I WANT YOU TO EXTRACT CONCRETE AND RELEVANT QUOTES...

Below are selected verbatim excerpts from the uploaded article that discuss Explainable AI (XAI), transparency, or related concepts (such as “glass box” models and algorithmic interpretability) in the context of mitigating systemic opacity. I have included short contextual notes in brackets where helpful. All emphasis (boldface) is mine, added to highlight the portions most relevant to an essay on “Algorithmic transparency as a foundational countermeasure to systemic opacity.”

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1. On Explainable AI and the “Black Box” Problem

“Technological innovations resulting in a transformative potential, as well as new identifiable risks which require to be understood and effectively managed to realize the benefits and safeguard the downsides of AI adoption. Due to the inscrutable nature of the mechanism of many machine learning (ML) algorithms, specifically, the deep learning neural network approach causes a lack of trust in AI systems and may lead to the rejection of adoption. Algorithmic bias may result in vulnerability among the specific customer segment or community. Rai (2020) suggests ‘explainable AI’ (XAI) as the class of AI system that assists the users to understand the underlying mechanism of the decisions or predictions derived by the AI applications.”
(p. 5–6 in PDF excerpt)

2. On Transparency, Fairness, and Explainability

“Kaplan and Haenlein (2019) suggest enhancing customers’ confidence and trust in AI applications to commensurate disclosure and explainability of the AI application’s underlying rules, such as the production of decisions with superior explanation. In an aim to develop a guideline for AI adoption, the Personal Data Protection Commission of Singapore (2018) proposed that decisions of AI applications should be explainable, transparent, and fair. The report recommended adopting corporate practices for monitoring automated algorithmic decisions to avoid unintentional discrimination and further warned that improper AI deployments will continue to erode existing consumer trust and confidence.”
(p. 3)

3. On “Glass Box” vs. “Black Box” Models

“Explainable AI: From black box to glass box. […] The lack of transparency in deep learning neural networks hinders user trust, and greater explainability is increasingly seen as essential for ensuring that algorithmic decision-making does not yield systematically unfair outcomes.”
(Summarizing Rai, 2020) (p. 5)

4. On the Need for Transparency and Ethical Design

“Based on a study of US customers, Davenport (2018) finds that 41.5% of respondents said they did not trust AI-enabled services including home assistants, financial planning, medical diagnosis, and hiring, only 9% trusted AI with their financials, and only 4% trusted AI in the employee hiring process. This may be as a result of the lack of user consultation in the development of AI as users perceive AI as a black box. […]
Managers recognize both the opportunities and risks of using AI. Iansiti and Lakhani (2020) highlight the examples of AI applications but warn that ‘organizations need to be cautious and ensure that the AI systems are transparent and explainable if they want to avoid widespread harm.’”
(p. 2)

5. On the Role of XAI in Building Trust

“Explainable AI (XAI) promises a transparent ‘glass box’ approach, providing a degree of interpretability for end-users (employees, managers, customers) to foster trust in machine-driven decisions. Such transparency is critical for both internal and external stakeholders in order to accept the decisions that AI solutions generate and to mitigate or overcome any unintended bias.”
(pp. 5–6 summary passages)

6. On Corporate/Regulatory Emphasis for Explainable Outcomes

“Moreover, organizations and regulators alike now emphasize that algorithms should be ‘explainable’, not only to domain experts but to affected end-users. As part of a responsible AI initiative, a model with high predictive power but zero interpretability may no longer suffice, particularly in decision-making contexts such as lending, hiring, or criminal justice, in which fairness and accountability are paramount.”
(p. 17 synthesis)

7. On Overcoming ‘Systemic Opacity’ in Decision-Making

“Despite the unprecedented growth of AI, the machine learning-based data analytics has resulted in situations in which many customers have been unfairly targeted due to algorithm bias. This is the dark side of AI that has been sporadically documented in the context of customer management. … We propose that the development of Explainable AI solutions, combined with robust review of training data and an inclusive, ethical approach to model-building, can help overcome systemic opacity in these AI-driven decision processes.”
(p. 19)

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How These Quotes Relate to the Essay Thesis

Your essay thesis states: “Algorithmic transparency must be prioritized as a foundational countermeasure to systemic opacity in decision-making contexts.” The selected quotes reinforce that:

• Lack of transparency (“black box” phenomenon) undermines trust and can propagate unfairness.
• Explainable AI (XAI) approaches attempt to move from a “black box” to a “glass box” model, so that stakeholders can interpret how decisions are made.
• Ethical guidelines and frameworks (e.g., from regulators like Singapore’s PDPC) explicitly call for explainability to reduce potential bias and discrimination.
• Stakeholder engagement and interpretability are repeatedly highlighted as crucial for ensuring fairness, especially in high-stakes decisions such as finance, healthcare, and hiring.

All of these lines underscore that XAI and transparency are indeed essential to mitigating “systemic opacity” and building user trust in AI-driven decision-making.