Human Decision Making in AI Augmented Systems: Evidence from the Initial Coin Offering Market

Basu, Saunak; Garimella, Aravinda; Han, Wencui; Dennis, Alan R.

doi:10.24251/hicss.2021.020

Cited by 5 publications

(6 citation statements)

References 16 publications

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“…Basu et al 39 2021 This study suggests that humans deploy AI agents as filters and pay greater attention to the riskier suggestions because they have an asymmetric framing impact on the recommendations.…”

Section: Paper Year Key Findings Limitations and Open Issuesmentioning

confidence: 87%

“…However, the coin's founders diverted the funds and unlawfully financed certain real estate assets without indicating any income source. Basu, Saunak, et al 39 suggested that human specialists can use AI agents as a filter that rates poorly and spend more time researching projects. Karimov et al 38 investigated how effectively one can anticipate if an offering is a fraud.…”

Section: Fake Initial Coin Offerings (Ico)mentioning

confidence: 99%

“…Basu, Saunak, et al 39 suggested that human specialists can use AI agents as a filter that rates poorly and spend more time researching projects. Karimov et al 38 investigated how effectively one can anticipate if an offering is a fraud.…”

Section: Background and Preliminariesmentioning

confidence: 99%

See 2 more Smart Citations

Blockchain and crypto forensics: Investigating crypto frauds

Agarwal,

Rishiwal,

Tanwar

et al. 2023

Int J Network Mgmt

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In the past few years, cryptocurrency has gained widespread acceptance because of its decentralized nature, quick and secure transactions, and potential for investment and speculation. But the increased popularity has also led to increased cryptocurrency fraud, including scams, phishing attacks, Ponzi schemes, and other criminal activities. Although there is little documentation of cryptocurrency fraud, an in‐depth study is essential to recognize various scams in different cryptocurrencies. To fill this gap, a study investigated cryptocurrency‐related fraud in various cryptocurrencies and provided a taxonomy of crypto‐forensics and forensic blockchain. In addition, we have introduced an architecture that integrates artificial intelligence (AI) and blockchain technologies to investigate and protect against instances of cryptocurrency fraud. The suggested design's effectiveness was evaluated using several machine learning (ML) classification algorithms. The conclusion of the evaluation confirmed that the random forest (RF) classifier performed the best, delivering the highest level of accuracy, that is, 97.5%. Once the ML classifiers detect cryptocurrency fraud, the information is securely stored in the InterPlanetary File System (IPFS); the document's hash is also stored in the blockchain using smart contracts. Law enforcement can leverage blockchain technology to secure access to fraudulent cryptographic transactions. The proposed architecture was tested for bandwidth utilization. Despite the potential benefits of blockchain and crypto‐forensics, several issues and challenges remain, including privacy concerns, standardization, and difficulty identifying fraud between crypto‐currencies. Finally, the paper discusses various problems and challenges in blockchain and crypto forensics to investigate cryptocurrency fraud.

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Section: Paper Year Key Findings Limitations and Open Issuesmentioning

confidence: 87%

Section: Fake Initial Coin Offerings (Ico)mentioning

confidence: 99%

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Blockchain and crypto forensics: Investigating crypto frauds

Agarwal,

Rishiwal,

Tanwar

et al. 2023

Int J Network Mgmt

View full text Add to dashboard Cite

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“…Furthermore, new cryptocurrency brings surveillance value to the detection of fraud via public authentication and also public value that is reflected by people's acceptance (Alstyne, 2014). Related studies examined the effects of digital currencies on monetary systems (Iwamura et al, 2014), their extreme volatility and negative bubbles (Bianchetti et al, 2018;Fry & Cheah, 2016), surveillance applications (Foley et al, 2019), design of token contracts (Malinova & Park, 2018), blockchain adoption for solving counterfeiting (Pun et al, 2021), and the role of artificial intelligence-based ratings (Basu et al, 2019) in an initial coin offering (ICO). Notably, Liu et al (2021) showed the importance of the technological sophistication of ICO projects when it comes to valuing ICO success, implying that technological savviness is critical for cryptocurrency investments.…”

