Finding New Rules for Incomplete Theories: Induction with Explicit Biases in Varying Contexts

Many AI problem solvers possess explicitly encoded knowledge - a domain theory ““ that they use to solve problems. If these problem solvers are to be autonomous, they must be able to detect and to fill gaps in their own knowledge. The field of machine learning addresses this issue. Recently two disparate machine learning approaches have emerged as predominant in the field: explanation-based learning (EBL) and similarity-based learning (SBL), EBL and SBL have been applied to problems in a variety of domains. Both methods have clear problems, however, EBL assumes that a system is given an explicit theory of the domain that is complete, correct, and tractable. These assumptions are clearly unrealistic for most complex, real-world problems. SBL suffers because of its lack of an explicit theory of the domain. The simplicity of the method requires that human intervention playa large role in tailoring input examples and the features describing them in such a way as to allow a system to choose an appropriate set of features to define a concept. Biasing a system in this way may result in its being unable to discover all concepts in even a Single domain. Less tailoring of the examples leaves a system open to the possibility of not converging on the best definition for a concept, or any at all, due to the computational complexity. The research described in this proposal addresses a number of the problems found in explanation-based and similarity-based learning. The major focus of the research is the elimination of the assumption that the domain theory of an EBL system is complete. In particular, it considers the problem of working with an incomplete theory by suggesting a method by which gaps in an EBL system's knowledge can be detected and filled. We suggest that when EBL cannot derive a complete explanation, the partial explanation focus a context in which learning takes place. Information extracted from partial explanations, as well as from complete explanations, can be exploited by SBL to do better induction of the missing domain knowledge. The extracted information constitutes an explicit bias for similarity-based learning. A second problem to be addressed is that of making the biases of SBL explicit. Finally, all testing of the claims made in this proposal is to be done in the Gemini learning system. The development of the system addresses the goal of constructing an integrated learning architecture utilizing both EBL and SBL

Subjective Moral Biases & Fallacies: Developing Scientifically & Practically Adequate Moral Analogues of Cognitive Heuristics & Biases

In this dissertation, I construct scientifically and practically adequate moral analogs of cognitive heuristics and biases. Cognitive heuristics are reasoning “shortcuts” that are efficient but flawed. Such flaws yield systematic judgment errors—i.e., cognitive biases. For example, the availability heuristic infers an event’s probability by seeing how easy it is to recall similar events. Since dramatic events, such as airplane crashes, are disproportionately easy to recall, this heuristic explains systematic overestimations of their probability (availability bias). The research program on cognitive heuristics and biases (e.g., Daniel Kahneman’s work) has been scientifically successful and has yielded useful error-prevention techniques—i.e., cognitive debiasing. I attempt to apply this framework to moral reasoning to yield moral heuristics and biases. For instance, a moral bias of unjustified differences in the treatment of particular animal species might be partially explained by a moral heuristic that dubiously infers animals’ moral status from their aesthetic features. While the basis for identifying judgments as cognitive errors is often unassailable (e.g., per violating laws of logic), identifying moral errors seemingly requires appealing to moral truth, which, I argue, is problematic within science. Such appeals can be avoided by repackaging moral theories as mere “standards-of-interest” (a la non-normative metrics of purportedly right-making features/properties). However, standards-of-interest do not provide authority, which is needed for effective debiasing. Nevertheless, since each person deems their own subjective morality authoritative, subjective morality (qua standard-of-interest and not moral subjectivism) satisfies both scientific and practical concerns. As such, (idealized) subjective morality grounds a moral analog of cognitive biases—namely, subjective moral biases (e.g., committed anti-racists unconsciously discriminating). I also argue that "cognitive heuristic" is defined by its contrast with rationality. Consequently, heuristics explain biases, which are also so defined. However, such contrasting with rationality is causally irrelevant to cognition. This frustrates the presumed usefulness of the kind, heuristic, in causal explanation. As such, in the moral case, I jettison the role of causal explanation and tailor categories solely for contrastive explanation. As such, “moral heuristic” is replaced with "subjective moral fallacy," which is defined by its contrast with subjective morality and explains subjective moral biases. The resultant subjective moral biases and fallacies framework can undergird future empirical research

