Among the many common criticisms of the Turing test, a valid criticism concerns its scope. Intelligence is a complex and multi-dimensional phenomenon that will require testing using as many different formats as possible. The Turing test continues to be valuable as a source of evidence to support the inductive inference that a machine possesses a certain kind of intelligence and when interpreted as providing a behavioural test for a certain kind of intelligence. This paper raises the novel criticism that the (...) Turing test represents an example of Goodhart’s Law operating in the field of artificial intelligence. As one measure towards the goal of creating genuinely intelligent machines, the Turing test must not be confused with the goal itself. Moreover, the Turing test ought to be augmented such that through its use additional evidence could be secured to support the strong inference that a machine, were it to pass the Turing Test, could think like a human. (shrink)

Artificially intelligent machines are different in kind from all previous machines and tools. While many are used for relatively benign purposes, the types of artificially intelligent machines that we should care about, the ones that are worth focusing on, are the machines that purport to replace humans entirely and thereby engage in what Brian Cantwell Smith calls “judgment.” As impressive as artificially intelligent machines are, their abilities are still derived from humans and as such lack the sort of normative commitments (...) that humans have. So while artificially intelligent machines possess a great capacity for “reckoning,” to use Smith’s terminology, i.e., a calculative prowess of extraordinary utility and importance, they still lack the kind of considered human judgment that accompanies the ethical commitment and responsible action we humans must ultimately aspire toward. But there is a perfect technological storm brewing. Artificially intelligent machines are analogous to a perfect storm in that such machines involve the convergence of a number of factors that threaten our ability to behave ethically and maintain meaningful human control over the outcomes of processes involving artificial intelligence. I argue that the storm in the context of artificially intelligent machines makes us vulnerable to moral complacency. That is, this perfect technological storm is capable of lulling people into a state in which they abdicate responsibility for decision-making and behaviour precipitated by the use of artificially intelligent machines, a state that I am calling “moral complacency.” I focus on three salient problems that converge to make us especially vulnerable to becoming morally complacent and losing meaningful human control. The first problem is that of transparency/opacity. The second problem is that of overtrust in machines, often referred to as the automation bias. The third problem is that of ascribing responsibility. I examine each of these problems and how together they threaten to render us morally complacent. (shrink)

Brain parts can scale independently of the whole brain, and an example is given to point out that the authors underestimate variation that can exist in brains of equal size.

Proactively including the ethical and societal issues of new technologies could have a positive effect on their acceptance. These issues could be captured in terms of values. In the literature, the values stakeholders deem important for the development of technology have often been identified. However, the relative ranking of these values in relation to each other have not been studied often. The best worst method is proposed as a possible method to determine the weights of values, hence it is used (...) in an evaluative fashion. The applicability of the method is tested by applying it to the case of smart meters, one of the main components of the smart grid. The importance of values is examined for three dimensions of acceptance namely sociopolitical, market, and household acceptance. (shrink)

As bridges or brains become bigger or smaller, the changes pose problems of design thatneed to be solved. Larger brains could have larger or more neurons, or both. With largerneurons, it becomes difficult to maintain conduction times over longer axons andelectrical cable properties over longer dendrites. With more neurons, it becomes difficultfor each neuron to maintain its proportion of connections with other neurons. Theseproblems are addressed by making brains more modular, thereby reducing the lengths ofmany connections, and by altering functions. (...) Smaller brains may not have enoughneurons for all circuits, and they may lose modules and functions. Mammals with moreneocortex tend to have more cortical areas and more columns and types of columnswithin the larger areas. (shrink)

In this chapter, we integrate three distinct strands of thought to argue that the concept of “fairness” varies significantly across cultures. As a result, ensuring that human-AI interactions meet relevant fairness standards requires a deep understanding of the cultural contexts in which AI-enabled systems are deployed. Failure to do so will not only result in the generation of unfair outcomes by an AI-enabled system, but it will also degrade legitimacy of and trust in the system. The first strand concerns the (...) dominant approach taken in the technology industry to ensure that AI-enabled systems are fair. This approach is to reduce fairness to some mathematical formalism that can be applied universally, a quintessentially Western conception of fairness. The second strand concerns alternative conceptions of fairness that have their roots in philosophical traditions of the East, namely Confucian virtue ethics. Understanding how individuals from diverse cultural backgrounds perceive fairness—particularly their beliefs about fairness in human-to-human interactions—is crucial for understanding how they will interpret fairness in human-AI interactions. Building on these philosophical and behavioral differences, as highlighted by empirical research, the third strand integrates insights from political science and interdisciplinary studies. This perspective offers valuable guidance on designing AI-enabled systems to align with contextually relevant standards of fairness. Examining existing beliefs about fairness within the context of institutional decision-making provides valuable insights into what people expect from AI-generated decisions. These expectations often include key elements such as a sufficient degree of transparency, clear lines of accountability, and mechanisms to contest decisions made by the system—all of which are essential components of procedural fairness. Rather than adopting a one-size-fits-all approach to ensuring fairness, the design and deployment of AI systems must carefully account for the operating environment, including the socio-political and cultural context, to ensure that the system aligns with the relevant standards of fairness. (shrink)

