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Transparency is the degree to which something is clear. In the context of Information and Communication Technology (ICT), the term describes the need for clarity about how information is created, processed, managed, transmitted, stored, and shared.
Transparency helps stakeholders identify potential bottlenecks and areas for improvement. It also helps ensure accountability. When information workflows and decision-making processes are transparent, it becomes easier for stakeholders to locate the root cause of a problem and identify who is responsible for correcting it.
Essentially, transparency encourages trust and discourages corruption, waste, and abuse of power by creating a system of checks and balances. It makes it difficult for individuals and organizations to engage in corrupt practices unnoticed and creates a strong foundation for ethical behavior, collaboration, and responsible decision-making.
Importance
The need for ICT systems to be transparent is growing as compliance regulations around data protection, data privacy, and artificial intelligence (AI) become more stringent.
The General Data Protection Regulation (GDPR) regulation enacted by the European Union (EU), for example, requires organizations to be candid about how they collect, process, and store personal data.
To ensure the human-centric and ethical development of explainable AI (XAI) Artificial in Europe, members of the European Parliament (MEPs) endorsed new rules for AI systems by passing the EU AI Act and the Digital Services Act. The EU AI Act requires that AI-based systems be transparent in their functioning so that users can understand how decisions are taken and the logic behind them. The Digital Services Act requires Very Large Online Platforms (VLOPs) and Very Large Online Search Engines (VLOSEs) with more than 45 million monthly active users in the EU to publish annual transparency reports.
AI transparency is especially important because of the way AI systems can potentially impact society. Unlike traditional software applications, which use if/then rules to make decisions, the logic an AI system uses to make a decision can be very complex and not easily understood by humans.
Without transparency, when an AI system makes an incorrect or ethically questionable decision, it can be impossible to pinpoint where the error occurred and who should be held accountable.
Challenges and Advantages
While ICT transparency promotes trust, accountability, data-driven decision-making, and ethical behavior, achieving and maintaining a culture of transparency often requires a considerable amount of technical and legal expertise, time, and money.
In large organizations, it can be challenging to build and maintain a culture that minimizes data silos and information turf-guarding and ensures all stakeholders have access to the information they need to do their jobs and make good decisions.
It can require management to make strategic financial investments in robust data governance and data management systems and hire knowledgeable staff capable of navigating the complex and ever-changing requirements for transparency in regional laws and nation-state legislation.
It is not always easy to strike the right balance between the need for transparency and the need to protect intellectual property, but when it’s done right, all stakeholders will have a clear understanding of their organization’s transparency goals, expectations, and strategies for success.
This often has the added benefit of making them feel more connected to the organization and its mission, which in turn can create a positive feedback loop that leads to increased job satisfaction and additional motivation for valuing transparency.
Who Is Responsible for Transparency?
Everyone involved in an organization, irrespective of their role or position, is responsible for transparency.
C-suite executives and board members are tasked with setting the tone at the top by fostering a culture of openness within the organization. They are responsible for building a corporate governance structure that supports transparency and ensures that critical information is shared in a timely and accurate manner with all authorized stakeholders.
Managers and supervisors also carry significant responsibility for nurturing and maintaining transparency within the teams they manage. They need to make sure that team members have the necessary information to perform their jobs effectively, promote open communication, and discourage information hoarding.
Employees, in turn, are responsible for raising concerns when they encounter information silos or poorly documented technology that could negatively affect the organization’s performance or reputation.
When an organization’s entire culture values transparency, it builds a foundation of trust and integrity in data-driven operations and makes navigating the complex regulatory compliance landscape easier.
Areas of Concern
Compliance and the need for transparency in business sectors, like finance and healthcare, have inspired renewed attention to the need for transparency in a wide range of ICT concerns. They include:
- IT cost transparency: Focuses on planning, controlling, and optimizing the expenses associated with technology resources and services an organization uses.
- Supply chain transparency: Focuses on using smart contracts to track and verify the movement of tech purchases and provide accurate and real-time visibility into the supply chain process.
- Security transparency: Focuses on making sure an organization’s security policies and enforcement measures are transparent to users, administrators, and other stakeholders.
- Algorithmic transparency: Focuses on providing documentation for how machine learning (ML) algorithms work in order to prevent machine bias and ensure fairness and accountability.
- Data transparency: Focuses on providing visibility into data collection, processing, storage, and sharing practices. This includes informing individuals about what data is collected, how it is used, and with whom it is shared.
- System transparency: Focuses on making the inner workings of ICT systems and networks understandable to users, administrators, developers, and software engineers.
- Privacy transparency: Includes the development, implementation, and enforcement of privacy policies and data handling procedures to make it clear how personal and sensitive information is collected, used, and protected.
- AI transparency: Focuses on making AI technical components, system governance, and impact on society explainable.
- Accessibility transparency: Focuses on providing documentation that digital products and services are accessible to individuals with disabilities.
- Licensing transparency: Focuses on making sure software licensing terms and conditions are clear so that all users understand their rights and obligations.
- Version and revision control transparency: Focuses on maintaining transparency about changes, updates, and revisions to software.
- Audit trail transparency: Focuses on providing and maintaining audit trails that log and track activities, changes, and events within a system to facilitate accountability, compliance, and forensic analysis.
- Decision-making transparency: Focuses on ensuring transparency for how robotic process automation (RPA) and other narrow AI systems make decisions.
- Service level transparency: Focuses on creating service level agreements (SLAs) that are clear about acceptable service level performance metrics, uptimes, and response times for internal and external digital services.
- Configuration transparency: Focuses on creating and maintaining documentation that provides stakeholders with a clear view of all configuration changes and the current configuration state.
