The agency of algorithms: Understanding human-algorithm interaction in administrative decision-making

2020 ◽  
Vol 25 (4) ◽  
pp. 507-522
Author(s):  
Rik Peeters
Keyword(s):  
Decision Making ◽  
Research Agenda ◽  
Computer Code ◽  
Decision Makers ◽  
Computer Algorithms ◽  
Academic Literature ◽  
Design Variables ◽  
Human Decision ◽  
Sources Of Variation ◽  
Algorithmic Design

With the rise of computer algorithms in administrative decision-making, concerns are voiced about their lack of transparency and discretionary space for human decision-makers. However, calls to ‘keep humans in the loop’ may be moot points if we fail to understand how algorithms impact human decision-making and how algorithmic design impacts the practical possibilities for transparency and human discretion. Through a review of recent academic literature, three algorithmic design variables that determine the preconditions for human transparency and discretion and four main sources of variation in ‘human-algorithm interaction’ are identified. The article makes two contributions. First, the existing evidence is analysed and organized to demonstrate that, by working upon behavioural mechanisms of decision-making, the agency of algorithms extends beyond their computer code and can profoundly impact human behaviour and decision-making. Second, a research agenda for studying how computer algorithms affect administrative decision-making is proposed.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

scholarly journals Emergence of Coordination in Growing Decision-Making Organizations: The Role of Complexity, Search Strategy, and Cost of Effort

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-26
Author(s):  
Friederike Wall
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
Search Strategy ◽  
Decision Makers ◽  
Agent Based ◽  
Distributed Decision Making ◽  
Human Decision ◽  
Nk Fitness Landscapes ◽  
The Cost ◽  
Different Levels

Coordination among decision-makers of an organization, each responsible for a certain partition of an overall decision-problem, is of crucial relevance with respect to the overall performance obtained. Among the challenges of coordination in distributed decision-making systems (DDMS) is to understand how environmental conditions like, for example, the complexity of the decision-problem to be solved, the problem’s predictability and its dynamics shape the adaptation of coordination mechanisms. These challenges apply to DDMS resided by human decision-makers like firms as well as to systems of artificial agents as studied in the domain of multiagent systems (MAS). It is well known that coordination for increasing decision-problems and, accordingly, growing organizations is in a particular tension between shaping the search for new solutions and setting appropriate constraints to deal with increasing size and intraorganizational complexity. Against this background, the paper studies the adaptation of coordination in the course of growing decision-making organizations. For this, an agent-based simulation model based on the framework of NK fitness landscapes is employed. The study controls for different levels of complexity of the overall decision-problem, different strategies of search for new solutions, and different levels of cost of effort to implement new solutions. The results suggest that, with respect to the emerging coordination mode, complexity subtly interferes with the search strategy employed and cost of effort. In particular, results support the conjecture that increasing complexity leads to more hierarchical coordination. However, the search strategy shapes the predominance of hierarchy in favor of granting more autonomy to decentralized decision-makers. Moreover, the study reveals that the cost of effort for implementing new solutions in conjunction with the search strategy may remarkably affect the emerging form of coordination. This could explain differences in prevailing coordination modes across different branches or technologies or could explain the emergence of contextually inferior modes of coordination.

scholarly journals Between technochauvinism and human-centrism: Can algorithms improve decision-making in democratic politics?

2021 ◽  
Author(s):  
Pascal D. König ◽  
Georg Wenzelburger
Keyword(s):  
Decision Making ◽  
Policy Instruments ◽  
Decision Makers ◽  
Democratic Politics ◽  
Cognitive Tasks ◽  
Political Decision ◽  
Human Decision ◽  
Established Democracies ◽  
Political Decision Making ◽  
Selection Of

AbstractThe promise of algorithmic decision-making (ADM) lies in its capacity to support or replace human decision-making based on a superior ability to solve specific cognitive tasks. Applications have found their way into various domains of decision-making—and even find appeal in the realm of politics. Against the backdrop of widespread dissatisfaction with politicians in established democracies, there are even calls for replacing politicians with machines. Our discipline has hitherto remained surprisingly silent on these issues. The present article argues that it is important to have a clear grasp of when and how ADM is compatible with political decision-making. While algorithms may help decision-makers in the evidence-based selection of policy instruments to achieve pre-defined goals, bringing ADM to the heart of politics, where the guiding goals are set, is dangerous. Democratic politics, we argue, involves a kind of learning that is incompatible with the learning and optimization performed by algorithmic systems.

