Building Accountability Into AI Services
Artificial Intelligence
Manju February 23, 2022

Artificial intelligence is transforming and affecting human lives and businesses in profound ways. Nowadays, automated machines are making crucial judgments in every sphere of life. For example, when it comes to hiring, some organizations solely rely on AI systems and chatbots recommendations whereas in the legal arena, judges are increasingly turning to AI algorithms to make judgements. Many of these AI decisions are difficult for humans to comprehend, and AI-based results are not always fair. Hence, it is critical to ensure that AI systems are developed and deployed in an ethical, safe, and responsible manner. Any negligence in implementing accountability into AI systems will cause significant damage to businesses, individuals and the entire society. Let’s see how to build accountability into AI services.

Responsible AI – An Overview

Artificial intelligence not only provides businesses with immense opportunities but enormous responsibilities too. The output generated by AI systems will have a direct impact on people’s lives, raising significant ethical, data governance, trust, and legal concerns. The more decisions a company entrusts to AI, the more substantial the risks it bears, including reputational, employment, data privacy, and safety concerns – Here comes the need for Responsible AI. It is the method of designing, developing, and deploying AI with the goal of empowering employees and organizations while also having a fair influence on consumers and society, allowing businesses to build trust and scale AI confidently. With Responsible AI, businesses can define key objectives and lay out governance strategies that outlines how a specific organization is tackling the ethical and legal issues concerning artificial intelligence. Many businesses are establishing high-level guidelines on how they create and deploy AI technologies. However, principles are only useful if they are followed. Here is a breakdown of the entire AI’s lifecycle:

  • Design: defining the system’s aims and objectives, as well as any fundamental assumptions and basic performance criteria
  • Development: establishing technical requirements, gathering and processing data, developing the model, and testing the system 
  • Deployment: testing, ensuring regulatory compliance, evaluating compatibility with other systems and analyzing user experience 
  • Monitoring: consistently reviewing the system’s outputs and impacts, revising the model, and deciding whether to extend or deactivate the system

To continuously assess progress, eliminate risks, and respond to stakeholder feedback, businesses should implement appropriate AI life-cycle activities that include planning, design, development, and testing.

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Accountability In Artificial Intelligence – The Four Dimensions

Accountability lays down the groundwork for responsibility across the AI life cycle, from design to deployment and monitoring. It also evaluates AI systems on the four dimensions: governance, data, performance, and monitoring. 

1. Examine the governance system

When it comes to a healthy AI framework, governance processes matter a lot. Appropriate AI governance can assist in risk management, demonstrating ethical values, and ensuring compliance. Accountability for AI entails looking for clear goals and objectives for the AI system, well-defined roles, and lines of authority, a diversified workforce capable of managing AI systems, various stakeholder groups, and risk-management methods at the organizational level. Additionally, system-level governance components such as written technical requirements for the specific AI system, compliance, and stakeholder access to system design and operation information are essential.

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2. Analyze the data

In this digital era, Data is the core of AI and machine-learning systems. This same data that provides strength to AI systems can also be a weakness. It’s critical to document how data is utilized at two stages of the AI system: when it’s being used to develop the underlying model and when it’s in use. Documenting the sources and origins of data used to construct AI models is an important part of good AI supervision. Technical difficulties like variable selection and the usage of manipulated data must also be addressed. The data’s trustworthiness and representativeness must be assessed, as well as the possibility of bias, inequality, or other societal issues.

3. Set performance goals and metrics

After developing and deploying an AI system, it is critical not to ignore the importance of questions like ‘why you developed this system’ and ‘how it’s functioning’? To answer these crucial concerns, businesses need detailed documentation of an AI system’s declared objective, as well as definitions of performance indicators and methodologies for evaluating the performance. Management and those responsible for reviewing these systems must be able to ensure that an AI application achieves its objectives. It is critical that these performance evaluations not only focus on the overall system, but also on the various components that support and interact with it.

4. Review monitoring strategies

Artificial intelligence should not be considered as a one stop solution. It is true that many of AI’s advantages arise from its capacity to automate particular jobs at scales and speeds far beyond human capability. At the same time, people must constantly monitor their own performance. This includes determining an acceptable range of model drift and ongoing monitoring to ensure that the system delivers the intended results. Long-term monitoring must also involve assessments of whether the operating environment has changed and whether the system can be scaled up or expanded to new operational settings. Other crucial things to consider include whether the AI system is still required to achieve the desired outcomes, as well as what KPIs are required.

Also read: Should Software Development Companies Prefer Agile Testing? | Blogs (


The entire framework lays out precise questions and audit methods for each of the four dimensions mentioned above (governance, data, performance, and monitoring). This approach can be used right away by executives, risk managers, and audit experts — in fact, anybody working to ensure accountability for an organization’s AI systems. To get the most out of AI, you need to have trust in it. Many businesses, however, struggle to overcome the inherent risks that comes with it. By developing and implementing solutions across four Responsible AI pillars, ThinkPalm assists firms in creating trustworthy, fair, transparent and accountable AI systems. Speak to our experts, to understand how responsible AI can help your organization be it financial, e-commerce, healthcare or telecommunications.


Author Bio

Manju is an enthusiastic content writer working at ThinkPalm. She has a keen interest in writing about the latest advancements in technology. Apart from writing, she is a classical dancer, embraces fashion attires, and loves spending time with her pets.