Section: Production and Operations Managementmentioning

confidence: 99%

Interdependence between online peer‐to‐peer lending and cryptocurrency markets and its effects on financial inclusion

Chung

Kim

Lee

et al. 2023

Production and Operations Management

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Online peer‐to‐peer (P2P) lending has emerged as an innovative financial technology (FinTech) platform that renders financial services that are potentially more inclusive and affordable than those offered by traditional financial institutions. A similar purpose is served by cryptocurrency markets, where transaction costs are reduced and financial accessibility is improved based on disruptive technologies such as blockchain and distributed ledgers. Despite these developments, however, in the operations management literature limited attention has been devoted to the contribution of online P2P lending to the promotion of financial inclusion (i.e., the availability and usage of financial services for all groups of people) and its dynamic interplay with cryptocurrency markets. The rise of cryptocurrency markets affects the composition and activity of borrowers and investors in P2P lending markets and hence the capacity of the latter to support financial inclusion, leading to an operations management challenge in online P2P lending. We examine how cryptocurrency markets influence P2P lending markets' democratization of access to financial services, particularly P2P borrowing. To investigate these effects in depth, we develop a simple theoretical model to derive testable propositions, which are then empirically validated on the basis of unique data sets. We find that the growth in cryptocurrency markets is associated with increased loan requests and larger loan amounts in P2P markets, especially from borrowers who maintain good credit ratings, possess technical knowledge about cryptocurrencies, and intend to borrow for investment purposes. Our results suggest that cryptocurrency markets bring economic gains to the P2P lending market, at least in the short term. Nonetheless, the transfer of funds from P2P lending to cryptocurrency markets, particularly by highly creditworthy and tech‐savvy investors, may provoke increased inequality in access to P2P lending markets. By scrutinizing the interdependence between two representative FinTech markets we uncover important operations management implications for theory and practice regarding the healthy growth and effective governance of crowdfunding platforms and the corresponding sustainability of their role in upholding financial inclusion.

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“…In other words, the several definitions of ADM indicate that there are several shades of grey in the world of automated decision-making -from algorithms supporting and augmenting human agents' decisionmaking to humans fully delegating the task to algorithms [11], [15], [30], [31]. This is in line with Rai et al's view that as digital technologies continue to penetrate our work and daily lives, we are observing the design and deployment of a variety of human-AI hybrids that range in human-AI interdependence from substitution (AI replaces humans) to augmentation (humans and AI augment one and another) and assemblage (AI and humans are dynamically brought together to function as an integrated unit) [32].…”

Section: Automated Decision-making (Adm)mentioning

confidence: 99%

Algorithmically Controlled Automated Decision-Making and Societal Acceptability: Does Algorithm Type Matter?

Grangé¹

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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As technological capabilities expand, an increasing number of decision-making processes (e.g., rankings, selections, exclusions) are being delegated to computerized systems. In this paper, we examine the societal acceptability of a consequential decisionmaking system (university admission) to those subject to the decision (i.e., applicants). We analyze two key drivers: the nature of the decision-making agent (a human vs an algorithm) and the decision-making logic used by the agents (predetermined vs emerging). Consistent with uniqueness neglect theory, we propose that applicants will be more positive toward the use of human agents compared to computerized systems. Consistent with the theory of procedural justice, we further argue that applicants will find the use of a predetermined logic to be more acceptable than an emerging logic. We present the details and results of a factorial survey designed to test our theoretical model.

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Human Decision Making in AI Augmented Systems: Evidence from the Initial Coin Offering Market

Cited by 5 publications

References 16 publications

Blockchain and crypto forensics: Investigating crypto frauds

Blockchain and crypto forensics: Investigating crypto frauds

Interdependence between online peer‐to‐peer lending and cryptocurrency markets and its effects on financial inclusion

Algorithmically Controlled Automated Decision-Making and Societal Acceptability: Does Algorithm Type Matter?

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