scholarly journals The Effects of Situational and Individual Factors on Algorithm Acceptance in COVID-19-Related Decision-Making: A Preregistered Online Experiment

2021 ◽  
Vol 3 ◽  
pp. 27-46
Author(s):  
Sonja Utz ◽  
Lara Wolfers ◽  
Anja Göritz
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Individual Factors ◽  
Online Experiment ◽  
Human Decision ◽  
Attitudes And Knowledge

In times of the COVID-19 pandemic, difficult decisions such as the distribution of ventilators must be made. For many of these decisions, humans could team up with algorithms; however, people often prefer human decision-makers. We examined the role of situational (morality of the scenario; perspective) and individual factors (need for leadership; conventionalism) for algorithm preference in a preregistered online experiment with German adults (n = 1,127). As expected, algorithm preference was lowest in the most moral-laden scenario. The effect of perspective (i.e., decision-makers vs. decision targets) was only significant in the most moral scenario. Need for leadership predicted a stronger algorithm preference, whereas conventionalism was related to weaker algorithm preference. Exploratory analyses revealed that attitudes and knowledge also mattered, stressing the importance of individual factors.

Decision-Making Cognitive Process

2018 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Thais Spiegel ◽  
Ana Carolina P V Silva
Keyword(s):  
Decision Making ◽  
Cognitive Process ◽  
Decision Makers ◽  
Time Constraints ◽  
Decision Making Process ◽  
Cognitive Limitations ◽  
The World ◽  
Human Decision ◽  
Per Se ◽  
Rationality Theory

In the study of decision-making, the classical view of behavioral appropriateness or rationality was challenged by neuro and psychological reasons. The “bounded rationality” theory proposed that cognitive limitations lead decision-makers to construct simplified models for dealing with the world. Doctors' decisions, for example, are made under uncertain conditions, as without knowing precisely whether a diagnosis is correct or whether a treatment will actually cure a patient, and often under time constraints. Using cognitive heuristics are neither good nor bad per se, if applied in situations to which they have been adapted to be helpful. Therefore, this text contextualizes the human decision-making perspective to find descriptions that adhere more closely to the human decision-making process. Then, based on a literature review of cognition during decision-making, particularly in healthcare context, it addresses a model that identifies the roles of attention, categorization, memory, emotion, and their inter-relations, during the decision-making process.

Searching for Herbert Simon

2013 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Michael F. Gorman ◽  
Donald E. Wynn ◽  
William David Salisbury
Keyword(s):  
Decision Making ◽  
Analytical Techniques ◽  
Decision Makers ◽  
Business Analytics ◽  
Herbert Simon ◽  
Business Decision ◽  
Wide Range ◽  
Human Decision ◽  
Potential Impact ◽  
Effective Use

Since Herbert Simon’s seminal work (Simon, 1957) on bounded rationality researchers and practitioners have sought the “holy grail” of computer-supported decision-making. A recent wave of interest in “business analytics” (BA) has elevated interest in data-driven analytical decision making to the forefront. While reporting and prediction via business intelligence (BI) systems has been an important component to business decision making for some time, BA broadens its scope and potential impact in business decision making further by moving the focus to prescription. The authors see BA as the end-to-end process integrating the production through consumption of the data, and making more extensive use of the data through heavily automated, integrated and advanced predictive and prescriptive tools in ways that better support, or replace, the human decision maker. With the advent of “big data”, BA already extends beyond internal databases to external and unstructured data that is publicly produced and consumed data with new analytical techniques to better enable business decision makers in a connected world. BI research in the future will be broader in scope, and the challenge is to make effective use of a wide range of data with varying degrees of structure, and from sources both internal and external to the organization. In this paper, we suggest ways that this broader focus of BA will also affect future BI research streams.

Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach

2014 ◽  
Vol 29 (2) ◽  
Author(s):  
Stanko Dimitrov
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Makers ◽  
Data Envelopment ◽  
Rank Ordering ◽  
Decision Making Unit ◽  
Human Decision ◽  
Survey Approach ◽  
Decision Making Units ◽  
Better Than

AbstractIn this paper we compare the ordinal rankings generated through Data Envelopment Analysis (DEA) methods to ordinal rankings generated by human decision makers. Through eliciting the total rank ordering for approximately 100 individuals on all of the four different datasets of Decision Making Units (DMUs), we compare the rankings generated by individuals to those generated by ten DEA methods. We observe that depending on the characteristics of the dataset one of the DEA methods performs better than the others in matching human decision makers.

Decision-Making

2019 ◽  
pp. 104-125
Author(s):  
Karl E. Misulis ◽  
Mark E. Frisse
Keyword(s):  
Decision Making ◽  
Large Data ◽  
Computer Algorithms ◽  
Human Judgment ◽  
Critical Events ◽  
Important Data ◽  
Human Decision ◽  
Computer Based ◽  
Recorded Data ◽  
Data Collections

Decision-making consists of the logical application of information. Human decision-making and computer-based decision-making are different. Human decision-making relies on nuances and impressions that are not represented in recorded data, and they are subject to behavioral and other factors not represented in the sterile logic of computer algorithms. Computers are capable of analyzing large data collections, ensuring that important data are not overlooked, reacting instantly to critical events, and operating in ways that ensure consistent processes. Computers therefore assist in decision-making but rarely substitute for human judgment. This chapter concerns the logical aspects of decision-making, including statistics and logic theory.

scholarly journals Evidence-informed decision making for nutrition: African experiences and way forward

2017 ◽  
Vol 76 (4) ◽  
pp. 589-596 ◽  
Author(s):  
Richmond Aryeetey ◽  
Michelle Holdsworth ◽  
Christine Taljaard ◽  
Waliou Amoussa Hounkpatin ◽  
Esi Colecraft ◽  
...  
Keyword(s):  
Decision Making ◽  
Research Agenda ◽  
Cost Benefit ◽  
Informed Decision ◽  
Decision Makers ◽  
Informed Decision Making ◽  
Structured Interviews ◽  
Country Level ◽  
Stakeholder Influence ◽  
Nutrition Research

Although substantial amount of nutrition research is conducted in Africa, the research agenda is mainly donor-driven. There is a clear need for a revised research agenda in Africa which is both driven by and responding to local priorities. The present paper summarises proceedings of a symposium on how evidence can guide decision makers towards context-appropriate priorities and decisions in nutrition. The paper focuses on lessons learnt from case studies by the Evidence Informed Decision Making in Nutrition and Health Network implemented between 2015 and 2016 in Benin, Ghana and South Africa. Activities within these countries were organised around problem-oriented evidence-informed decision-making (EIDM), capacity strengthening and leadership and horizontal collaboration. Using a combination of desk-reviews, stakeholder influence-mapping, semi-structured interviews and convening platforms, these country-level studies demonstrated strong interest for partnership between researchers and decision makers, and use of research evidence for prioritisation and decision making in nutrition. Identified capacity gaps were addressed through training workshops on EIDM, systematic reviews, cost–benefit evaluations and evidence contextualisation. Investing in knowledge partnerships and development of capacity and leadership are key to drive appropriate use of evidence in nutrition policy and programming in Africa.

scholarly journals Human-AI Collaboration with Bandit Feedback

2021 ◽  
Author(s):  
Ruijiang Gao ◽  
Maytal Saar-Tsechansky ◽  
Maria De-Arteaga ◽  
Ligong Han ◽  
Min Kyung Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Recent Work ◽  
Team Performance ◽  
Decision Makers ◽  
Human Decision ◽  
Classification Tasks ◽  
Personalized Routing

Human-machine complementarity is important when neither the algorithm nor the human yield dominant performance across all instances in a given domain. Most research on algorithmic decision-making solely centers on the algorithm's performance, while recent work that explores human-machine collaboration has framed the decision-making problems as classification tasks. In this paper, we first propose and then develop a solution for a novel human-machine collaboration problem in a bandit feedback setting. Our solution aims to exploit the human-machine complementarity to maximize decision rewards. We then extend our approach to settings with multiple human decision makers. We demonstrate the effectiveness of our proposed methods using both synthetic and real human responses, and find that our methods outperform both the algorithm and the human when they each make decisions on their own. We also show how personalized routing in the presence of multiple human decision-makers can further improve the human-machine team performance.

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