There is no denying the fact that artificial intelligence is the future. From the security forces to the military applications, AI has encompassed our daily lives as well. However, the AIs comes with its own limitations. The machines may be made by humans but the processes which they follow and the speed with which they analyse the huge volume of information is beyond human perception. This is where explainable AI (XAI) comes into the picture. It ensures that the humans can understand the reasoning and the logic behind every decision these machines take and use the knowledge to develop better machines.

How does Explainable Artificial Intelligence work?

Explainable AI (XAI) is artificial intelligence that is programmed to describe its purpose, justification and decision-making process in a way that can be understood by the average person. XAI is often conversed in relation to deep learning and its important role in the FAT ML model (fairness, accountability and transparency in machine learning).

XAI program will incorporate new explanation techniques based on the result produced by the machines to create more explainable models and outputs. Optimization techniques, architectural layers, design data and many other processes are used to experiment and develop interpretable models of the AI machines. Model induction would also take place to treat the machine processes like a black box and experiment with it to develop a better understanding of its processes.

GAVS Technologies collaborates with AI-based companies to create better understanding and learning techniques to provide a greater support for the machine-human relationship. The machine learning and AI capabilities of GAVS’ will support these companies and in their various test and research activities of XAI prototypes to create more explainable models. These prototypes would be available commercially as well create a more robust XAI solutions once they’re approved.

Challenges and opportunities for XAI:

Explainability is a scientifically interesting and socially important topic that is the crux of several areas of active research in machine learning and AI. The challenges that XAI face include:

  • Bias: How can I ensure that my AI system hasn’t learned a biased view of the world (or perhaps an unbiased view of a biased world) based on shortcomings of the training data, model, or objective function? What if its human creators harbor a conscious or unconscious bias?
  • Fairness: If decisions are made based on an AI system, how to verify that they were made fairly? Fairness is contextual and has different perspectives depending on the particular data input given to the machine learning algorithms.
  • Transparency: On what basis can individuals have the right to have the AI decisions explained in layman’s terms. Where and how can they be appealed? XAI tries to answer the transparency issues in intelligent systems.
  • Safety: Can customers gain confidence in the reliability of the AI system without an explanation of how it reaches conclusions? This is closely related to the fundamental problem of generalization in statistical learning theory i.e. how tightly can we bind errors on the unseen data?
  • Causality: Can the learned model provide not only correct inferences but also some explanation for the underlying phenomena? Can users gain a mechanistic understanding of a learned model?
  • Engineering: How to debug incorrect output from a trained model?

All these points present opportunities for businesses to leverage.

  • Collaboration for future innovations

The recently founded Partnership on AI was formed to bring together researchers, developers, and users to ensure that AI technologies work to serve people and society. The partnership’s mission includes addressing challenges and concerns around “the safety and trustworthiness of AI technologies, [and] the fairness and transparency of systems.” It is a platform that unites organization from different levels to collaborate on addressing concerns, rough edges, and rising challenges around AI, as well as to work together to pursue the opportunities and possibilities of the long-term dream of mastering the computational science of intelligence.

  • Issues not specific to deep learning or machines

Deep neural networks have achieved exceptional improvements on a number of challenging tasks, in part due to their enormous expressive power. This power, enabled by large number of free parameters and non-linearities, can make it difficult to interpret the learned values of any given parameter, especially in the deeper layers. XAI present an interesting opportunity to overcome these similar challenges.

  • Learn how to deal with AI systems that outperform humans in specific tasks

AI systems that outperform humans in specific domains already exist and will become more common. One consequence of an AI system’s astounding performance may be that there is no explanation for how it works that is easily acceptable by a human. Studies and research in developing a similar framework for AI systems in critical deployments are necessary to benefit from deploying some tools even before they are completely understood.

  • Make decision-making more systematic and accountable

XAI vastly improves the quality of decision makings and hold the respective stakeholders accountable. For an engineer, this translates to system requirements that can be designed, measured, and continuously tested. They will depend on the domain where they are applied. As we rely more on automated systems for making decisions, it gives us an unprecedented opportunity to be more explicit and systematic about the principles or values that guide on how we decide.