scholarly journals Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 199 ◽  
Author(s):  
Yash Raj Shrestha ◽  
Yongjie Yang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Recommender Systems ◽  
Minority Groups ◽  
Ethical Issues ◽  
Fixed Number ◽  
Future Research ◽  
Design Of Algorithms ◽  
Voting Machine

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.

Alignment for Advanced Machine Learning Systems

2020 ◽  
pp. 342-382
Author(s):  
Jessica Taylor ◽  
Eliezer Yudkowsky ◽  
Patrick LaVictoire ◽  
Andrew Critch
Keyword(s):  
Machine Learning ◽  
Side Effects ◽  
Objective Function ◽  
Unintended Consequences ◽  
Learning Systems ◽  
Large Impact ◽  
Future Research ◽  
Objective Functions ◽  
Research Areas ◽  
The Right

This chapter surveys eight research areas organized around one question: As learning systems become increasingly intelligent and autonomous, what design principles can best ensure that their behavior is aligned with the interests of the operators? The chapter focuses on two major technical obstacles to AI alignment: the challenge of specifying the right kind of objective functions and the challenge of designing AI systems that avoid unintended consequences and undesirable behavior even in cases where the objective function does not line up perfectly with the intentions of the designers. The questions surveyed include the following: How can we train reinforcement learners to take actions that are more amenable to meaningful assessment by intelligent overseers? What kinds of objective functions incentivize a system to “not have an overly large impact” or “not have many side effects”? The chapter discusses these questions, related work, and potential directions for future research, with the goal of highlighting relevant research topics in machine learning that appear tractable today.

scholarly journals Algorithmic Opacity: Making Algorithmic Processes Transparent through Abstraction Hierarchy

2018 ◽  
Vol 62 (1) ◽  
pp. 192-196
Author(s):  
Pragya Paudyal ◽  
B.L. William Wong
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Intelligence Analysis ◽  
Functional Relationships ◽  
Decomposition Space ◽  
Criminal Intelligence

In this paper we introduce the problem of algorithmic opacity and the challenges it presents to ethical decision-making in criminal intelligence analysis. Machine learning algorithms have played important roles in the decision-making process over the past decades. Intelligence analysts are increasingly being presented with smart black box automation that use machine learning algorithms to find patterns or interesting and unusual occurrences in big data sets. Algorithmic opacity is the lack visibility of computational processes such that humans are not able to inspect its inner workings to ascertain for themselves how the results and conclusions were computed. This is a problem that leads to several ethical issues. In the VALCRI project, we developed an abstraction hierarchy and abstraction decomposition space to identify important functional relationships and system invariants in relation to ethical goals. Such explanatory relationships can be valuable for making algorithmic process transparent during the criminal intelligence analysis process.

scholarly journals END-OF-LIFE INFORMATION PREFERENCES OF ASIAN OLDER ADULTS: A SYSTEMATIC REVIEW OF THE LITERATURE

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S412-S412
Author(s):  
Kristina Shiroma ◽  
Nathan Davis ◽  
Bo Xie
Keyword(s):  
Older Adults ◽  
Decision Making ◽  
Ethnic Minority ◽  
End Of Life ◽  
Minority Groups ◽  
Future Research ◽  
Ethnic Minority Groups ◽  
Information Preferences ◽  
Cultural Aspects ◽  
Mesh Terms

Abstract Older adults of Asian ethnic minority groups are often underrepresented in the literature on cultural aspects of end-of-life (EOL) decision making. This literature review aimed to systematically investigate the cultural aspects of EOL decision making for aging adults of Asian ethnic minority groups. In February 2019, systematic searches were conducted in PubMed using MeSH terms “end-of-life”, “decision-making”, and “culture OR cultural”. Articles with human subjects, full text in English, published in the past 10 years, with original, empirical findings were included. After multiple rounds of screening, the final sample included 22 results, with sample sizes ranging from 11 to over 9 millions representing South Asian, Chinese, Korean, Taiwanese, Singaporean, Asian and Asian/Pacific adults. The findings suggest the literature on older Asian adults is present, but limited. Future research is needed to explore cultural aspects of Asian ethnic minority groups in respect to older adult’s information preferences in EOL decision-making.