For older workers, self-employment is an important alternative to waged employment. Drawing on social learning theory and social cognitive career theory we examine how attitudes toward one’s own aging, future time perspective (captured by perceived time left to live) and perceived support from referent individuals predict self-efficacy for entrepreneurship and outcome expectations, influencing self-employment interest. Findings from a sample of professional association members (n = 174, mean age 52.5 years), revealed that an open-ended time perspective relates positively to entrepreneurial self-efficacy, (...) while social support relates positively to outcome expectations. Consistent with social cognitive career theory, entrepreneurial self-efficacy mediated the relationship between future time perspective and interest in self-employment, and outcome expectations mediated the relationship between social support and interest in self-employment. This study extends current career and entrepreneurship theory in several ways. First, the inclusion of age-related psychosocial and sociocultural factors in the study model shed light on the intersection between older age, the contextual environment and development of self-employment interest. Second, the findings support earlier arguments that older entrepreneurship is a social process whereby the social context in which people work and live influences their interest in entrepreneurship, and that entrepreneurial behavior among older peop... (shrink)

Standardisation is increasingly seen as a means to insert ethics in innovation processes. We examine the institutionalisation of responsible innovation in de jure standardisation as this is an important but unexplored research area. In de jure standardisation, stakeholders collaborate in committees to develop standards. We adopt the anticipation, inclusion, reflexivity, and responsiveness responsible innovation framework as our theoretical lens. Our study suggests that responsible standardisation processes should embody forms of these four dimensions. We investigate the institutionalisation of these dimensions and (...) identify 96 factors that can motivate, hinder, or facilitate responsible standardisation. Factors were found through in-depth interviews with managers of a standard developing organisation. These are subsequently validated/rejected using surveys completed by committee representatives. The results suggest that the social desirability of standards is not self-evident. This study could pave the way for future research on responsible standardisation processes, complementing research on legitimacy, responsible innovation, and standardisation. (shrink)

Presents Vedamta Dindimah Duly Translated With A Commentary By The Author - An Easy Manual On Advaita And Ascribed By Some To Sankara - Proclaims The Truth Of Vedanta In Clear Terms. 8 Small Chapters - Including Pre-View, Foreword - Introduction - Preface - Erc.

This paper uses the knowledge account of assertion (KAA) in defense of epistemological contextualism. Part 1 explores the main problem afflicting contextualism, what I call the "Generality Objection." Part 2 presents and defends both KAA and a powerful new positive argument that it provides for contextualism. Part 3 uses KAA to answer the Generality Objection, and also casts other shadows over the prospects for anti-contextualism.

The Knowledge Norm or Knowledge Account of Assertion (KAA) has received added support recently from data on prompting assertion (Turri 2010) and from a refinement suggesting that assertions ought to express knowledge (Turri 2011). This paper adds another argument from parenthetical positioning, and then argues that KAA’s unified explanation of some of the earliest data (from Moorean conjunctions) adduced in its favor recommends KAA over its rivals.

John N. Williams (1994) and Matthew Weiner (2005) invoke predictions in order to undermine the normative relevance of knowledge for assertions; in particular, Weiner argues, predictions are important counterexamples to the Knowledge Account of Assertion (KAA). I argue here that they are not true counterexamples at all, a point that can be agreed upon even by those who reject KAA.

The knowledge account of assertion (KAA) is the view that assertion is governed by the norm that the speaker should know what s/he asserts. It is not the purpose of this article to examine all the criticisms nor to try to give a full defence of KAA, but only to defend it against the charge of being normatively incorrect. It has been objected that assertion is governed by other norms than knowledge, or by no norm at all. It seems to (...) me, however, that a number of these criticisms are based on a number of misunderstandings of the notion of a norm and of the way it can regulated a given practice. Once we spell out in what sense knowledge can play a normative role in this context, the KAA appears much more plausible. (shrink)

Many philosophers are building a solid case in favour of the knowledge account of assertion (KAA). According to KAA, if one asserts that P one represents oneself as knowing that P. KAA has recently received support from linguistic data about prompting challenges, parenthetical positioning and predictions. In this article, I add another argument to this rapidly growing list: an argument from what I will call ‘reinforcing parenthesis’.

The Knowledge Account of Assertion (KAA) says that knowledge is the norm of assertion: you may assert a proposition only if you know that it’s true. The primary support for KAA is an explanatory inference from a broad range of linguistic data. The more data that KAA well explains, the stronger the case for it, and the more difficult it is for the competition to keep pace. In this paper we critically assess a purported new linguistic datum, which, it has (...) been argued, KAA well explains. We argue that KAA does not well explain it. (shrink)

John Turri has recently provided two problem cases for the knowledge account of assertion (KAA) to argue for the express knowledge account of assertion (EKAA). We defend KAA by explaining away the intuitions about the problem cases and by showing that our explanation is theoretically superior to EKAA.