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Artificial intelligence and entrepreneurial ecosystems: understanding the implications of algorithmic decision-making for startup communities

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Journal of Ethics, Entrepreneurship and Technology 2022年第1期2卷 23-38页

作者： Philip T. Roundy Gary W. Rollins College of Business The University of Tennessee at Chattanooga Chattanooga Tennessee USA

Purpose Entrepreneurs are increasingly relying on artificial intelligence (AI) to assist in creating and scaling new ventures. Research on entrepreneurs’ use of AI algorithms (machine learning, natural language processing, artificial neural networks) has focused on the intra-organizational implications of AI. The purpose of this paper is to explore how entrepreneurs’ adoption of AI influences their inter- and meta-organizational relationships. Design/methodology/approach To address the limited understanding of the consequences of AI for communities of entrepreneurs, this paper develops a theory to explain how AI algorithms influence the micro (entrepreneur) and macro (system) dynamics of entrepreneurial ecosystems. Findings The theory’s main insight is that substituting AI for entrepreneurial ecosystem interactions influences not only entrepreneurs’ pursuit of opportunities but also the coordination of their local entrepreneurial ecosystems. Originality/value The theory contributes by drawing attention to the inter-organizational implications of AI, explaining how the decision to substitute AI for human interactions is a micro-foundation of ecosystems, and motivating a research agenda at the intersection of AI and entrepreneurial ecosystems.

关键词： Entrepreneurial ecosystems Artificial intelligence (AI) AI technologies algorithmic decision-making Machine learning Automation Entrepreneurship communities

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Fairness in algorithmic decision-making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems

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ALGORITHMS 2019年第9期12卷 199-199页

作者： Shrestha, Yash Raj Yang, Yongjie Swiss Fed Inst Technol Chair Strateg Management & Innovat CH-8092 Zurich Switzerland Saarland Univ Chair Econ Theory D-66123 Saarbrucken Germany

algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives;(2) clustering a given set of alternatives into disjoint groups based on various similarity measures;or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.

关键词： algorithmic fairness bias machine learning recommender system algorithmic decision-making multi-winner-voting proportional representation survey

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The legitimacy gap of algorithmic decision-making in the public sector: Why it arises and how to address it

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TECHNOLOGY IN SOCIETY 2021年 67卷

作者： Konig, Pascal D. Wenzelburger, Georg TU Kaiserslautern Dept Polit Sci Erwin Schrodinger Str 57 D-67653 Kaiserslautern Germany

algorithmic decision-making (ADM) systems are increasingly adopted by the state to support various administrative functions and improve the effectiveness and efficiency of public services, such as in unemployment services or policing. While these systems create challenges of opaqueness, unfairness, and value trade-offs, the present paper argues that a more fundamental challenge lies in the way these systems alter the epistemic bases of decision-making. It contributes to the literature by highlighting why procedural standards of legitimacy in operative decision-making no longer suffice for certain applications and by discussing how the resulting legitimacy gap can be addressed through stakeholder involvement. By adapting research on participatory technology assessments to the particularities of ADM system design, it is possible to identify the core challenges of such a stakeholder process and the necessary steps to deal with them.

关键词： algorithmic decision-making Public services Smart city Legitimacy Stakeholder participation Accountability

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What influences algorithmic decision-making? A systematic literature review on algorithm aversion

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2022年 175卷 1页

作者： Mahmud, Hasan Islam, A. K. M. Najmul Ahmed, Syed Ishtiaque Smolander, Kari LUT Univ LUT Sch Engn Sci Lappeenranta Finland Univ Toronto Toronto ON Canada

