Understanding the Effect that Task Complexity has on Automation Potential and Opacity: Implications for Algorithmic Fairness

Scholars have increasingly focused on understanding different aspects of algorithms since they not only affect individual choices and decisions but also influence and shape societal structures. We can broadly categorize scholarly work on algorithms along the dimensions of economic gain that one achieves through automation and the ethical concerns that stem from such automation. However, the literature largely uses the notion of algorithms in a generic way and overlooks different algorithms’ specificity and the type of tasks that they perform. Drawing on a typology of tasks based on task complexity, we suggest that variations in the complexity of tasks contribute to differences in 1) their automation potential and 2) the opacity that results from their automation. We also suggest a framework to assess the likelihood that fairness concerns will emanate from automation of tasks with varying complexity. In this framework, we also recommend affordances for addressing fairness concerns that one may design into systems that automate different types of tasks

Improving Organizations by Replacing the "Mechanical" Model with the "Organic" one

Organizations are currently viewed as artificial structures. However, in our opinion, organizations seem to match a biological structure much better. This paper explores this new approach with some interesting conclusions and results: organizations aim at perpetual exis-tence and continuous adaptation. We advance the ideas of organizational "instincts", organizational pathology and organizational optimization using genetic algorithms. In competitive markets, organizations are in a natural selection process, which actually is part of a natural genetic algorithm. This process may be simulated in an artificial multidisciplinary optimization environment, based on minimizing a Total Costs and Risks objective function. Unlike the gradient optimization methods, the genetic algorithms may be applied to such problems with thousands of degrees of freedom. This opens the way to the organizational structure optimization through genetic algorithms.organization, genetic algorithms, multidisciplinary optimization, organizational analysis, organizational structure

Fairness Emergence in Reputation Systems

Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

Designing Fair AI for Managing Employees in Organizations: A Review, Critique, and Design Agenda

Organizations are rapidly deploying artificial intelligence (AI) systems to manage their workers. However, AI has been found at times to be unfair to workers. Unfairness toward workers has been associated with decreased worker effort and increased worker turnover. To avoid such problems, AI systems must be designed to support fairness and redress instances of unfairness. Despite the attention related to AI unfairness, there has not been a theoretical and systematic approach to developing a design agenda. This paper addresses the issue in three ways. First, we introduce the organizational justice theory, three different fairness types (distributive, procedural, interactional), and the frameworks for redressing instances of unfairness (retributive justice, restorative justice). Second, we review the design literature that specifically focuses on issues of AI fairness in organizations. Third, we propose a design agenda for AI fairness in organizations that applies each of the fairness types to organizational scenarios. Then, the paper concludes with implications for future research.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/4/AI Fairness Final to Online Feb 24 2020.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/1/AI Fairness Final to Online Feb 21 2020.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153812/6/Robert et al. 2020 AI Fairness New Proof.pdfDescription of AI Fairness Final to Online Feb 24 2020.pdf : Update Preprint Feb 24 2020Description of AI Fairness Final to Online Feb 21 2020.pdf : PreprintDescription of Robert et al. 2020 AI Fairness New Proof.pdf : Corrected Proof Mar 1 202

Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature

Algorithmic decision-making (ADM) 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 taking people's fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (a) algorithmic predictors, (b) human predictors, (c) comparative effects (human decision-making vs. algorithmic decision-making), and (d) consequences of ADM. 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 ADM

When Do Customers Perceive Artificial Intelligence as Fair? An Assessment of AI-based B2C E-Commerce

Artificial intelligence (AI) enables new opportunities for business-to-consumer (B2C) e-commerce services, but it can also lead to customer dissatisfaction if customers perceive the implemented service not to be fair. While we have a broad understanding of the concept of fair AI, a concrete assessment of fair AI from a customer-centric perspective is lacking. Based on systemic service fairness, we conducted 20 in-depth semi-structured customer interviews in the context of B2C e-commerce services. We identified 19 AI fairness rules along four interrelated fairness dimensions: procedural, distributive, interpersonal, and informational. By providing a comprehensive set of AI fairness rules, our research contributes to the information systems (IS) literature on fair AI, service design, and human-computer interaction. Practitioners can leverage these rules for the development and configuration of AI-based B2C e-commerce services