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作者： JESSICA LYNN BABIN The University of Georgia

学位级别：博士

The abundance of online information available to consumers may leave them feeling overwhelmed at the volume of data they must sift through in order to make a choice. I examine two e-commerce domains in which firms can assist in decision-making: curation (i. e., the selecting, organizing, and displaying of content) and platform firms (i. e., online firms that link buyers and sellers). Companies increasingly offer services that curate selections of products for consumers (e. g., the clothing subscription service StitchFix and music playlists on Spotify). Though algorithms drive much of the decision-making behind curation services, companies often choose to present a human curator to consumers. Essay 1 examines the consumer preference for algorithmic over human curators, especially in the context of complex decision-making. Essay 2 focuses on consumer search on online platform firms. I analyze clickstream data to understand the consumer online search process for a complex product and uncover ways the platform firm can assist buyers in making more efficient choices. Finally, in Essay 3, I provide a conceptual framework for understanding the role of curation in marketing. Together, these essays demonstrate that firms can assist consumers in their decision-making, providing value to consumers in a way that also benefits the firm.

关键词： algorithmic decision-making Humanization Platform firms Consumer online search Clickstream data Curation Consumer-based strategy

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'Not all algorithms!' Lessons from the Private Sector on Mitigating Gender Discrimination

'Not all algorithms!' Lessons from the Private Sector on Mit...

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2022 Informatik in den Naturwissenschaften, INFORMATIK 2022 - 2022 Computer Science in the Natural Sciences, INFORMATIK 2022

作者： Winkler, Mareike Köhne, Sonja Klöpper, Miriam Carl von Ossietzky University of Oldenburg Institute for Social Sciences Oldenburg26111 Germany Denkfabrik Digitale Arbeitsgesellschaft The German Federal Ministry for Labour and Social Affairs Wilhelmstraße 49 Berlin10117 Germany Alexander von Humboldt Institute for Internet & Society Research Group Innovation Entrepreneurship & Society Französische Straße 9 Berlin10117 Germany FZI Forschungszentrum Informatik Department Innovation Strategy and Transfer Haid-und-Neu Straße 10-14 Karlsruhe76131 Germany

ISBN: (纸本)9783885797203

In the public sector, the use of algorithmic decision-making (ADM) systems can be directly linked to crucial state assistance, such as welfare benefits. Prominent examples such as an algorithm of the Public Employment Service Austria, that predicted below-average placement chances for women, underline the high risks of systematic gender discrimination. The use of ADM is rather novel in the public sector. The private sector, on the other hand, can resort to a relative wealth of experience in adopting such algorithms and dealing with algorithmic gender discrimination, for example in recruiting. Based on empirical examples our paper 1) explores how gender is currently considered in the development of ADM for the public sector, 2) highlights the potential risks of algorithmic gender discrimination, and 3) analyzes how the public sector can learn from the experience of the private sector in mitigating these risks. © 2022 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： algorithmic bias algorithmic decision-making automation bias eGov gender discrimination

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Bridging the Gap from AI Ethics Research to Practice 20

Bridging the Gap from AI Ethics Research to Practice

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ACM Conference on Fairness, Accountability, and Transparency (FAT)

作者： Baxter, Kathy Schlesinger, Yoav Aerni, Sarah Baker, Lewis Dawson, Julie Kenthapadi, Krishnaram Kloumann, Isabel Wallach, Hanna Salesforce Inc San Francisco CA 94105 USA Pymetrics Inc San Francisco CA USA Yoti Inc San Francisco CA USA LinkedIn Inc Mountain View CA USA Facebook Inc Menlo Pk CA USA Microsoft Inc Seattle WA USA

ISBN: (纸本)9781450369367

The study of fairness in machine learning applications has seen significant academic inquiry, research and publication in recent years. Concurrently, technology companies have begun to instantiate nascent program in AI ethics and product ethics more broadly. As a result of these efforts, AI ethics practitioners have piloted new processes to evaluate and ensure fairness in their machine learning applications. In this session, six industry practitioners, hailing from LinkedIn, Yoti, Microsoft, Pymetrics, Facebook, and Salesforce share insights from the work they have undertaken in the area of fairness, what has worked and what has not, lessons learned and best practices instituted as a result.• Krishnaram Kenthapadi presents LinkedIn's fairness-aware reranking for talent search.• Julie Dawson shares how Yoti applies ML fairness research to age estimation in their digital identity platform.• Hanna Wallach contributes how Microsoft is applying fairness principles in practice.• Lewis Baker presents Pymetric's fairness mechanisms in their hiring algorithm.• Isabel Kloumann presents Facebook's fairness assessment framework through a case study of fairness in a content moderation system.• Sarah Aerni contributes how Salesforce is building fairness features into the Einstein AI *** on those insights, we discuss insights and brainstorm modalities through which to build upon the practitioners' work. Opportunities for further research or collaboration are identified, with the goal of developing a shared understanding of experiences and needs of AI ethics practitioners. Ultimately, the aim is to develop a playbook for more ethical and fair AI product development and deployment.