With the continuing application of artificial intelligence (AI) technologies in decision-making, algorithmic decision-making is becoming more efficient, often even outperforming humans. Despite this superior performance, people often consciously or unconsciously display reluctance to rely on algorithms, a phenomenon known as algorithm aversion. Viewed as a behavioral anomaly, algorithm aversion has recently attracted much scholarly attention. With a view to synthesize the findings of existing literature, we systematically review 80 empirical studies identified through searching in seven academic databases and using the snowballing technique. We inductively categorize the influencing factors of algorithm aversion under four main themes: algorithm, individual, task, and high-level. Our analysis reveals that although algorithm and individual factors have been investigated extensively, very little attention has been given to exploring the task and high-level factors. We contribute to algorithm aversion literature by proposing a comprehensive framework, highlighting open issues in existing studies, and outlining several research avenues that could be handled in future research. Our model could guide developers in designing and developing and managers in implementing and using of algorithmic decision.

关键词： algorithmic decision-making Algorithm aversion Algorithm appreciation AI decision-making AI adoption Systematic literature review

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The public perceptions of algorithmic decision-making systems: Results from a large-scale survey

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TELEMATICS AND INFORMATICS 2023年第1期79卷

作者： Aysolmaz, Banu Mueller, Rudolf Meacham, Darian Eindhoven Univ Technol Informat Syst Grp Eindhoven Netherlands Maastricht Univ Sch Business & Econ Maastricht Netherlands Maastricht Univ Dept Philosophy Maastricht Netherlands

algorithmic decision-making (ADM) systems are heavily used by businesses, governments, and the nonprofit sector. Their adoption by the public is important for their optimal performance, but the impact of user perceptions on ADM system adoption is not well-understood. We develop a theoretical model that examines the effect of a user's transparency concern on perceived fairness, accountability, and privacy;it then captures the effect of these on trust in, usefulness of, and finally, intention to adopt an ADM system. We use results from a large-scale survey among 2612 Dutch citizens to test our research model. Our results shed light on the role of transparency concern on trust and perceived usefulness through its impact on perceived fairness, account-ability, and privacy. The survey data of a large representative group enables us to capture public views on ADM systems. Finally, the study provides a comparative view of ADM system percep-tions for different application scenarios. Our insights on differences between scenarios can help organizations prioritize relevant measures to improve the adoption of their ADM systems.

关键词： algorithmic decision-making ADM system Transparency concern Perception Fairness Accountability Privacy Trust algorithmic acceptance Public

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What type of algorithm is perceived as fairer and more acceptable? A comparative analysis of rule-driven versus data-driven algorithmic decision-making in public affairs

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GOVERNMENT INFORMATION QUARTERLY 2023年第2期40卷

作者： Wang, Ge Guo, Yue Zhang, Weimin Xie, Shenghua Chen, Qiwei Cent China Normal Univ Fac Polit Sci Sch Publ Adm Wuhan Peoples R China Beijing Normal Univ Sch Govt Beijing Peoples R China 152 LuoYu Rd Wuhan Hubei Peoples R China

Various types of algorithms are being increasingly used to support public decision-making, yet we do not know how these different algorithm types affect citizens' attitudes and behaviors in specific public affairs. Drawing on public value theory, this study uses a survey experiment to compare the effects of rule-driven versus data-driven algorithmic decision-making (ADM) on citizens' perceived fairness and acceptance. This study also examines the moderating role of familiarity with public affairs and the mediating role of perceived fairness on the relationship. The findings show that rule-driven ADM is generally perceived as fairer and more acceptable than data-driven ADM. Low familiarity with public affairs strengthens citizens' perceived fairness and acceptance of rule-driven ADM more than data-driven ADM, and citizens' perceived fairness plays a significant mediating role in the ef-fect of rule-driven ADM on citizens' acceptance behaviors. These findings further imply that citizens' perceived fairness and acceptance of ADM is strongly shaped by how they perceive familiarity of the decision-making context. In high-familiarity AI application scenarios, the realization of public values may ultimately not be what matters for ADM acceptance among citizens.

