자연주의적 탐구조사(Naturalistic Inquiry)

计量经济分析 = Econometric analysis

The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

Trust: The Social Virtues and the Creation of Prosperity

The measurement of meaning

Abstract Intelligence Augmentation Systems (IAS) allow for more efficient and effective corporate processes by means of an explicit collaboration between artificial intelligence and human judgment. However, the higher degree of system autonomy, along with the enrichment of human capabilities, amplifies pre-existing issues of the distribution of moral responsibility: If an IAS has caused harm, firms who have operated the system might argue that they lack control over its actions, whereas firms who have developed the system might argue that they lack control over its actual use. Both parties rejecting responsibility and attributing it to the autonomous nature of the system leads to a variety of technologically induced responsibility gaps. Given the wide-ranging capabilities and applications of IAS, such responsibility gaps warrant a theoretical grounding in an ethical theory, also because the clear distribution of moral responsibility is an essential first step to govern explicit morality in a firm using structures such as accountability mechanisms. As part of this paper, first the necessary conditions for the distribution of responsibility for IAS are detailed. Second, the paper develops an ethical theory of Reason-Responsiveness for Intelligence Augmentation Systems (RRIAS) that allows for the distribution of responsibility at the organizational level between operators and providers. RRIAS provides important guidance for firms to understand who should be held responsible for developing suitable corporate practices for the development and usage of IAS. 

/pdf/augmented-intelligence-augmented-responsibility-we8vvqun.pdf

Augmented Intelligence, Augmented Responsibility?

How Transparency Measures Can Attenuate Initial Failures of Intelligent Decision Support Systems

Standards setting has gone from being a technical concern for individual companies to being a factor with important strategic implications for cooperation and competition among market players. The strategic issues raised by this development include the formation of competitive advantages by promoting standards or preventing their diffusion. In Sect. 1 of this chapter, we discuss the economic interpretation of standards from a descriptive perspective by drawing on the closely related issue of positive network effects, which induce a self-enhancing cycle and lead to a widespread adoption of the standard. In Sect. 2, we highlight standards setting as a competitive weapon for media companies. Standards act not only as a precondition for compatibility across technologies and systems, but can also be used to generate incompatibility and customer lock-in effects. Thus, we examine standardization strategies and tactics for horizontal competitors in the digital media industry. In Sect. 3, we view standards as a strategic instrument for cost reduction in media companies by describing a normative decision model for the economic selection of standards, referred to as the standardization problem.

The Strategic Role of Communication Standards for Media Companies

Data analytics (DA) provides versatile decision support to help employees tackle the rising complexity of today’s business decisions. Notwithstanding their benefits, research has shown their potential for provoking discriminatory decisions. While technical causes have been studied, the “human” side has been neglected, albeit employees mostly still need to decide to turn DA recommendations into actions. Drawing upon theories of technology dominance and of moral disengagement, we investigate how task complexity and employees’ expertise affect the approval of discriminatory DA recommendations. Through two online experiments, we confirm the important role of advantageous comparison, displacement of responsibility, and dehumanization, as the cognitive moral disengagement mechanisms that facilitate such approvals. While task complexity generally enhances these mechanisms, expertise retains a critical role in DA-supported decision-making processes. Importantly, we find that task complexity’s effects on users’ dehumanization varies: more data subjects increase dehumanization, whereas richer information on subjects has the opposite effect. By identifying the cognitive mechanisms that facilitate approvals of discriminatory DA recommendations, this study takes the first step toward designing tools, methods, and practices that combat unethical consequences of using these systems.

https://journals.sagepub.com/doi/pdf/10.1177/02683962231181148

Not Seeing the (Moral) Forest for the Trees? How Task Complexity and Employeesâ Expertise Affect Moral Disengagement with Discriminatory Data Analytics Recommendations

Christian Matt

Papers

Augmented Intelligence, Augmented Responsibility?

How Transparency Measures Can Attenuate Initial Failures of Intelligent Decision Support Systems

The Strategic Role of Communication Standards for Media Companies

Not Seeing the (Moral) Forest for the Trees? How Task Complexity and Employeesâ Expertise Affect Moral Disengagement with Discriminatory Data Analytics Recommendations

Statistische Auswertungssoftware für die Analyse großer Datenbestände

Christian Matt

Papers

Augmented Intelligence, Augmented Responsibility?

How Transparency Measures Can Attenuate Initial Failures of Intelligent Decision Support Systems

The Strategic Role of Communication Standards for Media Companies

Not Seeing the (Moral) Forest for the Trees? How Task Complexity and Employeesâ Expertise Affect Moral Disengagement with Discriminatory Data Analytics Recommendations

Statistische Auswertungssoftware für die Analyse großer Datenbestände

Not Seeing the (Moral) Forest for the Trees? How Task Complexity and Employeesâ Expertise Affect Moral Disengagement with Discriminatory Data Analytics Recommendations