scholarly journals AI in learning

2021 ◽  
Vol 15 ◽  
pp. 183449092110381 ◽  
Author(s):  
Hannele Niemi
Keyword(s):  
Machine Learning ◽  
Ethical Issues ◽  
Cognitive Task ◽  
Future Research ◽  
Human Beings ◽  
Special Issue ◽  
Recent Discussion ◽  
Data Ownership ◽  
Digital Pedagogy ◽  
Intelligent Tools

This special issue raises two thematic questions: (1) How will AI change learning in the future and what role will human beings play in the interaction with machine learning, and (2), What can we learn from the articles in this special issue for future research? These questions are reflected in the frame of the recent discussion of human and machine learning. AI for learning provides many applications and multimodal channels for supporting people in cognitive and non-cognitive task domains. The articles in this special issue evidence that agency, engagement, self-efficacy, and collaboration are needed in learning and working with intelligent tools and environments. The importance of social elements is also clear in the articles. The articles also point out that the teacher’s role in digital pedagogy primarily involves facilitating and coaching. AI in learning has a high potential, but it also has many limitations. Many worries are linked with ethical issues, such as biases in algorithms, privacy, transparency, and data ownership. This special issue also highlights the concepts of explainability and explicability in the context of human learning. We need much more research and research-based discussion for making AI more trustworthy for users in learning environments and to prevent misconceptions.

scholarly journals Application of Twin Objective Function SVM in Sentiment Analysis

2020 ◽  
Author(s):  
Qiaoman Yang ◽  
Chunyu Liu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Objective Function ◽  
Sentiment Analysis ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Key Issues ◽  
Single Objective ◽  
Accuracy And Stability

Classification modeling is one of the key issues in sentiment analysis. Support vector machine (SVM) has been widely used in classification as an effective machine learning method. Generally, a common SVM is only for decision-making that sacrifices the distribution of data. In practice, sentiment data are big and mazy, which results in the deficiency of accuracy and stability when common SVM is used. The study investigates sentiment analysis by applying the twin objective function SVM, including nonparallel SVM(NPSVM) and twin SVM (TWSVM). From the experiments, we concluded that twin objective function SVMs are superior to NB and single objective function SVM in accuracy and stability.

Ethical Issues Encountered by Military Nurses During Wartime

2016 ◽  
Vol 34 (1) ◽  
pp. 227-246 ◽  
Author(s):  
Janice Agazio ◽  
Petra Goodman ◽  
Oluwakemi Opanubi ◽  
Patricia McMullen
Keyword(s):  
Decision Making ◽  
Ethical Issues ◽  
Code Of Ethics ◽  
Ethical Dilemmas ◽  
Daily Practice ◽  
Professional Behavior ◽  
Future Research ◽  
Ethical Practice ◽  
Military Nurses ◽  
The Military

Military nurses encounter similar issues as civilian nurses in daily practice situations; however, wartime and humanitarian missions may bring unique and difficult ethical dilemmas. While nursing has the American Nurses Association code of ethics to provide a framework to guide ethical practice decisions, conflicts may arise from the unique aspects of nursing within a wartime environment. Understanding those conflicts occuring within the military wartime scenario can provide nurses with experiential examples from which to derive strategies for personal coping and professional behavior and decision making. This chapter describes the research that has focused upon the identification of these issues, the effects from uresolved issues, and those directions for future research to better prepare miltiary nurses before and during deployment.

scholarly journals Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Wajid Hassan ◽  
Te-Shun Chou ◽  
Omar Tamer ◽  
John Pickard ◽  
Patrick Appiah-Kubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cloud Computing ◽  
Data Science ◽  
Service Level Agreement ◽  
Resource Scarcity ◽  
Cloud Services ◽  
Future Research ◽  
Intelligent Decision Making ◽  
Intelligent Decision

