On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning

2020 ◽  
pp. 1-21
Author(s):  
Justin B. Biddle
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Value Judgments ◽  
Epistemic Risk ◽  
Prediction Algorithms ◽  
Recent Scholarship ◽  
Recidivism Prediction ◽  
Human Decision ◽  
Technological Decision Making

Abstract Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems.

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.

Rome, Religion, and Christian Emperors

2020 ◽  
pp. 107-139
Author(s):  
Mattias P. Gassman
Keyword(s):  
Decision Making ◽  
Religious Tolerance ◽  
Power Politics ◽  
The Public ◽  
Public Role ◽  
Recent Scholarship ◽  
Competing Ideas ◽  
Role Of Religion ◽  
Public Decision

The controversy over the altar of Victory shows how pagans and Christians expressed competing ideas on the public role of religion in an increasingly Christian empire. In 382, Gratian revoked funding from the Roman state priesthoods and removed the altar from the Senate house. Following Gratian’s death in 383, the Senate appealed to his brother, Valentinian II, through the urban prefect, Symmachus, whose communiqué was successfully countered by Ambrose of Milan. Recent scholarship has favoured Symmachus’ account, which it sees as an appeal for religious tolerance, and argued that the affair was decided by the power politics of a child emperor’s unstable court. In response, this chapter argues that Symmachus was actually trying to exclude the emperor’s Christianity from public decision-making. All religions may, for Symmachus, lead to God, but the old cults are Rome’s divinely appointed defence, as well as the bond between Senate and emperors. Ambrose put Valentinian’s duty to God at the heart of his appeal. Ambrose’s Senate contained many Christians, and Ambrose was bound to resist an emperor who endorsed pagan sacrifices (the closest either work comes to explicit political gamesmanship). Together, their works show how malleable Rome’s public religion still was, more than seventy years after Constantine embraced Christianity.

scholarly journals Predicting Mortality with Applied Machine Learning: Can We Get There?

2019 ◽  
Vol 8 (1) ◽  
pp. 115-119
Author(s):  
Emily S. Patterson ◽  
C.J. Hansen ◽  
Theodore T. Allen ◽  
Qiwei Yang ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Drug Administration ◽  
Patient Population ◽  
Food And Drug Administration ◽  
Hospital Environment ◽  
Clinical Settings ◽  
Applied Machine Learning

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are ‘ready yet’ for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

scholarly journals The role of time in human decision-making

2013 ◽  
Vol 13 (9) ◽  
pp. 305-305
Author(s):  
M. Popovic ◽  
M. Lengyel ◽  
J. Fiser
Keyword(s):  
Decision Making ◽  
Human Decision

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.

Supply to Deny: The Benefits of Nuclear Assistance for Nuclear Nonproliferation

2019 ◽  
Vol 5 (2) ◽  
pp. 282-298 ◽  
Author(s):  
Rebecca Davis Gibbons
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Nuclear Weapons ◽  
Nuclear Explosion ◽  
Nuclear Proliferation ◽  
Nuclear Nonproliferation ◽  
Nuclear Technology ◽  
Recent Scholarship ◽  
Nuclear Assistance

Abstract Recent scholarship on nuclear proliferation finds that many forms of nuclear assistance increase the odds that recipient states pursue nuclear weapons programs. While these studies may help us understand select cases of proliferation, they overshadow the role of nuclear supply in bolstering global nonproliferation efforts. After the risks of nuclear assistance became well-known following India's nuclear explosion in 1974, most major suppliers conditioned their assistance on recipients joining nonproliferation agreements. Case studies of states’ decision-making regarding these agreements illustrate how the provision of nuclear technology has been an effective tool in persuading states to join such agreements, the most important of which is the Treaty on the Non-Proliferation of Nuclear Weapons (NPT). By joining the NPT, states strengthen the global nonproliferation regime and increase the costs of any potential future decision to proliferate. The offer of nuclear assistance has done far more to bolster global nuclear nonproliferation efforts than recent research suggests.