关键词： Artificial intelligence algorithmic decision-making Data-driven decision-making Public value Fairness

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Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature

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BIG DATA & SOCIETY 2022年第2期9卷

作者： Starke, Christopher Baleis, Janine Keller, Birte Marcinkowski, Frank Univ Amsterdam Amsterdam Sch Commun Res Nieuwe Achtergracht 166 NL-1018 WV Amsterdam Netherlands Heinrich Heine Univ Dusseldorf Dept Social Sci Dusseldorf Germany

algorithmic decision-making increasingly shapes people's daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires considering people's fairness perceptions when designing and implementing algorithmic decision-making. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 58 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: ( I ) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of algorithmic decision-making. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from Western-democratic contexts. By advocating for more interdisciplinary research adopting a society-in-the-loop framework, we hope our work will contribute to fairer and more responsible algorithmic decision-making.

关键词： algorithmic decision-making fairness perceptions algorithmic fairness artificial intelligence ethics human-centric approach systematic literature review

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Fairness and algorithmic decision-making

TEORIA-RIVISTA DI FILOSOFIA

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TEORIA-RIVISTA DI FILOSOFIA 2022年第2期42卷 117-129页

作者： Giovanola, Benedetta Tiribelli, Simona Univ Macerata Macerata Italy

The paper focuses on one of the most urgent risks of artificial intelligence, and more specifically of algorithmic decision-making (ADM), that is, the risk of being unfair. In the first section we provide an overview of the discussion on fairness in ADM and show its shortcomings;in the second section we pursue an ethical inquiry into the concept of fairness, and identify its main dimensions and components, drawing insight from a renewed reflection on respect, which goes beyond the idea of equal respect to include respect for particular individuals too. In the third section we show how our conceptual re-elaboration of fairness can help identify the criteria that ought to steer the ethical design of ADM-based systems to make them really fair.

关键词： Artificial Intelligence algorithmic decision-making fairness ethical design

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Social impacts of algorithmic decision-making: A research agenda for the social sciences

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BIG DATA & SOCIETY 2022年第1期9卷

作者： Gerdon, Frederic Bach, Ruben L. Kern, Christoph Kreuter, Frauke Univ Mannheim Mannheim Ctr European Social Res A 56 D-68131 Mannheim Germany Ludwig Maximilians Univ LMU Munchen Dept Stat Munich Germany Univ Mannheim Sch Social Sci Mannheim Germany Univ Maryland College Pk MD 20742 USA

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.

关键词： Algorithms social inequality fair machine learning social impacts of AI algorithmic decision-making artificial intelligence

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algorithmic decision making in Education: Challenges and Opportunities 15

Algorithmic Decision Making in Education: Challenges and Opp...

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15th IEEE Global Engineering Education Conference (IEEE EDUCON)

作者： Levantis, Nikolaos Sgora, Aggeliki Ionian Univ Dept Digital Media & Commun Argostoli Greece

ISBN: (纸本)9798350394023;9798350394030

algorithmic technology has experienced significant development in recent years. With advancements in Artificial Intelligence (AI) and the proliferation of Machine Learning (ML) algorithms, algorithmic decision-making Systems (ADSs) are embraced by several public sectors, such as health, justice, education, in order to improve their efficiency and create additional sources of information. The need for modernization has led to extended Digital Transformation projects that pose a significant impact on everyday processes but also organizations' long-term goals and even purpose. However, although the benefits, ADSs raise serious concerns in terms of transparency, fairness, and accountability, trust, etc. This paper aims to point out the major challenges related to algorithmic decision-making technology, especially in the field of education, focusing on the effectiveness and suitability of current systems based on their technological limitations. In particular, it identifies and breaks down the key challenges emerging due to the adoption of ADSs and the concerns that they create, while it also discusses the solutions proposed by experts in recent research papers. The paper also provides some thoughts for the future of ADSs in the education sector reflecting upon potential counteracting measures and conflicts with existing technologies or issues regarding the implementation of those solutions, identifying areas that need further research.

关键词： algorithmic decision-making Education Machine Learning Challenges Fairness Transparency Accountability Trust

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