Ethical Guidelines for Solving Ethical Issues and Developing AI Systems

Balasubramaniam, Nagadivya; Kauppinen, Marjo; Kujala, Sari; Hiekkanen, Kari

doi:10.1007/978-3-030-64148-1_21

Cited by 12 publications

(8 citation statements)

References 24 publications

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“…There were only two organizations out of sixteen that did not emphasize transparency. The results of this paper support the findings of our previous study that were based on the analysis of ethical guidelines in three organizations [3]. The findings of our previous analysis were preliminary and they suggested that transparency, explainability, fairness, and privacy can be critical requirements of AI systems [3].…”

Section: Transparency and Explainability Guidelines In Practicesupporting

confidence: 87%

“…The number of organizations that are interested in developing AI systems are increasing. However, the black-box nature of AI systems has raised several ethical issues [3].…”

Section: Introductionmentioning

confidence: 99%

“…The ethical guidelines of AI developed by three established expert groups [16,20,25] emphasized transparency and explainability for developing AI systems. In addition to that, organizations have defined their own ethical guidelines of AI that encompass the ethical issues which are prominent to the organization [3].…”

Section: Introductionmentioning

confidence: 99%

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Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

Balasubramaniam

Kauppinen

Hiekkanen

et al. 2022

Lecture Notes in Computer Science

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Context and Motivation]Recent studies have highlighted transparency and explainability as important quality requirements of AI systems. However, there are still relatively few case studies that describe the current state of defining these quality requirements in practice.[Question] The goal of our study was to explore what ethical guidelines organizations have defined for the development of transparent and explainable AI systems. We analyzed the ethical guidelines in 16 organizations representing different industries and public sector.[Results] In the ethical guidelines, the importance of transparency was highlighted by almost all of the organizations, and explainability was considered as an integral part of transparency. Building trust in AI systems was one of the key reasons for developing transparency and explainability, and customers and users were raised as the main target groups of the explanations. The organizations also mentioned developers, partners, and stakeholders as important groups needing explanations. The ethical guidelines contained the following aspects of the AI system that should be explained: the purpose, role of AI, inputs, behavior, data utilized, outputs, and limitations. The guidelines also pointed out that transparency and explainability relate to several other quality requirements, such as trustworthiness, understandability, traceability, privacy, auditability, and fairness. [Contribution] For researchers, this paper provides insights into what organizations consider important in the transparency and, in particular, explainability of AI systems. For practitioners, this study suggests a structured way to define explainability requirements of AI systems.

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Section: Transparency and Explainability Guidelines In Practicesupporting

confidence: 87%

“…The number of organizations that are interested in developing AI systems are increasing. However, the black-box nature of AI systems has raised several ethical issues [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

Balasubramaniam

Kauppinen

Hiekkanen

et al. 2022

Lecture Notes in Computer Science

Self Cite

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“…The rapid evolution of AI and ML in these domains has prompted the need for ethical guidelines and privacy protection (Jobin & Ienca, 2019;Vellido, 2018;Naik et al, 2022;Anshari et al, 2022). As AI and ML become increasingly applicable to expert decision-making, it is essential to address ethical and privacy concerns to ensure effective human-AI collaboration (Cai et al, 2019;Bellamy et al, 2019;Stead, 2018;Fritsch et al, 2022;Balasubramaniam et al, 2020). The surge of interest in AI and ML techniques in healthcare and agriculture has raised concerns about algorithmic bias, transparency, and accountability (Bellamy et al, 2019;Fritsch et al, 2022;Dara et al, 2022;Huang et al, 2023).…”

Section: Emerging Trends and Future Directionsmentioning

confidence: 99%

The Evolving Role of Geospatial Intelligence in Enhancing Urban Security: A Review of Applications and Outcomes

Preye Winston Biu,

Johnson Sunday Oliha,

Ogagua Chimezie Obi

2024

Eng. sci. technol. j.

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Geospatial intelligence (GEOINT) has emerged as a pivotal tool in addressing the complexities of urban security, offering insights crucial for effective decision-making and resource allocation in the face of evolving threats. This review examines the multifaceted applications and outcomes of GEOINT in enhancing urban security, encompassing its utilization in surveillance, risk assessment, disaster management, and infrastructure protection. The proliferation of advanced satellite imaging, aerial photography, and geographic information systems (GIS) has revolutionized urban security efforts. Through the integration of various data sources, GEOINT enables the comprehensive mapping and monitoring of urban landscapes, facilitating the identification of vulnerable areas and potential security risks. This proactive approach empowers authorities to preemptively deploy resources and implement targeted interventions, thereby mitigating potential threats before they escalate. Moreover, GEOINT plays a pivotal role in enhancing situational awareness during emergencies and crises. By providing real-time updates on evolving situations, including natural disasters or terrorist activities, GEOINT facilitates rapid response and coordination among emergency responders. Furthermore, its ability to overlay spatial data with demographic and socio-economic information enables a nuanced understanding of the urban environment, ensuring more effective emergency management strategies tailored to specific community needs. Furthermore, GEOINT is instrumental in safeguarding critical urban infrastructure, such as transportation networks, power plants, and water supply systems, from potential security breaches. Through comprehensive spatial analysis, vulnerabilities within infrastructure networks can be identified, and security measures can be optimized to mitigate risks and ensure resilience against potential threats. This review also explores the emerging trends and future directions in the utilization of GEOINT for urban security, including the integration of artificial intelligence and machine learning algorithms for predictive analytics and anomaly detection. Additionally, the ethical and privacy implications associated with the widespread adoption of GEOINT in urban security are discussed, emphasizing the importance of responsible data governance and transparency. The evolving role of GEOINT in enhancing urban security underscores its indispensability in addressing contemporary security challenges. By leveraging spatial data and advanced analytics, GEOINT offers a comprehensive and proactive approach to safeguarding urban environments, thereby ensuring the safety and resilience of communities in an increasingly complex threat landscape. Keywords: Intelligence, Security, Geospatial, Technology, Review.

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“…The literature suggests that algorithmic bias and unfairness in financial decision-making lead to discriminatory practices, inaccurate financial risk assessments, and legal and regulatory challenges (Balasubramaniam et al, 2020;Long et al, 2021). Biased decisions, as noted by Ghazwani et al (2022), result in tangible consequences that negatively affect the financial performance of institutions, such as investment and trading losses.…”

Section: Literature Reviewmentioning

confidence: 99%

Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions

Mai,

Ambashe,

Ohueri

2024

International Journal of Information Technologies and Systems Approach

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Chinese financial institutions (CFIs) are increasingly embracing artificial intelligence (AI) for their financial decision-making driven by AI's capacity to mitigate risks and enhance efficiency and accuracy. However, there remain ethical challenges related to the integration of AI in financial decision-making. This study develops the AI ethics best practices model (AB-PraM) to mitigate ethical concerns and enhance the application of AI in financial decision-making. By employing a quantitative methodology, this research collected questionnaire data from 320 financial experts in CFIs. Structural equation modelling (SEM) was adopted to identify AI ethics best practices for the implementation of the AB-PraM. The findings of this research will mitigate AI ethics challenges in CFIs and provide a practical framework for transparent and accountable decision-making in alignment with ethical standards and regulations.

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Ethical Guidelines for Solving Ethical Issues and Developing AI Systems

Cited by 12 publications

References 24 publications

Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

Transparency and Explainability of AI Systems: Ethical Guidelines in Practice

The Evolving Role of Geospatial Intelligence in Enhancing Urban Security: A Review of Applications and Outcomes

Artificial Intelligence Ethics Best Practices Model for Financial Decision-Making in Chinese Financial Institutions

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