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Theories and concepts of natural resource governance
Published in Felix Danso, Mineral Resource Governance and Human Development in Ghana, 2020
Accountability refers to the allocation and acceptance of responsibility for decisions and actions as well as the demonstration of whether and how these responsibilities have been met. Accountability is an issue for mineral governance in contexts where the effectiveness of decision-making processes is essential for their authority and credibility. Where accountability is unrealisable through direct democratic involvement and is more informal, the need of citizens for proper access to information, meaningful consultation, and for enhanced opportunities for active participation become more significant. Compliance with regulatory requirements is an important component of good governance for a public entity. Accountability in the mining sector also requires compliance, which means the extent to which governments and other actors in the mining sector observe relevant legislations, standards and codes and have a compliance programme that is integrated with their operational and financial plans; systems to monitor conformity, such as internal and external audits; and processes to meet external reporting requirements. Reporting requirements should be the minimum necessary to provide financial, governance and performance accountability (Lockwood et al., 2010).
Managing the SMS
Published in Alan J. Stolzer, Carl D. Halford, John J. Goglia, Safety Management Systems in Aviation, 2018
Alan J. Stolzer, Carl D. Halford, John J. Goglia
After the safety goals and objectives are established, management must establish a plan for accountability. Many people confuse the terms accountability and blame; these terms have substantively different meanings. In practice, accountability means that someone is responsible and answerable for an activity. This means that someone may need to produce an account of their knowledge of the activity in question. That accounting may be used for understanding the circumstances of that activity and, hopefully, used to improve the system that produced that activity.
Managing the SMS
Published in Alan J. Stolzer, John J. Goglia, Safety Management Systems in Aviation, 2016
Alan J. Stolzer, John J. Goglia
After the safety goals and objectives are established, management must establish a plan for accountability. Many people confuse the terms accountability and blame; these terms have substantively different meanings. In practice, accountability means that someone is responsible and answerable for an activity. This means that someone may need to produce an account of their knowledge of the activity in question. That accounting may be used for understanding the circumstances of that activity and, hopefully, used to improve the system that produced that activity.
Managing the waste of over processing in healthcare using accountability through utilization reviews and information technologies
Published in Quality Management Journal, 2022
John Wallace Gardner, Sarah Childs
This article focuses on some of the most prominent mechanisms in healthcare policy and initiatives that may help address over processing. Many people believe that accountability can improve healthcare service while reducing unnecessary costs and patient length of stay. Accountability, as defined in the psychology literature, “refers to the implicit or explicit expectation that one may be called on to justify one’s beliefs, feelings, and actions to others” (Lerner and Tetlock 1999, 255; Scott and Lyman 1968; Semin and Manstead 1983). Accountability generally includes negative or positive consequences and the justifications for the actions that led to the outcomes. In this article, the authors will refer to accountability as having ownership or responsibility for one’s actions and related performance or outcomes.
Critical review of tailings dam monitoring best practice
Published in International Journal of Mining, Reclamation and Environment, 2020
The stakeholder’s perception of the risk associated with any project is influenced by their values, needs, assumptions, and concerns. ‘The more hazardous a stakeholder perceives the risk to be, the greater the communication and consultation challenge your organisation will face’ [17]. Transparency from the organisation throughout the entire project encourages trust, respect, credibility, and understanding of the risks that exist, and how these are being managed. From the engineer’s perspective, engaging with stakeholders drives engineering perspective beyond objective risk management to understand complex attitudes and perceptions, signifying the full reach of accountability that the engineer has on the project. Mutual understanding increases stakeholder confidence, and paves the way for progression and advancement.
Human-centred artificial intelligence: a contextual morality perspective
Published in Behaviour & Information Technology, 2022
Niels van Berkel, Benjamin Tag, Jorge Goncalves, Simo Hosio
Fairness, Accountability and Transparency (frequently abbreviated as FAT*) have emerged as important considerations in AI. Lepri et al. (2018) define fairness as ‘the lack of discrimination or bias in decision making’. The authors identify a diverse range of perspectives on how fairness can be achieved, which include among others ‘group fairness’ (each group in the dataset should receive an equal fraction of each possible outcome) (Calders and Verwer 2010), ‘individual fairness’ (similar people should be treated similarly) (Dwork et al. 2012) and ‘equality of opportunity’ (people of equal talent and motivation should be offered the same perspectives regardless of their place in the current social system) (Hardt, Price, and Srebro 2016). Accountability is defined as the ‘assumption of accepting the responsibility for actions and decisions’ (Lepri et al. 2018). Although the literature describes a number of ways to achieve accountability (see e.g. Kroll et al. 2016; Veale, Van Kleek, and Binns 2018), transparency is often seen as a key enabler of accountability in AI. Lepri et al.'s definition of transparency describes it as the ‘openness and communication of both the data being analysed and the mechanisms underlying the models’ (Lepri et al. 2018). Transparency of a model can be achieved at a number of levels, most notably at the level of the model (i.e. grasping the model's workings), at a component level (i.e. parameters and computation can be intuitively explained) or at the algorithmic level (Lepri et al. 2018). Lack of either one of the aforementioned factors can severely hamper a user's understanding of the inner workings of an application (Eslami et al. 2015).