The Role of Executive Function in Shaping Reinforcement Learning

2020 ◽  
Author(s):  
Milena Rmus ◽  
Samuel McDougle ◽  
Anne Collins
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Instrumental Behavior ◽  
Complex Environments ◽  
Neural Computations ◽  
Human Decision ◽  
Brain And Behavior ◽  
And Behavior ◽  
The Brain

Reinforcement learning (RL) models have advanced our understanding of how animals learn and make decisions, and how the brain supports some aspects of learning. However, the neural computations that are explained by RL algorithms fall short of explaining many sophisticated aspects of human decision making, including the generalization of learned information, one-shot learning, and the synthesis of task information in complex environments. Instead, these aspects of instrumental behavior are assumed to be supported by the brain’s executive functions (EF). We review recent findings that highlight the importance of EF in learning. Specifically, we advance the theory that EF sets the stage for canonical RL computations in the brain, providing inputs that broaden their flexibility and applicability. Our theory has important implications for how to interpret RL computations in the brain and behavior.

Complexity, Bounded Rationality and Heuristic Search

2002 ◽  
Vol 1 (1) ◽  
Author(s):  
W. Bentley MacLeod
Keyword(s):  
Decision Making ◽  
Rational Choice ◽  
Heuristic Search ◽  
Rational Choice Theory ◽  
Choice Theory ◽  
Heuristic Function ◽  
Heuristic Search Algorithms ◽  
Human Decision

Abstract This paper explores the use of heuristic search algorithms for modeling human decision making. It is shown that this algorithm is consistent with many observed behavioral regularities, and may help explain deviations from rational choice. The main insight is that the heuristic function can be viewed as formal implementation of one aspect of emotion as discussed in Descarte's Error by Antonio Damasio. Consistent with Damasio's observations, it is shown that the quality of decision making is very sensitive to the nature of the heuristic ("emotion"), and hence this may help us better understand the role of emotion in rational choice theory.

Tactical Considerations1

2020 ◽  
pp. 154-190
Author(s):  
Seth W. Stoughton ◽  
Jeffrey J. Noble ◽  
Geoffrey P. Alpert
Keyword(s):  
Decision Making ◽  
Use Of Force ◽  
Active Shooter ◽  
Community Members ◽  
Human Decision ◽  
Effects Of Stress ◽  
Police Tactics

Officers do not use force in a vacuum. It has long been recognized that a use of force is not the result of a single decision, but rather of “a contingent sequence of decisions and resulting behaviors—each increasing or decreasing the probability of an eventual use of … force.” How officers approach a situation, then, can affect whether and how they use force. Tactics are the techniques and procedures that officers use to protect themselves and community members. This chapter provides a framework for assessing police tactics, then offers an in-depth discussion of core tactical concepts. It explains why time is the single most important tactical consideration, details the effects of stress on human decision making, and illustrates how officers use tactical choices to “create time” and how they can use that time to minimize their need to use force. The chapter concludes by exploring the role of police tactics in three very different situations: arrests, crisis interventions, and active-shooter situations.

Emotions in Offender Decision Making

2017 ◽  
Author(s):  
Jean-Louis van Gelder
Keyword(s):  
Decision Making ◽  
Theoretical Framework ◽  
Cognitive Sciences ◽  
Offending Behavior ◽  
Human Decision ◽  
Understanding Emotions ◽  
Crime Causation

This chapter examines the influence of emotions on offender decision making. It reviews the empirical and theoretical criminological literature on the role of emotions in crime causation but also draws from other disciplines in the behavioral and cognitive sciences that have examined the influence of emotions on human decision making. Specific attention is devoted to appraisal theories of emotion, which, it is argued, provide a useful theoretical framework for studying and understanding emotions in criminal contexts. In doing so, it is shown that criminal decision-making research and theorizing may have so far failed to fully acknowledge the influence of emotions on offending behavior.

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