关键词： Artificial Intelligence algorithmic decision-making fairness ML fairness practitioners ethics data

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Where do algorithmic Accountability and Explainability Frameworks Take Us in the Real World? 20

Where do Algorithmic Accountability and Explainability Frame...

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ACM Conference on Fairness, Accountability, and Transparency (FAT)

作者： Szymielewicz, Katarzyna Bacciarelli, Anna Hidvegi, Fanny Foryciarz, Agata Penicaud, Soizic Spielkamp, Matthias Panoptykon Fdn Warsaw Poland Amnesty Int UK London England AccessNow Brussels Belgium Stanford Univ Stanford CA 94305 USA French Prime Minister Serv Etalab Paris France AlgorithmWatch Berlin Germany

ISBN: (纸本)9781450369367

This hands-on session takes academic concepts and their formulation in policy initiatives around algorithmic accountability and explainability and tests them against real cases. In small groups we will (1) test selected frameworks on algorithmic accountability and explainability against a concrete case study (that likely constitutes a human rights violation) and (2) test different formats to explain important aspects of an automated decision-making process (such as input data, type of an algorithm used, design decisions and technical parameters, expected outcomes) to various audiences (end users, affected communities, watchdog organisations, public sector agencies and regulators). We invite participants with various backgrounds: researchers, technologists, human rights advocates, public servants and designers.

关键词： Accountability algorithmic decision-making algorithmic impact assessment Explanations Fairness Discrimination Interpretability Philosophy of Science

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Risky Code: A Digital Workshop to Educate Participants and Facilitate Deliberation About algorithmically Informed decision-making

Risky Code: A Digital Workshop to Educate Participants and F...

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作者： Holmes, Joli Northeastern University

学位级别：M.F.A., Master of Fine Arts

algorithmically informed decision-making was an early focus in domains such as human factors, e.g., air traffic control and clinical contexts. As data collection has become more automated and computing capabilities have expanded, AI has become both a household term and a household item. Today, algorithmic decision-making tools are now used in every field; they are used to evaluate prisoners for parole, triage patients in emergency rooms, and predict where and when services might be needed. In the originating contexts of algorithmic decision-making, end-users would have been someone such as a doctor or an air traffic controller. In these contexts, the decision-maker (e.g., air traffic controller) and stakeholders of the decision (e.g., passengers) likely have high levels of consensus — safely flying from origin to destination — making decision-making more ***, the societal problems that public policy interventions address inherently differ from the engineering problems where algorithmic decision-making was initially employed. Public policy problems are particularly complex due to their political nature. Stakeholder groups of public policy problems have heterogeneous values resulting in a solution that will afford some stakeholders or the system and disafford others. algorithmic decision-making tools have been marketed to policymakers as an objective way to make decisions while increasing efficiency, decreasing costs, and minimizing human bias and error. However, algorithmic systems are historically and systemically situated in social systems, thereby reproducing the dominant norms and values of these systems. Moreover, policymakers who purchase and use algorithmic systems are experts in areas other than AI and may need to be aware of algorithmic equity and bias issues. Few artifacts exist to inform policymakers about algorithmic decision-making's potential consequences. This research proposes an intervention — Risky Code — a digital workshop to in

关键词： algorithmic decision-making algorithmic literacy Fairness Public policy Risk Wicked problems

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Towards a System Dynamics Framework for Human-Machine Learning decisions: A Case Study of New York Citi Bike

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10647页

作者： Sankaran, Ganesh Palomino, Marco A. Knahl, Martin Siestrup, Guido Univ Plymouth Sch Engn Comp & Math Plymouth PL4 8AA England Univ Aberdeen Sch Nat & Comp Sci Aberdeen AB24 3UE Scotland Hsch Furtwangen Univ Fac Business Informat Syst D-78120 Furtwangen Germany

The growing number of algorithmic decision-making environments, which blend machine and bounded human rationality, strengthen the need for a holistic performance assessment of such systems. Indeed, this combination amplifies the risk of local rationality, necessitating a robust evaluation framework. We propose a novel simulation-based model to quantify algorithmic interventions within organisational contexts, combining causal modelling and data science algorithms. To test our framework's viability, we present a case study based on a bike-share system focusing on inventory balancing through crowdsourced user actions. Utilising New York's Citi Bike service data, we highlight the frequent misalignment between incentives and their necessity. Our model examines the interaction dynamics between user and service provider rule-driven responses and algorithms predicting flow rates. This examination demonstrates why understanding these dynamics is essential for devising effective incentive policies. The study showcases how sophisticated machine learning models, with the ability to forecast underlying market demands unconstrained by historical supply issues, can cause imbalances that induce user behaviour, potentially spoiling plans without timely interventions. Our approach allows problems to surface during the design phase, potentially avoiding costly deployment errors in the joint performance of human and AI decision-makers.

