Introduction
Business Increasingly relies on AI (Artificial Intelligence) to make important decisions and embraces AI in the business workflow. AI systems should be responsible, transparent, and trustworthy. Businesses need decision-making systems that combine the power of the machine, data, AI, human judgment, and then make explainable and traceable decisions and provides transparent, fair, explainable, secure, and robust AI systems.
Challenges of the AI System
Most commonly used AI systems face certain types of issues and challenges such as bias, opaque, data insecurity, etc. Simultaneously, transparency and privacy are an important concern in healthcare, finance, and law. If a system is opaque and cannot provide transparency and explainability, it threatens trust in the system. If there is an AI system that predicts, after two years, a person will have cancer. In this situation, it is sure and obvious that both the patient and the doctor will ask what parameters and algorithms based on which system predicts that a person will have cancer after two years are. They would also get to know whether the system performed correctly or not. It is not explaining to the user of the black box functioning of the system. As the system cannot clear the user's doubts, it can lose their confidence. This is just one example, but there are numerous issues the system can face. They are given below:
Privacy
Most of the data is stored digitally on a single internet. In the digital world, it is hard to control access to data. Therefore the security of data is always at risk while using AI. But in today's world, data security is at the top.
Manipulation of Behaviour
In AI in surveillance, the use of information or collected to manipulate behavior can reduce autonomous rational choice. It directly attacks the autonomy of individuals.
Opacity
Lack of accountability, auditing, and engagement reduce opportunities for human perception. As well as developers, users are not aware of the process system use to reach the output. This opacity increases the bias in datasets and decision systems.
Bias in Decision Making
Human beings are sometimes biased against other communities or systems. Unconsciously this bias enters into the AI system. This can be through data or the user's intention while interpreting the output of the system. This bias against a particular subpopulation can harm that specific group, which faces challenges in society.
Evil genies
AI can work as an evil genie that will obey the order, but the consequences of the way it uses to obey the order can be terrible. But this is only when there is a lack of understanding of full context when the system trains.
Security
When a system is designed, it is first trained then tested in several cases. Only after it launched in the real world, but it may be possible that it may not cover all the examples that systems deal with in the training phase. It can fool a human being by the wrong prediction for a new case in the real world.
Solutions Provided by XenonStack
Data is sensitive and confidential in healthcare, so model data should be encrypted to give users confidence against system bias, understand and verify the system's output, and continuously monitor the model. Which data is used, the provenance of data and why that data is selected, how the model works, and which factors influence the model, why the particular output is obtained is also very important to ensure data security.
Another is an AI model operationalization. ModelOps focus on the operationalization of all AI and decision models. It focuses on governance and life cycle model management of all AI and decision-making model knowledge based on ML (Machine Learning), knowledge graphs, optimization, rules, linguistics, and agents. Similarly, MLOps focus only on the operationalization of MLOps and AIOps models.
To adopt AI in real life, we have to adopt explainability by design. It is an integral part of the system that helps to understand data patterns by visualization. Understanding of models can be better by visualizing neural net layers. By incorporating behavior morals in the system, it becomes easy to understand user behavior.
XenonStack enables enterprises with an AI platform and complete model management lifecycle model that design, develop, train, test, deploy, and monitor models using explainable decisions and governance. The vision of XenonStack to reduce those issues and provide a completely transparent, secure, robust, trustworthy, and automated system to everyone who is looking for a successful and efficient AI system. XenonStack is working on the following technologies to remove those issues and make their system more reliable.
Explainable AI
XenonStack is explaining with their systems. Users can understand the data, model, and algorithm from this explanation. XenonStack uses visualization to explain their system so that users can easily and quickly understand the system. It helps to understand and track the behavior of the model and the reason behind each prediction. The model's fairness can be improved by identifying and reducing bias in the model. It helps to gain user's trust and confidence.
Ethical AI
XenonStack provides AI systems that are human-centered and values human rights. These are aligned with the ethical principles and values of society. The adoption of Ethical AI allows the organization to track ethical issues in the system and reduce them.
Privacy and Preserving AI
AI systems are using data points to train and make decisions. This data can be so expensive. It can be essential for users. For a normal AI system, this data should be explained to the model, which can breach data privacy and security. But XenonStack comes with Privacy and Preserving AI. It lets the user secure their data and make it private. An encrypted data can be sent for the model. The result that will be generated by the model can also be obtained in an encrypted form.
MLOps
XenonStack enables Enterprises to efficiently streamline Machine learning cycles with solutions for automated deployment and administration of ML models. Automated Monitoring solutions empower enterprises to understand and Proactively identify Performance and Operational Issues. With an Effective MLOps platform, companies can Establish cross-functional governance and achieve capabilities to audit, manage access control in real-time.
ModelOps
ModelOps similar to MLOps, but focus on all AI and decision models rather than limited to MLOps.
"Confront the risks of Artificial Intelligence" - McKinsey and Company
Principles
Technology that XenonStack is using based on some principles. These are given below:
- Trust: The self-explanation capability of Explainable AI increases accountability. It also enhances the trust of customers and stakeholders.
- Transparency & Explainability: Our system explains each prediction and output. It provides transparency for the logic of the model. Users get to know the contribution of data for the output. This disclosure justifies the output and builds trust. Transparency of the model lets the user adapt it easily by avoiding black box model questions about robustness, biasing, and logic.
- Feasible: Explainable AI is feasible as it completes the coming demands without degrading model performance and accuracy.
- Decision making: Tracking bias and gaps in models using interactive dashboards let the user fill those gaps and allow the AI system to make better decisions without any misleading.
- Social Wellbeing: XenonStack provides the available system for the individual, society, and the environment. We are doing continuous research to address all the challenges so that future generations can also take advantage of the AI system. AI shows a huge change in industries also. Healthcare, Gaming, Manufacturing, Banking, etc., almost all industries are getting the benefits of AI.
- Human-centered: Ethical AI system values human diversity, freedom, autonomy, and rights. It serves humans by respecting human values. The system is not performing any unfair and unjustified actions.
- Reasonable: It accomplishes the reason behind each outcome of the AI system.
- Understandable: Explainable AI has the ability to understand the working of the model. It can check and let the user know whether the system is working properly or not.
- Traceable: Explainable AI can track the logic and data. Users get to know the contribution of data in the output. The user can track problems in logic or data and then can solve it.
- ROI: Removal of the loop between plans and operating output increases ROI. Accordingly, changing things on time increases clarity and work values.
- Avoid Unfair Bias: XenonStack uses systems that are based on ethical AI. It is not doing any unfair discrimination against individuals or groups. It provides equitable access and treatment. It detects and reduces unfair biases based on race, gender, nationality, etc.
- Privacy and Security: AI systems should keep data security at the top. Our Ethical AI designed system provides proper data governance and model management system. We are designing a system that can identify vulnerabilities and reduce attacks.
- Reliable and Safe: The AI system should work only for the intended purpose. Our system works appropriately. Safety measures are on priority for us. We always apply strong safety and security practices in our system. Our systems are fully tested and monitored.
- Accountability: Our system provides opportunities for feedback and appeal. Our system is under the control of appropriate human direction.
- Value of alignment: Humans are making decisions by considering universal values. The motive of XenonStack is to provide AI that also considers those universal values.
- Governable: XenonStack designing a system that works on tasks so that they give only intended results. It detects and avoids unintended consequences.
Conclusion
XenonStack comes with a change in the AI system's world by implementing the Responsible AI that gives a different experience to their user and end customers. It works on each principle that needs to be followed to implement an AI system and make it a justified, responsible, human-centered AI system.
