Unlocking Strong Governance With AI TRiSM
The world is paddling towards the usage of Artificial Intelligence at an alarming pace 🏍️🏍️. It is difficult to catch the rhythm. There is a Bollywood dialogue in the famous movie “Don” and if we convert it into English then we can say “the pace of AI & the security impacts it create is elusive”. It is difficult to catch but it is not impossible!
Here comes strong Governance into the picture & our main topic of discussion- AI TRiSM. Let’s understand what it means-
Imagine we have a crystal-clear prism now if we put inside it a large amount of data then the prism works as a tool for protection and the three dimensions are Trust, Risk & Security Management which protects data. In simpler terms, we can name it as AI TRiSM.
As AI continues to advance and infiltrate every aspect of society, questions about data breaches, trust issues, risk, and security arise. How can we ensure that AI systems are reliable and safe? How can we protect our data and privacy in this digital age? This blog explores the challenges and opportunities of AI trust, risk, and security management, and how we can unlock its potential to create a brighter future for all.
Embedding Transparency in AI Systems
Transparency is a crucial aspect of building trust in artificial intelligence (AI) systems. As AI technology becomes more widespread and influential in our daily lives, it is essential for users to understand how these systems work and make decisions. Transparency not only helps users trust AI but also allows them to hold developers and operators accountable for the actions and outcomes of AI systems.
One of the formidable issues regarding AI is the “Black box” problem. Conventional AI algorithms are often complex and difficult to interpret, making it challenging to understand how and why certain decisions are made. This lack of transparency can lead to mistrust and skepticism among users. When AI systems are used in critical areas such as healthcare, finance, fraud detection or law enforcement, it is vital for users & public at large to have confidence in the decision-making process.
Transparency in AI can be achieved through various means. To begin with, developers should strive to make AI algorithms and models more explainable. This involves designing AI-enabled applications & systems in a way that makes it possible to trace back decisions to specific input data and rules. By providing insights into the decision-making process, users can better understand and trust the output of AI systems.
In addition to it, transparency can be fostered by providing clear documentation and explanations of AI systems. This includes disclosing the data sources used, any biases or limitations present in the system, and the algorithms and methods employed. Users should have access to information about how the AI system was trained, validated, and tested, as well as any ongoing monitoring and updates.
Furthermore, organizations should establish standards and regulations regarding transparency in AI. Governments and regulatory bodies can play a crucial role in ensuring that AI developers and operators adhere to ethical and transparent practices. By implementing guidelines and requirements for transparency, society can benefit from the responsible and accountable use of AI.
Building trust in AI through transparency not only benefits users but also AI developers and operators. Transparent AI systems are more likely to be accepted and adopted, leading to increased user satisfaction and usage. Moreover, transparency can help identify and mitigate biases and errors in AI systems, improving fairness and accuracy.
How AI TRiSM Can be Expedited Properly?
For expediting the potential of AI TRiSM the developers & professional experts must have a proper plan of action in place. Attention all the AI practitioners & Technocrats!!
It’s time to get all the ducks in a row for proper implementation of AI TRiSM strategy in order to avoid the invaders like vulnerabilities & security issues 💁🏼♀️
Let’s discuss the process to expedite AI TRiSM Strategy-
➡️ Stakeholder Gathering
In order to implement a rock-solid AI TRiSM strategy the Companies operating in any sector have to conduct Stakeholder meetings. The presence of Directors, CXO-level executives, Agencies, and Government should be there and the employees & engineers working on AI must be informed so the meeting board room, Real-time video conferencing, and Internal communication channels like MS Teams, Slack or any other channel must be used to endure proper communication. The common goal is to achieve the trust of users in AI and must be realized. The cost-benefit analysis, security & savings from the deployed AI models should be taken into account.
➡️ Ethical Training
There are many ethical concerns which penetrate like if the engineers are using Computer vision technology then sometimes the model is trained on biased datasets which can give rise to issues like inequality, misinterpretation or social issues as well. While working on deep learning algorithms, Predictive models and other ML tasks proper training with respect to ethics & governance must be given from the base level in order to avoid vulnerabilities and risks.
➡️ Defining KPIs
There are several Key performance indicators like measuring model accuracy, reliability & precision score apart from the security deployment & execution of the model into production, cost-saving generated from the model etc.
➡️ Setting up of Ad-Hoc Teams
The success factor in AI TRiSM implementation comes as a result of collaboration to co-create & share the success of the project. The Companies have to set up Ad -hoc teams frequently in which people from technology departments like Developers, Data Scientists, DevOps engineers, Legal, Security & IT team, Project manager & Business teams should come forward for the strategy.
➡️ Model Governance & Security
The traditional MLOps process has been implemented to prepare the model, evaluate it and then deploy it into production based on the Business needs. However, to ensure smooth AI TRiSM, the AI experts must add an extra layer of ModelOps on top of the MLOps process which helps in monitoring, management, Governance & security thereby helping in model lifecycle automation, and achieving goals of risk management & compliance.
Wrapping it all!
The iterative process, the focus on improving the performance of the model, data sanitation & cleaning, and anomaly detection on timely bases can help to optimize the model while keeping the data secure & free from biases. It is not confined to just making accurate & precise predictions >90% of the time but also includes responding fairly in dynamic large data sets, detecting threats & control them. Numerous Product Companies & Invest Bank leaders spoke about Responsible AI. The main goal is to use AI for decision alert & Human intervention through AITRiSM.
By the time the period of 2028 will start, the Machines driven by AI will take up many tasks. Gartner predicted 20% of the workforce will be AI & 40% of all economic productivity will be powered by AI. So in the risk & vulnerability battle, we have to unlock the potential of AITRiSM efficiently. Responsible AI is similar to an Angel fighting with the Devils like Risks & data breaches to establish goodness in the technological environment!
