Quint C. Thurman and Phil Bogen
Looks at a project in Spokane, Washington in which two community police officers (CPOs) were assigned to two economically disadvantaged neighborhoods. Finds that there is a…
Abstract
Looks at a project in Spokane, Washington in which two community police officers (CPOs) were assigned to two economically disadvantaged neighborhoods. Finds that there is a continuing need for improving communication between police and the neighborhood residents and between residents themselves. Notes the importance of improving the image of law enforcement and encouraging residents to take an active role in solving crime‐related problems in their neighborhood. Says that CPOs enjoy their work and appear quite successful at it; that those who come into contact with CPOs appreciate them. Comments that the flexibility to interact with residents on a personal level and view problems from a local perspective seems to help in problem solving.
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CHEE H. WONG, GARY D. HOLT and PHIL HARRIS
The ‘lowest‐price wins’ philosophy has been a consistent theme of contractor selection over the years. To comprehensively elucidate this selection preference and compare it with…
Abstract
The ‘lowest‐price wins’ philosophy has been a consistent theme of contractor selection over the years. To comprehensively elucidate this selection preference and compare it with the use of a multi‐criteria selection (MCS) approach in the tenderer evaluation process, this paper investigates MCS tender price selection preferences. That is, project‐specific criteria (PSC) and lowest‐price wins selection practices of UK construction clients, in both building and civil engineering works at in detail via results of the empirical survey. The investigation provides further insight into the evaluation of contractors' attributes (i.e. PSC). Levels of importance assigned (LIA) for each criterion were analysed (i.e. quantitative analysis of the differences in opinions and, variance amongst the respondents) in a multivariate statistical method. Importance attached by construction clients to the ‘lowest‐price wins’ philosophy is also presented. Contrast was made between the MCS approach and the ‘lowest‐price wins’ option amongst the surveyed construction clients. It was found that increased awareness of the use of PSC prevailed amongst the survey construction clients. This indicated that cost has to be tempered with the evaluation of PSC and the attempt of construction clients searching for a new evaluation paradigm (i.e. adoption of MCS approach rather than basing on the lowest‐price wins alone).
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Biju P.R. and Gayathri O.
The purpose of this paper is to explore the challenges of implementing accountable artificial intelligence (AI) systems in India, focusing on the need for algorithms to justify…
Abstract
Purpose
The purpose of this paper is to explore the challenges of implementing accountable artificial intelligence (AI) systems in India, focusing on the need for algorithms to justify their decisions, especially in subjective and complex scenarios. By analyzing various government projects, documented biases and conducting empirical case studies and experiments, the study highlights the limitations of AI in recognizing the nuances of India’s unique social landscape. It aims to underscore the importance of integrating political philosophy to ensure that AI systems are held accountable within India’s sociopolitical context, urging policymakers to develop frameworks for responsible AI decision-making.
Design/methodology/approach
The research adopts a mixed-methods approach to address the five research questions. It begins with an extensive literature review, focusing on AI’s transformative potential, algorithmic bias and accountability in the Indian context. Data is collected from 15 AI use cases in health care, education and public safety, 13 government automated decision tools and five bias cases, including facial recognition and caste-based discrimination. Additionally, ten case studies and three experiments on ChatGPT are analyzed. Content analysis is used to interpret and categorize the data, identifying patterns and themes. Specific case studies and experiments on autocompletion in search engines further support the findings.
Findings
The study revealed significant limitations in current AI systems when applied to India’s complex socio-cultural landscape. Analyzing 15 AI applications and 13 government projects, the research identified multiple instances of algorithmic bias. Experiments with Google’s autocomplete and ChatGPT showed that these systems often reinforce social stereotypes and struggle with nuanced, subjective situations. The findings emphasize the accountability gap in AI-driven decisions, highlighting the need for rigorous oversight, particularly in welfare projects where errors could lead to severe consequences. The study recommends developing regulatory frameworks, improving AI design and raising public awareness to address these challenges.
Originality/value
In the context of complex societies like India, a pressing concern arises: who should assume responsibility for the repercussions stemming from algorithmic failures to comprehend subjective complexities? To this end, there exist no serious scholarly works toward which present paper tries to shed new insights. It draws upon insights from the corpus of political philosophy literature, encompassing both classical and contemporary notions of responsibility, and seeks to establish connections between these concepts and the unique sociopolitical structure of India. The work is unique in the focus of the paper and is original in the direction projected.