Transparency is an ethical principle that encourages openness and communication while at the same time guaranteeing accountability to comply with established laws and regulations.
Effective regulation is vital to the responsible deployment and transparency of artificial intelligence technologies. Finding a balance between encouraging innovation and placing restrictions can create an AI system which is trustworthy and accountable.
Transparency in AI Decision-Making Processes
AI transparency ensures ethical use and can expose any biases. This is particularly crucial when making critical decisions, such as AI-based assistive technologies for people with disabilities where multiple actors with differing capacities interact (Kuner & Edwards 2017). Transparency also proves invaluable in meeting accessibility and inclusion challenges (AIAA 2018).
Providing users with adequate transparency requires informing them about what data an algorithm will be using, alerting them that they are engaging with an AI system, and explaining its operation. In addition, the EU AI Act contains transparency requirements intended to improve data management, enhance security measures, detect or mitigate potential harm caused by models’ designs or deployment.
One of the key challenges in AI decision-making is determining how much information should be provided. This decision depends on the severity of each task; for instance, providing reasoning mechanisms is key when performing life-critical decisions such as cancer detection – where even one percent error could lead to fatal results.
Provide the appropriate level of transparency to avoid the perils of over-transparency, which could result in the abuse of an AI by powerful entities with monopolistic control. Commons-based peer production that emphasizes collaboration and decentralized approaches to open data and algorithms is one way of mitigating this risk.
Transparency in AI Data
AI models can often seem opaque. But they don’t have to be. Explainable AI (XAI) and transparent AI can help people better understand how algorithms arrive at certain conclusions, making it easier for people to trust the results of machine-driven decisions.
Transparency allows companies to avoid miscommunication between employees and customers regarding how an algorithm operates, as well as making it simpler to identify any defects within the system and communicate any shortcomings to stakeholders.
Transparency can be achieved in various ways, such as creating a central database to track all AI systems used across your company and communicate any model updates to employees, and training employees more closely on how AI systems operate will increase awareness among users and allow them to identify any pitfalls more quickly.
Transparency requires that we feed into our model the appropriate data set. By providing clean, well-rounded and impartial data sets that eliminate biases in the model and increase its accuracy.
Thirdly, transparent processes and policies relating to AI development, testing and implementation must be fully disclosed – this includes providing documentation of significant decisions taken along the way and setting governance protocols around how an AI system is constructed, managed and deployed.
Transparency in AI Models
As AI models become more complex, users find it increasingly challenging to understand how AI models arrive at decisions or predictions. This lack of transparency poses serious problems for companies using AI technologies; it can erode trust with users while making it hard to hold companies accountable; it may even result in biases and discriminatory practices being perpetuated through training data used for AI systems.
One way of improving AI transparency is through model explainability, which allows people to see how algorithms make decisions or predictions. This involves showing both its underlying logic as well as providing further details regarding its conclusions.
Implementing AI transparency can be challenging due to information asymmetries and technical challenges. With complex AI algorithms and proprietary data being shared among companies, companies may find it hard to implement measures without jeopardizing intellectual property rights or their competitive edge.
Another challenge associated with AI models is education of employees and end-users on how an AI system operates and what type of information it will collect; this can be difficult and time consuming. Furthermore, training sessions designed to increase AI literacy may take many resources to complete successfully.
Transparency for AI differs based on industry and use case, but all stakeholders should have access to relevant data. Transparency helps eliminate biases, increase accountability, and ensure AI aligns with society values and goals.
Transparency in AI Decisions
As AI is used to make decisions, it is vital for stakeholders to understand how and why these decisions were made (de Laat 2018 for more on this). Transparency allows regulators and internal risk managers to effectively fulfill their roles while mitigating risks associated with unfavorable AI-driven decisions.
Demanding transparency can be challenging to implement and has proven ineffective in producing desired results, especially regarding perceived legitimacy. Demands for transparency could even compromise it by discouraging people from working together or using AI in future (see this study).
Requiring transparency about an algorithm might mean publishing its programming codes publicly; however, this would be nearly impossible for an average person to interpret these codes. Furthermore, mandating transparency through such means may result in increased complaints against algorithms which could have serious repercussions for perceived legitimacy (see this study).
To investigate this issue, we conducted an experimental vignette that provided participants with a realistic work scenario involving human-AI collaboration. This consisted of task assignment in a company where AI was the main decision maker for tasks assigned. To increase transparency around AI decision-making results, rationale, and process, and assess their effect on employee perceptions of its effectiveness and discomfort with it.