Getting to know you: Accuracy and error in judgments of character

Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate

X - Phi and Carnapian Explication

The rise of experimental philosophy has placed metaphilosophical questions, particularly those concerning concepts, at the center of philosophical attention. X-phi offers empirically rigorous methods for identifying conceptual content, but what exactly it contributes towards evaluating conceptual content remains unclear. We show how x-phi complements Rudolf Carnap’s underappreciated methodology for concept determination, explication. This clarifies and extends x-phi’s positive philosophical import, and also exhibits explication’s broad appeal. But there is a potential problem: Carnap’s account of explication was limited to empirical and logical concepts, but many concepts of interest to philosophers are essentially normative. With formal epistemology as a case study, we show how x-phi assisted explication can apply to normative domains

Libertarian Paternalism Is Not An Oxymoron

Cass R. Sunstein and Richard H. Thaler assert that while the idea of libertarian paternalism might seem to be an oxymoron, it is both possible and legitimate for private and public institutions to affect behavior while also respecting freedom of choice. Often people's preferences are ill-formed, and their choices will inevitably be influenced by default rules, framing effects, and starting points. In these circumstances, a form of paternalism cannot be avoided. Equipped with an understanding of behavioral findings of bounded rationality and bounded self-control, libertarian paternalists should attempt to steer people's choices in welfare-promoting directions without eliminating freedom of choice. Sunstein and Thaler argue that it is also possible to show how a libertarian paternalist might select among the possible options and to assess how much choice to offer. This paper gives examplesfrom many areas, including savings behavior, labor law, and consumer protection.

The Case for Case Studies

This volume demonstrates how to conduct case study research that is both methodologically rigorous and useful to development policy. It will interest scholars and students across the social sciences using case studies, and provide constructive guidance to practitioners in development and public administration

INCREASING RELEVANCE IN IS RESEARCH: CONTEXTUALIZING KNOWLEDGE IN NETWORKS

Relevance is useful and actionable knowledge in situ. It is a result and condition of ‘knowledge exchanges’ between practitioner and scientific communities taking place in heterogeneous knowledge networks. Whereas IS research has traditionally emphasized a selection perspective in disputes around relevance preferring scholarly community’s viewpoint over the other, this paper articulates a networking perspective which analyzes enablers, competencies and barriers for useful knowledge flow across communities. After introducing main types of knowledge that flow in the knowledge system we apply the concept of absorptive capacity to analyze the outcomes and processes of knowledge exchanges and map how each type of knowledge is sought and absorbed by one community from another by leveraging specific knowledge networks including the focal one. Given little empirical research about a) how IT managers and other high level IT professionals (consultants, etc) source and exchange different forms of knowledge in their practice, and b) the properties of this knowledge such as its volatility, accuracy, validity demands, forms of sourcing, genre or presentation, we outline a field study on salient knowing and knowledge practices among high achievement IT individuals with significant careers. Preliminary findings are reported

Stereotype threat, epistemic agency, and self-identity

Stereotype threat is a psychological phenomenon that occurs when individuals become aware that their behavior could potentially confirm a negative stereotype. Though stereotype threat is a widely studied phenomenon in social psychology, there has been relatively little scholarship on it in philosophy, despite its relevance to issues such as implicit cognition, epistemic injustice, and diversity in philosophy. However, most psychological research on stereotype threat discusses the phenomenon by using an overly narrow picture of it, which focuses on one of its effects: the ability to hinder performance. As a result, almost all philosophical work on stereotype threat is solely focused on issues of performance too. Social psychologists know that stereotype threat has additional effects, such as negatively impacting individuals’ motivation, interests, long-term health, and even their sense of self, but these other effects are often downplayed, or even forgotten about. Therefore, the “standard picture” of stereotype threat needs to be expanded, in order to better understand the theoretical aspects of the phenomenon, and to develop broader, more effective interventions. This dissertation develops such an “expanded picture” of stereotype threat, which emphasizes how the phenomenon can negatively impact both self-identity and epistemic agency. In doing so, I explore the nature of stereotypes more generally and argue that they undermine groups’ moral status and contribute to what is called “ontic injustice.” I also show how stereotype threat harms members of socially subordinated groups by way of coercing their self-identity and undermining their epistemic agency, which I argue is a form of epistemic injustice. Lastly, I analyze the expanded picture’s implications for addressing the low proportion of women in professional philosophy. I critically engage recent arguments that these low numbers simply reflect different interests women have, which if innate or benign, would require no intervention. My expanded picture shows the mistakes in this sort of reasoning, which is also present in discussions on the underrepresentation of women in science. The expanded picture of stereotype threat that this dissertation develops is not only practically important, but also advances key philosophical debates in social epistemology, applied ethics, and social metaphysics