathematics in the book. Contributors: James A. Anderson, Michael Arbib, Gail Carpenter, Leon Cooper, Jack Cowan, Walter Freeman, Stephen Grossberg, Robert Hecht-Neilsen, Geoffrey Hinton, Teuvo Kohonen, Bart Kosko, Jerome Lettvin, Carver Mead, David Rumelhart, Terry Sejnowski, Paul Werbos, Bernard Widrow.

We introduce a network of networks (NoN) model to solve image regularization problems. The method is motivated by the fact that natural image formation involves both local processing and globally coordinated parallel processing. Both forms are readily implemented using an NoN architecture. The modeling is very powerful in that it achieves high-quality adaptive processing, and it reduces the computational difference between inhomogeneous and homogeneous conditions. This method is able to provide fast, quality imaging in early vision, and its replicating structure and sparse connectivity readily lend themselves to hardware implementations.

Neural networks are used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy-minimizing network. The limiting process contains the state vector within a set of limits, and this model is called the brain state in a box, or BSB model, which forms energy minima (attractors in the network dynamical system) based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to tentatively identify them based on their observed parameters. As a test of this idea, a neural network was used to form a small database that could potentially make emitter identification. There were three errors of classification. The number of iterations are required to reach an attractor state was very long, and some of the final states were not fully limited. These factors indicate the uncertainty of the neural network.>

Biological support exists for the idea that large-scale models of the brain should be parallel, distributed, and associative. Some of this neurobiology is reviewed. It is then assumed that state vectors, large patterns of activity of groups of individual somewhat selective neurons, are the appropriate elementary entities to use for cognitive computation. Simple neural models using this approach are presented that will associate and will respond to prototypes of sets of related inputs. Some experimental evidence supporting the latter model is discussed. A model for categorization is then discussed. Educating the resulting systems and the use of error correcting techniques are discussed, and an example is presented to the behavior of the system when diffuse damage occurs to the memory, with and without compensatory learning. Finally, a simulation is presented which can learn partial information, integrate it with other material, and use that information to reconstruct missing information.