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

Neuromarketing Trends and Opportunities for Companies

2020 ◽  
pp. 82-103
Author(s):  
Arabinda Bhandari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Decision Making ◽  
Brand Management ◽  
Technological Development ◽  
Research Direction ◽  
Future Research ◽  
Design Decision ◽  
Future Research Direction

The main purpose of this chapter is to concisely describe the origin of neuromarketing, its applications in the organization, and to explore consumer behavior with the help of different neuromarketing technologies like fMRI, EEG, and MEG. This chapter gives a guideline on how neuromarketing would be used in different areas of organization functions, like, brand management, advertisement, communication, product design, decision making, etc. with the help of data mining, artificial intelligence, social media, machine learning, remote sensing, AR, and VR. The chapter identifies the opportunities of neuromarketing with the latest technological development to understand the customer mindset so that it would be easy to formulate neurostrategy for an organization. This chapter gives a future research direction with strategic management, so that it will be helpful for a professional to create a more accurate strategy in a VUCA (volatility, uncertainty, complexity, ambiguity) environment, predict, and fulfill the “institution void” situation with more accuracy in an emerging developing market.

scholarly journals Implicit Estimation of Paragraph Relevance From Eye Movements

2022 ◽  
Vol 3 ◽  
Author(s):  
Michael Barz ◽  
Omair Shahzad Bhatti ◽  
Daniel Sonntag
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Feature Extraction ◽  
Eye Movements ◽  
Relevance Feedback ◽  
User Study ◽  
Future Research ◽  
Perceived Relevance ◽  
Difficult Cases ◽  
Effective Source

Eye movements were shown to be an effective source of implicit relevance feedback in constrained search and decision-making tasks. Recent research suggests that gaze-based features, extracted from scanpaths over short news articles (g-REL), can reveal the perceived relevance of read text with respect to a previously shown trigger question. In this work, we aim to confirm this finding and we investigate whether it generalizes to multi-paragraph documents from Wikipedia (Google Natural Questions) that require readers to scroll down to read the whole text. We conduct a user study (n = 24) in which participants read single- and multi-paragraph articles and rate their relevance at the paragraph level with respect to a trigger question. We model the perceived document relevance using machine learning and features from the literature as input. Our results confirm that eye movements can be used to effectively model the relevance of short news articles, in particular if we exclude difficult cases: documents which are on topic of the trigger questions but irrelevant. However, our results do not clearly show that the modeling approach generalizes to multi-paragraph document settings. We publish our dataset and our code for feature extraction under an open source license to enable future research in the field of gaze-based implicit relevance feedback.

Investigating Market Research Ethics

2016 ◽  
Vol 58 (3) ◽  
pp. 381-400
Author(s):  
Anca C. Yallop ◽  
Simon Mowatt
Keyword(s):  
Decision Making ◽  
Ethical Decision ◽  
Ethical Decision Making ◽  
Ethical Issues ◽  
Marketing Research ◽  
Codes Of Ethics ◽  
Future Research ◽  
Research Approach ◽  
Ethical Behaviour ◽  
Client Relationships

In academic and practitioner literature, codes of ethics are generally understood to act as a mechanism guiding and ensuring ethical behaviour. However, this premise has not yet been thoroughly explored. Using a qualitative research approach this study examines the tools used in ethical decision-making by New Zealand marketing research practitioners, with a focus on client relationships. Participants reported on their awareness, familiarity, and use of professional and organisational codes of ethics. In particular, information was sought on how ethical issues were dealt with when they arose in their relationships with clients. This empirical research focused on the effects of different variables and emerging constructs, and the interplay between them, on ethical decision-making in client relationships. The paper concludes with a discussion of research contributions, implications for the practice of marketing research, and future research opportunities.

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