关键词： machine learning system dynamics simulation modelling algorithmic decision-making supply chain planning New York City Citi Bike

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Learning decision catalogues for situated decision making: The case of scoring systems

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2024年 171卷

作者： Heid, Stefan Hanselle, Jonas Fuernkranz, Johannes Huellermeier, Eyke Ludwig Maximilians Univ Munchen Inst Informat Munich Germany Munich Ctr Machine Learning Munich Germany Johannes Kepler Univ Linz Linz Austria

In this paper, we formalize the problem of learning coherent collections of decision models, which we call decision catalogues, and illustrate it for the case where models are scoring systems. This problem is motivated by the recent rise of algorithmic decision-making and the idea to improve human decision-making through machine learning, in conjunction with the observation that decision models should be situated in terms of their complexity and resource requirements: Instead of constructing a single decision model and using this model in all cases, different models might be appropriate depending on the decision context. decision catalogues are supposed to support a seamless transition from very simple, resource-efficient to more sophisticated but also more demanding models. We present a general algorithmic framework for inducing such catalogues from training data, which tackles the learning task as a problem of searching the space of candidate catalogues systematically and, to this end, makes use of heuristic search methods. We also present a concrete instantiation of this framework as well as empirical studies for performance evaluation, which, in a nutshell, show that greedy search is an efficient and hard-to-beat strategy for the construction of catalogues of scoring systems.

关键词： Machine learning algorithmic decision-making Scoring systems

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A Cogitation on the ChatGPT Craze from the Perspective of Psychological Algorithm Aversion and Appreciation

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Psychology Research and Behavior Management 2023年 16卷 3837-3844页

作者： Hao Yu Faculty of Education Shaanxi Normal University Xi’an Shaanxi People’s Republic of China

In recent times, ChatGPT has garnered significant interest from the public, sparking a range of reactions that encompass both aversion and appreciation. This paper delves into the paradoxical attitudes of individuals towards ChatGPT, highlighting the simultaneous existence of algorithmic aversion and appreciation. A comprehensive analysis is conducted from the vantage points of psychology and algorithmic decision-making, exploring the underlying causes of these conflicting attitudes from three dimensions: self-performance, task types, and individual factors. Subsequently, strategies to reconcile these opposing psychological stances are proposed, delineated into two categories: flexible coping and inflexible coping. In light of the ongoing advancements in artificial intelligence, this paper posits recommendations for the attitudes and actions that individuals ought to adopt in the face of artificial intelligence. Regardless of whether one exhibits algorithm aversion or appreciation, the paper underscores that coexisting with algorithms is an inescapable reality in the age of artificial intelligence, necessitating the preservation of human advantages.

关键词： ChatGPT contradictory psychology algorithm aversion algorithm appreciation algorithmic decision-making artificial intelligence

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Do we Reach Desired Disparate Impact with In-Processing Fairness Techniques?

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Procedia Computer Science 2022年 214卷 257-264页

作者： Sandro Radovanović Boris Delibasić Milija Suknović University of Belgrade - Faculty of Organizational Sciences Jove Ilića Street 154 Belgrade 11000 Serbia

Using machine learning algorithms in social environments and systems requires stricter and more detailed control. More specifically, the cost of error in such systems is much higher. Therefore, one should ensure that important decisions, such as whether to convict a person or not based on the previous criminal record, are by the legal requirements and not biased toward a group of people. One can find many many papers in the literature aimed at mitigating or eliminating unwanted bias in machine learning models. A significant part of these efforts add fairness constraint to the mathematical model or adds a regularization term to the loss function. In this paper, we show that optimizing the loss function given the fairness constraint or regularization for unfairness can surprisingly yield unfair solutions. This is due to the linear relaxation of the fairness function. By analyzing the gap between the true value of fairness and the one obtained using linear relaxation, we found that the gap can be as high as around 21% for the COMPAS dataset, and around 35% for the Adult dataset. In addition, we show that the fairness gap is consistent regardless of the strength of the fairness constraint or regularization.

关键词： algorithmic decision-making Fairness Disparate Impact Machine Learning Logistic Regression Fairness Gap

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What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness

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TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE 2023年第1期193卷

作者： Mahmud, Hasan Islam, A. K. M. Najmul Mitra, Ranjan Kumar LUT Univ LUT Sch Engn Sci Lappeenranta Finland Univ Dhaka Dhaka Bangladesh LUT Univ LUT Sch Engn Sci Software Engn Dept Lappeenranta 53850 Finland

Artificial intelligence has been increasingly used in many managerial decision-making contexts for its superior performance. However, many managers are still averse to using AI algorithms in decision-making. The benefits of algorithms cannot be realized optimally if managers are reluctant to use them. Although there are many studies investigating the factors that make an individual averse towards algorithms, studies examining such factors in the managerial decision-making context are scarce. This study, drawing on the innovation resistance theory and the technology readiness concept, proposes a structural model depicting relationships between managers' perceived barriers and algorithm aversion. The model also incorporates the role of managers' technology read-iness in influencing these relationships. To test the model, data were collected from 179 managers of 31 banks/ financial institutions in Bangladesh through an online survey and were analyzed using partial least squares structural equation modeling. The results indicate that barriers related to value, tradition, and image are significantly associated with algorithm aversion, whereas barriers related to usage and risk have a non-significant impact. Moderation analysis suggests that motivating factors of technology readiness mitigate the impact of value barriers on algorithm aversion.

关键词： Algorithm aversion Innovation resistance theory Technology readiness algorithmic decision-making Artificial intelligence

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